diff --git a/README.md b/README.md
index edcf18ea..4a85f34d 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,7 @@
 # Data-Science-For-Beginners-from-scratch-SENATOROV
 Командный репозиторий.
+ 
+
+https://t.me/RuslanSenatorov 
+      
+       
diff --git a/git/stash.ipynb b/git/stash.ipynb
new file mode 100644
index 00000000..f62a3b38
--- /dev/null
+++ b/git/stash.ipynb
@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"STASH.\"\"\""
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    },
+    "image-3.png": {
+     "image/png": 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"
+    },
+    "image-4.png": {
+     "image/png": 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"
+    },
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CIiACIiACIiACIjBIBKRwG6S7qbKIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAj0nIAUbj1HrgRFQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAQGiYAUboN0N1UWERABERABERABERABERABERABERABERABERABERABERABERCBnhOQwq3nyJWgCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIjAIBGQwm2Q7qbKIgIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0HMCUrj1HLkSFAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERGCQCUw5SYXpRls033zwsscQSSVJHHXVU+Oc//9mLZAcmjfnmmy/svvvuSXluvvnmcNVVVw1M2VQQERABERABERABERABERABERABERABERABERABERABERCBSZOAFG4d3PcPf/jDYZNNNgmTTTZZGD9+fKmybZ555gkf//jHk9jHjh0bnnnmmQ5SGtygf/nLX8KoUaPClFNOmWylcBvce62SDQaBhRdeOMw000xJYW677bbBKJRKkRD4yle+kt7bLJLbb789PP/889nD+i0CA0Ng9tlnD/whjz766MCUSwURgX4loGeuX+9MvHzNOOOM4ROf+ESYdtpp00jVd0xRaGcEEZhrrrkCE4URjeWMoBunrIpAlwioD9MlsD2Mtp/a9W6Nw6ie9rBCVUhqxCjcPjDbXGH6eRcOU80wS5hy+pnDxAlvhHdefiGMf/6Z8OYzj4X33p1YobjNgowZMyZRthHLJZdcUhoZirnlllsuCcPA5bHHHlsaflI6ed1114W11lorUbodcsgh4YgjjpiUiq+yOgKf/vSnE8WrHXryySfDE088YT9btossskjgJWnCx08/DpKuvPLK6SCu5dW2L7/8cnj88ccDiueRIocffniYeuqpk+yS/z/84Q89zzoTGJZddtncdN9+++3wpz/9KTz44IO55yeFgzwXyy+/fFrUp59+Ovz2t79Nfxft7LjjjuGDH/xg7ul33nknXH311bnnBu0gA4RrrLFGS7HeeuutpC0ajvrekpEB+cEzvNpqqyXt/RxzzBHefffd8NJLL4Xnnnsu3HLLLeH+++/veUmPO+64QF6Qk08+Odx00009z8OkniDPHc9fVeEd9Itf/KJq8BEVbtD6Dnnw9czlURmMYxtttFHYdNNNw1RTTTWkQH/9618D/RKJCIwkAhtuuGFYaaWVkizfe++9ge+h4Za6famy76iiMlFmvq+WXnrplvf0I488UjqRnImaH/vYx5Joee4fe+yxoiSS7/qq3y+jR48Oiy++eGFc7U68/vrrud81M888c/KN+dnPfjbpo/7rX/8KDz30ULjzzjuT8ufFm/fdYOGYlM9YBpw6kU7zMeuss4ZVV101SeLf//53uOyyy9omx70c9f7Ed2TcuHEBb1OS6gTUh6nOql9D9lO73q1xGNXT/qp9fa9w++DHPhlmXfKrYZrZ584lN/P7R99549Xwyh9uCy89eEsI772XG67pwQUXXDB86lOfSqJ54403whVXXNE0ykn2+jPPPDMZeOOj7Atf+ELSKXv22WcnWR6zzTZbQJmL/O1vfwunnnrqJMNir732Ch/5yEfS8jIAu+WWW6a//c5hhx2WKn44juJqn3328UH6Yn+nnXYKH/jAB0rz8t777dTDDz8cTjnlFFkRlZL6z8k111wzrL766m1Dvvrqq+Hss8+e5D4gvv71r4ellloq5TNhwoRKCjc+0CQhcRO92WabFaJAqYu1Hx3YXg4aDsq7gYk1vOuz8tGPfjTQt2KGH3WRZ/faa6/NBuvJbxSAkt4T2GGHHZLJV1VTZiBrUBVuk1rfQc9c1Vrf/+FQtm211VaFGZ18ci0ZXwhHJ0oJDEo/qLSQFU826UtV/Y7yWcG7CQq3/fffP/hn+IUXXgjbbrutD9qyTz7NwhWl03777ddy3v/o5PsFhf7nPvc5f3lH+/TlsxMJ6X/uueee6YR6ImQiFso92rX77rsvUJ6soMAs+26w8EwSYtyrnZVvnXx85jOfackDE9e4X2VCWW2iJZOWYyvcvvzlLwcUrvTpf//734e77767LDsj+lwnfZhBascGqSzDXQF7MQ7TST2NwUP1YyjFvu4Bz/K5lcJca+xQqGyz4kw13Qxhti+uET76ftgpppneDkfd+o4FFlqSZgTuuOOONILtttsu3Z8Ud2aYYYZAp4m/IiuekcRl4403DmeccUa4/PLLk44tnduLLrooHH/88eGTn/xkaVGY3YXr1qx8/vOfb1G2Zc+PtN+4paVDygAznXZJHAI8S3vvvXdgfc1JSbD+9DLNNNOEBRZYwB/K3edjEQW3/fERLRlKAAvPueeeO3zve98L3/zmN4cG6NKRQXg3/OhHP8pVtmWR4Wba3HBnz+m3CIjAfwio76Ca0I8E8OpignUI/X++A+xvJHl1sHJo2x8EBqEfFINkP/WlUEhlvzt8GXlPVZVsPFW/X6rGXxbua1/7WvjWt77VomzLhseiDg8IdQWlJQrHQw89tDCKuvlAiecH080asyghBsJN2UYYvEvEEsZ3Lr300uQbfJVVVkkmyR588MHhvPPOa5lYHSu9kRbPILVjg1SW4a5HgzgOo/oxtFZNOfRQ/xyZdYnVOsrMdO9bw8211jfCX6/4Xnhv4jsdXdsusCkKsEy54IIL2gXX+TYEeCmvsMIKSShM+CUjnwBuDTCNNveDvkR0OPk76aSTAgrr008/3Z9u2WdGGQPbXuiMjkRhkOGee+5Jso5FJx8pWMqaGzNmDKKI9IMVI7Gcvcwzrg5/9rOfJUnCb84550yUS1jLmCy22GKJFe31119vhwZ2i9uW6aabbkj5cNtb5SMRq1ITZn9KQvLROMsssyRKNp5V3LYgDCLgAm/eeedNrZLFq5jAvvvum7IjFNaB559/fjLrld9f/OIXk9nKzOzNe28QpptC+zDqfdc69OuGw6VlN8s2EuP+4x//GHArXSYvvvhi2emBOTeofQc9cwNTRVsKYu03g79llm4tF+mHCIhAJQKx+1J8R914441t0y6zysIie+edd24bR1mATr9frrrqqkKvMN4LStHSDa+88kpLdvxker5/UGqihGK5i6233jr5BuAC1qRkMvFPf/rTluv9jyuvvDLgip7vUiYOs/YfE/VMllhiicLv0ib5wDOSpdNuPA231Sb0e2N6CzjmmGNyvfvwDfXd7343bLHFFpb0iN6qDzOib19fZr4b4zCqp/11q/ta4VYH1Qdm/WiYY7n1w/M3X1rn8txrmNFus3XwQy9pTgAXksyCZFCTme0oWar4nm6esmLoBgFcQmywwQZp1HTkcO/HOmt0YhnUZGYV8tWvfjVZu+fiiy9Ow/sdBmGzCjes/0aiMIiLdV9W6MjjQxqZfvrpE8u/orXrstdO6r+pT3lMv/SlL7W4LeEDhg7HoAuKNRPcHZvyrYnbFYtvUtzSdmXrF+0PLmV4VhF+8wHeznXLpMjPl5m23CRv/ZO77ror8McEjM033zygcOmlqM/RS9rt02LdSZtM0T70YIcY1L6DnrnBq7e+r/Haa68NXgFVIhEYZgKx+1J8R/3gBz9oVCq8szTtB3f6/YJ7R/7yhDWCze3lrbfeOsR1ZPYavhFtbI9+/7e//e10XXjrm1544YUBzzsIYxdlCrdzzz03m0TAOw+WbZYOYyXZ79Km+cBjlE3aZQJqmXjX7v/4xz/KgnZ0DsWrLaWBG8nTTjstYKmIxxkMFphwvd566w3Ecjzqw3RUNRR4mAiong4T+IJkB07hRjln+NTnwysP3x4m/OPZgmJ3dth3dMpm+1SJFSughRZaKDB7lcEF1oWpIsy24aWFFQfm4Fz/4IMPJtsq11sYZr/MP//86WwYXIg988wzbX1L2/W25aW96KKLJnnBP/fvfve7gJ/qToQBOFvsFWu3XjcOMZia+0NmdVrnBcUS5WHGEbOdmNWfnUVk18Fr9tlnT7FNMcUUuS4ViacK307uCwv+0iFCqIfUc9aBQvHj19HB+oB7zeDyNddck+bVdnBh4JVtRf7a6Vh/4xvfSDqeKFizCre///3vSf0mX3TkqZcIihRbhN3CWNpF2xj31sfNOkO4vWA23j//+c9kFj4d+rrCLLr1118//TigU16mcCNd3FrwccO94tn/wx/+0FHyMeIoS9Dq9MSJExNGZWG7cY62mYW3rb32bjOy6VleO31us/F0m2k2vbzfSy65ZHqYZ2r77bdPnjE+cPhQ9DOn0oBd3OmkDSIbdi+qtHEWFp/nvbR0YZ0DPmhZu9Vm8uOGpmx2b902yMoImxjvhrr5IP0mwtqc9gFOPIcffnhpdD/5yU9Kz2dPxm6Ts/FX+d1pXSdOu7++70ffjj4Dk49o1+v0M5vyqFOWKox6HaZpm2z3p+m7oVvl7rTvQD6a3lv6YChV6ONSN61v1q0y+njtfjR5XmLE4fPEfhOmlp+mdSzW91y2bFV+WxkIyzNnQpn8OY636xNa+KY8On32Y30DWdljbmPe27rvhhj3pW7/w9KGaYx+kL83dXn4OGK8s318Zfvd7kuVpd3uHH1g+sJ1ZTi/XxjbMOH7m35+VnCHiNIIYWzHj01kw+b9ZmyMNczsu9QmRPqwTfOBAs8UbigcGU8g3TzxbtsZg4slrHeH8G3mXWfutddeybgRCkfaNL6hhkOGqw/TzXasCcc675fYZan7bsiWu05ZsnHY716265ZmP27rMI1dP/qRS5M8DaTCDSAzLLh0mHDLZU3YpNdihWXi1x6zY1W2zJyxFy7hWVSUtcvKFp5lwPSggw5K3KWZwsHS4nqEWTmskcVaUGWCUoSBWJuhnw07ZsyY8PDDD4cDDjgge6rlNy4BaZC8mIn6n//852ThWX+ubJ9OiCncvDu4smuanovJlMbFZjThngG3hywsvMwyy6SzmSy/KJqw2rrpppuSBW6LFtplMNfitGvZltUTzte5L8cee2yqeMXi0D6a6fxRV3bddddwyimnpAsfr7jiiomrGMriB/H9Gnwo6lizIU9Q1qGExO0DA4tYeXm5/fbbE0tHjqGMMnd4NgOOj3EGI4vqSsx7a/k68sgjk06i/fbbffbZJ5lpd9hhh/nDlfcpj83G+9CHPpR7HR+GDFSbYtQC0bnm2WedDAbgyiRGHGXxowTELaa1UcxwZmHr4ZDf/OY3Le0sHwG4FvFS97n1cXSbqU+r3b5ZjhIOdys8LzbLkf0f//jH7aKIcr5OG4SLEftQpD7jNid7vyxzl1xySbDnZNy4cWGHHXawUz3bMthsH8d8eGO5y4QKk6ZtEO+FGO+Gpvmw8jTZ4nazG9KkTaatNPeg2bzxvuH9XEXq1HXiZbF6W2ODwQ76k7xneR+a8H4kHGtf5A0AWTjbNuFBHHXLYun3yzZGmxzj3dALHlX6DuSj6b2lb5d15827nXc8Fr/WR2OyBH22PGnyzMV4XmLE4cvVlGmMOhbre86Xq5N9zzR7HZN88r5hcDeP67OsxOBR99mP9Q2ULVOT3zHvbZN3Q5P70rT/EasflL0PTXhYXNyfpu9si6vqtlt9qarpZ8MxqZZnDuH7j7EDxhDqyHB9v1C/UbibMAE+T26++eaA0sgs1NZdd93EeisvbNExLL5s/I94WF+b8TIkRj6YBIxHIdZNQhiryVO44eLSJgwSzk+s5ncTsQmuEyZMGBIN1pRMvstTNg4JHPnAcPZhutWONUFU9/0SqyxN3w2+7HXL4uOw/eFo1y3tWNsmfW3LQ12mseqH5WMQt5MPYqEo04fmWyRK0XhJ2ouWmRt1ZnYuvfTS6cs2myl8GzNozks3K8xqYgaADWRnz/ObvK2zzjrh1FNPzTudHEM5xwdSkbLN4mFtqSLBugdXP1llmw9PJ6KoLD6c7dvaVvxmwInOQLclFtO8fOLfe9lll03riw9D+brhFjHWfTFlm+WZOnfmmWemyjY7Pu2004ZddtnFfiaDMTYIjguGImWbXYALCwaLECwtveBbHcUi4me9WZ2jg8pM2SKJeW+x6KO+M8ujSGwmGel2Kryc/CDrAw88MCQKBrAYEMgq2ywgzz5uKW3Qy477bYw4fHzZfeo0vtmtjeIeDpeyjbwx8OfFT5bwx/1+p89tt5n6vLXbx1LUBBe9CB93Jij/uy1N2iBcN5rCivp8xBFH5GaX49bO8B4+8MADc8N1+yCzXb3YoIMdi9kGWZx1tv2Qj+wAzDbbbFOnKOk1Mdpkm+CQRup27L3kDg3ZbVLXs5Ex03iPPfZoeQ9YGAYnvvOd79jP3G1THjHLkpvBHh7sVpvc6buhF0Wu0neIcW9RqGWVbVY+2mL6JlWk6TNnaTR9XoinSRwxmFpZ/LbTOhbje86n36t9b+1clmanPGI9+3W/gcrK0um5WPe26bshL9+d3Jd+6H/4MsTi0aT98PnpdD92X6rT9LPhmXDr3X/7MYFs2LLfw/n9wr308qtf/cr/bNn3E4zzxupaAuf88N/6nEYBZRIrHw899JBFmXjRSn+4HZscz6Hx48fXGs900bXs2nI7jDMybmnCAL61/UXr8/JuxSovtvRbHyZ2+TqNL9b7pdN0ffhY74aYZRmudt1zibHftK8dk2mM8gxaHP+bVjtgJZt86mnCFNPNGCa+8Uqjki288MLp9dnFVtMTbXbsIUBZx6KqrLPDDBSb8cKAOgqxIusyZv6jbGBNB7YoJeaaa66w9tprB1OSobxgQOv8888fkhtvCfDmm28mPqgZlCVfXIerGMvLkIv/e4AXl700yQ+zfpidzeAnyhF8WzNgSlmYmc0MsCpCfmxmDJ1iXGX2QpoyzeYRhYMtCEtHBmtBOhcM+DMoy/0yYRa978ChdLX1vLCUO+ussyxoui1aKyjWfWHGMoob8uE7S8xWYuAPSyEGehCvOGTdHQSerMPjhbUPl1tuuXSgHJeYWGOSFjPLsFTjOi8sVkwnnE4bClj+rLOKW05Tvvlrsvsx7i112Oo78XM/77zzzsTtI7MN6SCiSLdnO5sH+z3qfQsYXmII8dHZYOFkrGNMnnvuucTlhP227ZZbbpkqb3nOsF7Cwo+6xECYzQxkhiFWEVm3pcQTIw7LT3bL88qMUWOAwmerrbbKBuvp76yCtOi5sUx18tzaNd1kamlU3fJ8meBmFLnhhhvCGmuskewXWYMmJyP9a9oG8a5Auc3zwbvguOOOa1mLj49E3Kma0E6Z+1471qstfQCeRWuT/HPs81C3DYr5biA/dfPhy1J3n/4O6dMvQJgYRBtBO1ZHYrTJTAixxeXJA4OIfsZvu3w1res+ft77yFNPPRV++ctfhtdffz2wBokpcaljWJPn9Qe4rimPmGUhPzFkscUWC7jVLhImVOTVn260yXXeDUX5rnO8Sd+h6b2lf4PLRBPaPdrop9+35qW/T3/D948sXN626TNncTZ9XoinSRxNmVo5/LZOHYvxPefzUGf/5z//ecuAO9+hDKwiPKPnnHPOkGirfNvV4RHr2a/7DTSkoA0OxLq3Td8N2SLUuS/EUbf/EbsfFItHk/Yjy7ST37H7UqTNPaVPVibcP77Z84TvfdbpQlgmhe9RLK06keH8fjEvIJbfsvE9+mY2edNbxdm17bb+XUpYP3E/Vj747mPCN2J5TX64f/77+LHHHnNnmu8y2Z6xDYRJuCjg6CPYM0Nd4r3hhTFLvu+wzGMs0Ctgfbg6+/3Qh4ndjtXh4K9p8n6JXZa67wYrT5OyWBy2tTpa91vM4hnubdO+dhOmsevHcLPsRvoDq3AD1tQzzBbGN1S4+RcXirK6gqIMpZoJ5usslMoioggdFlwd+XVpeCEyiM7aJtnOAPHhPg0XhvaSxT1jnsLNzMxJB3/bvlPER9CNN97IqUQ5kuxk/jF7yaxsmAVOOfzHE8o7BowYCGVwjYEClBFFJvo+ehQ6pnAzBYI/H3s/FtO8fFF2ZhnlWV8we8dm0LMWxXXXXZdGgVLJFG4M5vpzaaCcnZj3BXePWBzyR6fIBpTJiy1STF2lHpilCYpaqxdYaHklYrbhJ/uYkqOQ4UWLcC3KSS+4v7NOF+vCoaBC4MIMtDKFW6x7u/vuu6flIq9Yj/rZb5YOM934kENJWiQw4q9IUOIdffTRQ07jE9/uAWXHpYU9c7iKYNCRgVhTqDDokFW4xYhjSMb+e4APCNzkmrKtncvTonhiHud+oPg3YeCnimKm6nNLvN1kavnuZOsVPrTBCPWEsvOxQ9mKlLGdpFMUNlYbhNtUs+hB4cBAAHWce+pnz+J2lvfecAqzU+3ZtOfP8mNtQ913dqx3Q9N8WHmabmnf6AsgMKNDz0f22LFjA+8MOul+8KEovVhtcrZPQp/JPraK0rbjseq6xccWt5J+bTsmMp144onpRComQuUp3Jry6EZZfLnq7jMg5AeFsvHQh8oq3LrZJnfybsjmtenvun2HGPd2p512SrPPt4dNrOIg/UEmd+T1c9OL3E6TZ85Fk+zWfV58PHXiiMHU58Hvd1rHmn7P+bTr7qN45c+Eb2NTuKG4qvoNY9f7bSc8Yj77db6BfL5j7Me4t03fDUXl6OS+NO1/xOoHUZbYPOq0H0VMOzkeqy9laTLRyA+w2vHstkjhxncGfTe+QagbjC0VeajIxmm/h/P7xa8LaGMzlq/s1o/9+Wc0Gy7vNxPjfZ+GsTsvsfLBhHw/GZD22I9bkKb/XrFxP5+XJvu4GcVFpU329BPbiPeCCy5IXAqjCGO8gknc1BsTGxey3023/dCHidmONeXB9b7udjoWHKssTd8NxqFJWSwOvx2udt3noel+0752E6ax6kdTBv18/cC6lAT6u+8M9SXc6c1ASWDCh0Rdwe9/VnBPxQsS4cXDbOus4IIvq2zzYVBsmOT5R/YdGsL5F5xdZ9tbb73Vdlu2ts4aB3EBZgP/PhCdCAZDTdZcc03bLd36joxXbpZe1PBkU6ZFyVM/igYhUJIwUBFTYt4XLKdM8AVu4o+bQs3qkHcJ6deJohNvnS06UcTBQKIpeu16SyO7xeILQaljaWQ7qdlr7HeMe2sDB8SJMjnbabW0eMFgnWPlsuNVt7AxhWX2GlOic5w85D1z3o0sSlA6sl5ixOHjs30WH0bRaMo23J0weaBXQrq0a/yNel+5zyxJBl5Yx9IUIeSFDlQV6eS57RbTKvnMhsFy0u4B7xH/rHrXHbbmWPb6GL9jtUFMVMD62wS/+9zfY445Jr2nTEap6sbM4unGlpmYJnmTRGK0QRZ/k20/5AMlqrkJtrLwjNKu4wqMPNKn8AP6Fs5ve9Um+zSz+7HqusXLII9Xttnxyy7739rDNrnFztm2KY/YZbF8Dce2m21yJ++GXpa9rO8Q49767wbft7My8mFft99jcXS6bfK8WFp144jB1PKQ3XZSx/x9IZ6yvnTR91w2/X773QmPmM++7z9V/QaKyS7WvW36bigqUyf3hTj6of9BPmLyqNt+kI+mEqsv1TQf/no//mTWTf582f5wf794RZeNwxXl149TsaxGkbCWOd8sfKdQ/+nL4aXAhAnmnPcSMx9+EoS3HiQ9JvDY9yL1mPWoYwv14bDDDksmY/CdhGUgXmYOOuigRNlz8cUXJxMr8aLEu4t+DK5JDz300Nzxzyb58+1pv/RhmpSn6bWeB3ENZ9+h6bshdlmGs11vel9jXR+baax8DVI8A23h9s4rnZm3591Y/3LFaqCOoDArmsX96KOPpgPlpqQoSwN3foTzbpB4adF45jWg2XSxiNt7773Lkmg5h/s6e0lzgo6HfXz69Nj3A5FVykJ8nqlZunG8l9Ip06K80ZnolcS+L16p6/2L+w9Pf68YaPa+x73rvrXWWivBQL0cM2ZMYEaLCQOMZRZfhMPqgQFYr4xi9mkd6fTeYlHj67tXatVJn0XimfmF4CoLawruHcplnhlmvmENkx1w9u6aLr300tykiZdnzp4bXEt6P+4x4sgmjLLN3MdyDkUgM0iLZOutt04WiC46X3acGZ1Zqz3C0ybTYSsT6nNV5Uwnz21TpjF5rLLKKimCbDvPx5S5fjWldRo40k7sNujcc89N2gY6fjyDuKyxdwwfxLQl/SC+HbRnryxfnbZBZXE1OddJPmLWU5TxTMKg3+DbdCsLk5pY93GFFVYI++yzz5AJRrHbZEu3k23suk7aRQoLP7nEtzeW36Y8YpQlZv2wcrFl8gZ94iLJs1j2jOq+K4vSq/pu6AaPOn2HGPfWz8an3cVVVZ5gaYzSvFdS93nx+asTRwymPg/Z/ap1jOuy7/lOv+e6UU+z5Wn6uxMeMZ/9Ot9A1KdYTJveW7g3fTeU3btO7ktePJ30P/Kur3MsNo867Ues+kH5m/alPEPa9nZKFwaiywQlNRNkcYtIX53+2wknnFB2SXpuuL9ffN+9XTkZyzDx4wN2zLb2zWW/bcv1TLhGsZSVmPlgksUCCyyQJJH1BmRLW3AyuyZgNk9NfjO5nD+EScAstXLUUUel33IcR3nPRGa++boh/dqH6UZZq8YZ4/1SNa1Ow3X6bohdljrteqdl7PfwsZn2e3mHI38Dq3B7+6UX3rdwq6cg8zeCGRomeRZkdq5sa5ZBeWH4qDbLFFxK5gmmv8zS8kq2vHAcw7+0/3Dg2N///vfUlBxf+ygvGLhgZgmdgDLTcr+eF3GxLlcVKZqZnb3Wdza8cicbLvbvpkzz8oOCoFcS+754Zal3kVh0HNNjc0/gZ4eRL+uQ0unyyjbY0OH0LivzeF1yySXJenF+sL0TN3JN7q3vMKN4LHt28/KePXbXXXflunllZi6DJQjP7G677Ra+973vJb/5SPTilZn+OPusi2fPkF/0PUYc2bT4jXtCL6xtVyb4Z/dWwmVhs+f4CMpTuGXD+d982GBpyzpgVaXqcxuDaUwetn4n5cyWgTZ+xx13TBBgUYRLW/sIqsqlXbjYbRDp8ezaem72/HMcxTcWpf0g9ryRF56/PGnSBuXFV/dY3XzErKfkHetT/phsgWKNdpbJB/4e4z6YNSGzCvzYbXIdlt2o66xnlyferbjnY2Gb8ohRltj1w8pGX5Tnv6rEaJPL0sq2q0Vhu8GjTt8hxr2d9/01ak2YkV8krNHSS6n7vPg81okjBlOfh+x+1Tpm1zX5nutGPbV8xdpW5RH72S/61ik6zjcQA3UxmTa5t/Bv+m4ou4dV74uPo27/w8fRZD82jzrtR8z6AYsmfSnPkoljuK9uKqzXyPICCN+1VRVuw/394vvuZevGUi7vOcWPj3CuijBG4ifD+mti5uOKK65Ivc1MP/30LeOBCy+8cJps1vVceiLSjl+bzaLk25wxx//7v/9LLNsYr2CScNn4hl3b6bZf+zCdliN2+Kbvl5j5afpuiFmWOu16TBb9EldMpv1Spn7Kx8Aq3F597O4onP2Ae12FW5kiyb9svf9UMo8CjvU7/Cy+doUyZYcPh19tXFraQCGDOAx28YfFCgNcTzzxRK7lGy/Eboq3ICxq9GKmH4tpNk90JopmSWTDxvgd+774vE+cODHNorfm8LPA6KCagti7W/CDE3ffnf8Mcp+pe2WCItoUSI8//nhZ0PRcjHvruXple5pIpB2UQgyosUYPwiC0Kdy8RZJnnpc0ynVTfPrFl2PEkZde9hjWjHfccUdqxZc9343fMKHjbsKACB0F3GlQ53ybamGKtp08t71iWpRXf5x1zvwEDNbDyg5A8d6xdwpWibEVbv5Z8Xlruo/1Ii5CTbD4wSVtv4i9R8lP1uImRhsUo5z9ko9sWXCPy58JFjLMgJ1qqqmSQ6xnuuiiiyZrRFkYX8+62SZbenlbn4e883WOMcu3jvi81OHhr6+Tfj9d0802uZN3Qy+ZlPUdYtxb63dRJq9syJbRfxtlz3Xjd93nxeelThwxmPo8+P06dazJ95xPux/3O+ER+9mv8w0Um2HTe+vrap13Q1F5OrkvxNEv/Y/YPOq0H0VMmx6v05dqmmbe9XzH8k2P1xYUUwykt/NA0g/fL/55b6dw82N/Ze/EAw44IDBJhe8uvumZ6Mjkc/q3KKH4JstOcI+dD7sX3Csm6tsEJr9kS5M1NvPqAMdQbn/9618PjAH5iWJW5vPPPz9stdVWiXLWn4cnFnBFCsmi9MqO92sfpizPvTjX9P0SI4+x3g0xy9JP7XoMxnXjiMm0bh4G+bqBVLi9+/aE8Oofq63f0+7m+pchM0ZiCx1Zk+zgOi7RvLKNQWZe1sws9flixlOeos3ixXx8o402CqyLs8wyywR8RvsXHvvMNsIdzyabbGKXJVufJ16Me+21V8v5oh9eCVMUhuN+8DK71kvZdXXPxWKaTd9beWXPdeN3t+9LlTzbrC+voPP3s6gO+DpflA4+t20dQCzeqkiMe+vL4p+RKul3GgarPVO4+efc39t2cfo8eq4x4ihKG+UH6+vxEUL6uAndYIMNcoMfffTRqdInN0DJwXHjxuWeHT9+fNh3331zz3V6sJPnNgbTWDxsYWor73e/+13bzd362Y25AWoc9DxivhtYUNsLA2soYvLWMfTherVvyiHSY2KAlxhtkI+v7n7TfMSqp+3yz2AA7SDr2Vofhj7Kgw8+mF7ayzY5TTSz0626nkmm0s+mPGKUpVf1ox0QX5Z2YYvelUXXdfJu6DWPKn2Hum2yt2prNxBZxG6Qjvs6VpdpEY9O6pjF0eR7rtf11PJcddsJD39f2sXf6bPfLj5/PibTJveWPDV9N/hy+f1O7gvXNe1/+LSb7HeLRyd5ilk/ytKt0pcqu77JOb7X99xzzyQKLPraKdz64fvFjzdZ37OIgZ8YXjaB3q8DaRPLLrjggmBrPaOMzCrcYueDiZXmrnOppZZKFG6s421tIPn3XhSKytzpccYYzaKUsQhcg7M+szHZbLPNknHIbLyMGfGM7LLLLkNcJmfDVv2tPkw+qabvl/xYOzsa693QD2XprOT9H1pMu3uPBlLh9vwtPw0T33ozCrl77rknbLjhhklcuH6rI2Uu1exFTLzeFSTHcbFk8v3vfz9cf/319rNl266zYIHxl2w+k3HtRMeIAX9TnKBQZLY5L0kTLN9MGGjkgYwpXtEA625KN5h2M79lcXf7vpSlbeeYScVMLq+I9u4BRo8enesj3qxuLJ68LS4POnF7EOve+vz7cuXlsemxsWPHtkTB2lX4UX7qqafS4+2ebc8SX/omMeKwuPyW9VywxMM/PO5I6MSzLhNuRPDfn5WsS9Hs+ZH0OwbTWDwWW2yxjtDxUWP1q6MLSwJ3ow3ab7/90o9TS5o6xoLx2ckgdr6XWz4o/TPp/Z7HaoOalidGPmLV0yplYfIQVoy25kR28eZetslF+e1GXS9Kq93xpjxilKWX9aOMR4w2uSz+qud6zaOo7xDj3vq4/cz+LAtvUZ89N0i/YzDtBo8633O9rqfdKLfFOcjPfp17C5em7wZj22Qbo//RJH1/bT/w6OUz164v5dnE3Gc9LiZ0mzUX69aVST98v2QndJZN6pt55pnT4nTiQYWLDjrooPDDH/4w+VZmDG2PPfZIXORbhLHzgfWaKdzMtSKerExiWpJZnGxZi57JkdSFH/3oR/5Usm/fb7jkxK0k682xdj154xvvwAMPDDvttNOQ6+ocUB+mnFrd90t5rO3PduPdMFxlaV/akRtCTLtz7ybvTrRxYp04/o2OI3rxdzeG1//yh46vK7qAzpLN7uKlUMdSwL+ss+mYOziO+1knKMRMsBQqUrYtueSSFqyjLbNvmGmw7rrrtij6bD05i8wvYO9n9tv5JlvM3U1wXZh1z2XnYm27zTRGPv1gbll83bwvZen6c7jxQ7xrO+9GctVVV/XBk32O+dliQwLUPBDr3vqPI+p71k1fzezlXoZpvRcUVwhrVXlrNe+m04dn37ctfk2VGHFk0+L3LbfckhxmEIq1+ExQruIebpClW0w7ZUad9IpWLJ/5iMr7s3cXaeD+s5348O3W4YzdBrGot/8wxAWJrZeA8pv31XALgwomPKPeTWesNsjiz26rvhu6nY9svmL8LpuR2ss2uagsset6UTpVjjfl0U9lqVLesjD90iaX5bEb54r6DjHurbck9hPisuXALdikIDGYdptT1e+5buejl/FPKs9+J/e26bshxv3rdv+jaj+IsvQDjxhMO4mjrC/VSTydhsVDksnaa69tu0O23fx+GZJYyQHvRYFgTEDPEybM+8m3TA7rRPCC4b8TsDbzEjsfKJltKRDe37hX9OvlMWm2G0K6eCfJU7YxbmrP7dVXXx0uvvjiZPI+a63buIUfC22av5HQhzEeTcva9PpO3i9FaVUtS7ffDTHKUlTGXh7vZBym2/mKwbRq/eh2WYY7/r5WuD1z+clhwgvPVGL07jtvhXE3XhT+9bv4LxM69iZLL7207VbeMnDpfYn7CxmoNvEz9szqjHM26Gjh/LbdTCIftmjfWxJlB1kpu3/4/do6RfFVPY5bOhPWX+q29JJpJ2XxnYOqSs1u3peqeff3zNdvW18LBRKuJvBjTicb/9277rpr1eg7Chfr3qL09fXd3GR0lJmKgb3CmUv8TGrrMHMcVwx5svjii7coL7MfAjHiyEvXjjELxrvUoy3KWqdY2EHZdptpFU7mapWw1NW99947mR3IDMHsn5/hW2Vyhn/X2ezIojzFbINoH5j5aXLfffclrlC8q8yFFlpoWJW6fDSaFRb5ZO1Cb5Ueqw0yBmzrvBu6kQ+fp072mTVcRT7+8Y+nwXyZOdjLNjnNRGYnZl3PRN3xz6Y8+qksHRc+54J+aJNzstXVQ0V9hxj3lneGuetjkuE3v/nN3LJUeZ/kXjjCDsZg2ssil33P9TIfvUhrUnv2293bpu+GGPesG/0P3yeo+o1MWfqBRwymxBGjLxUrL3nxXHXVVcn6ZZxD0WMTSLNhu/n9kk2r3W/vsWmJJZbIDc761yZMsvMeoOx4u+1pp52WTqJl8Dm7NEvsfPixhPXWWy+xPCSPfC/ef//97bIb/bxfP441aL2YUjzmoHy/9mHqtmOeVzf3271ffNp1ytKNd4PPk9/vpCz+un7Yp89p0m4cxsL1YtsJ0zr1oxdlGM40+lrh9u83Xg5//fnpYdyvfhLG//0vuZz+/ear4V+//1UYe+nx4fUnH8gN0/TgL3/5yzQK1hepI9kXLHHsvvvuqTtHPnAZwDYxv8f8xpVlXmeLF2m7AW7OMyDLum1F4mcd+LXhLLxfYBUrBBQoRYLLLWatlFnl2LUoDEz+3//7f7bbtW0spt3IoA1w0OlYbbXVKiXRrftSKfH3A5lijfB+dtgxxxyTKq3oaB122GFJ3cbHNwM43ZCY9/YXv/hFmkUG2Ysst1AS4KveW5mlF7bZQfm4/PLLp6Gyzx3rpJksuuiiqW90O8aW9sOEgX/PgOMx4rD4i7b7779/eq+pu9z7QZZeMG3Hz7+DvOuMvOtw22HiP3rsWHbrJ33QzmctKbLhY7VBKNasM84CxoccckiSFB081isyYVHuBRZYwH72bMs7lLpt7Rftddbizj9/Td7Z2UJ1+m7oVj6y+ary+9RTTw3nnHNOaZ+BSQ3eXffDDz88JOpetMlDEs0ciFXXM9HW+tmURz+VpRYAd1E/tMkuO13fbdd3iHFv/Yc1Xgnog3jhvV9m/ebDDsJ+DKYxOMT4nouRj36JY5Ce/Vj3tum7oem97Vb/o9N+kJVjuHlYPppuY/Wlmuaj7Porr7yy7HRyrpvfL20TzwRASWiCi2RcHHrhmdx4443TQ08++WS638nOSy+91PJ9vtxyy7VcHjsfv/71r9P4/eQc/32XBujBjk/X8yRpW8e+zLCgThb7tQ9Ttx2rw8BfE+v94uPstCyx3g3dKIsv13Dv++elyjhMjPx2g2mn9SNGOfo5jin7OXNJ3t6dGF7/8/3J3+RTTxumnnmOMNUMs4aJE94I77z8j/DO6y+F96eOdLUYLEaLqTQDggxazjXXXC2WHVUSn3vuucN5550X6PzxYuEl491TepNz4mNtGMIxm4tBvpNOOilxj4B/ZMzbeWF7M/GiPDBgipKMP0y3H3jggcSShpkudHzIgx/s8i9+i/PMM88MK664YmA9BwbVUaDgZgVTU6yc6KgwCIpfblM+2NbiyG75gLcZUMxS9B9O2bCxfsdiGis/Ph5mNNiafViBoXTjfo0fPz4JxsD6tdde6y8J3bgvLQm0+cH9p0GlTuAz3BZKRnm04447Jut6ZesBbiiZJcYz5N0mtkmq7emY9/ass84KuH2w9Uu22WabsMIKKwTcZTJrg2cZhTKKap5Nq8d5mWR2nH1gwIk4canJvhfaBi+nn356osSkzSEsg/s33XRT8uzyTBOvf25xv5eVGHFk48z+5kOCD0EUEgh5Ovjgg8ORRx6ZDToQv3vBtB0oP3nirrvuKg1+zTXXJM+iBdp0003DJZdcYj+HbJm9ucEGGyTvOuoek0Co87g1Rli38eSTT06vi9EGMXhrykDaBNY88IIyjvcUYXjeWGA7+9HmwzfdJw2URDynPK+mCLR4i2a5xmyDLC22nb4bupUPn6dO9ukf0Gdg7QveadQn6hGubrCS8e5ROX7bbbcNiT5Gm7zOOuu0tJkk4l0F0aeibffCQDuz5JEYdd3H3WS/KY9+KksTDlzbD21y0zLkXV+37xDj3p5xxhlJv4W2kPbvqKOOCvRBWSeWvj5rcVSRps9clTR6ESYG0xj5jPE9FyMf/RLHID37se5t03dD03vbrf5Hp/0gK8dw87B8xNjG6EtZPujbXnjhhfazcMvEWtr/KsI6XkxQ9ctMZK/r5vdLNq12v+nf7bDDDml++T7C0g03jwxAM6bl+/+4QqwrtFW8RxDixFuUfUvFzseNN96YTMjl/e3HGvwEzLrlqHMdbcKbb74ZWEucsU84YNlG/9+8annXzXXSyF7Tr32Yuu1Ytnyd/o71fvHpdlqWWO+GbpTFl2u49zsdhyG/Tfva3WDaaf0Ybu7dTr//FW6OwLtvjw8Tnh+b/LnDPdlFuWAuVFj888QTT6ycrinO5phjjsAM/axgncKgdVZ4QZtLO17QDDp6JR3hUXgxcOU7Bdl47DeDSdkBJTvHFlPzrOLPztsguikhsGTjr674AVNvwVA3vqrXxWZaNd124ahPJ5xwQhqMQQ1vyfHCCy8MUbgROPZ9STNQcYe1BVdfffWk/jFQzmA4QkOLkppOq7kOZSCV4zTs1CPqvS9zxSQLg8W8t1mumHXXMe2mg8lfkaD45qMnryN89tlnJ4sIW6cZK0JvSWhxMmsIRXyexIgjL15/DGU51npm+Uo7iYK/F0p0n49e7feCaVFZ6FRRH0yK1va082xRYphCizXSyhRuhOejEisKhPeKd6PI5Aj7SEwCvP8v+6x08m5Asb3ssstaVEnevCsCO7HvvvsGuJvS+vjjjw9jxoyx09G3DGzkCSwPe195lJdHwsdsgyz9Ou+GbuTD8lN3y1oY/GX7MBYfbSH3uUiy9azTNpk6XWaVw0BL1q0Q9d9PhsjmoZO6XlSuusezeemUR/b64SxLXQZ23XC2yZaH2NsmfYem95a+GRa9TIagzeWdM2rUqOTPynnnnXeGpZZayn7mbmM8c7kRD8PBpkxjZ7nJ91zsvAxnfIP47De9t9m62um7oen97Eb/o04/yMox3DwsH7G2TftSlo/shFg77rfe3bc/XrTPGmFF60X34vulKF9Fx/E+Rd2yvmF27MWuY1LrPffcYz873rL8Ass+2LgO38z+Wyp2PpgcY5O4LbNVLBAtbOwt/Ykjjjgi6Usw4Zo/E5Rx2YmWdq7utl/7ME3asbosstc1fb9YfHXKEvvdEKssVqZ+2XY6DhOzrx2LaZ360S/8u5GPVjOLbqQwIHEyS8oschi0bCdvv/12GgTLGAbE7fr0xPs7DNxhxs7LIStcwywNH5eFIS5mhGTXxMLixAsWRaTBYFaRMIjKgrd0SosEn9AoyfC/nFcOu45yMLvGm3PbOdv6QTfyhX/rXkkMpnl5NdPZvHNVjj3++ONh5513TiyYsGrLxldkbt/kvhTVCX/c1z2/b2snYNVmizQzcynrKoEZLZdffnnyh7INefHFFxMLTurKxIkTq+DJDZNlEvPeVuFK+iiLx40bl5u/vIPcV+4vClSsj9Zdd91kraq8sFg0sq7Vq6++mnc6qSMXXHBBOOCAA3LPczBGHP55t3udTRAlxOuvv54e9utxpQcb7DSpJ2XJZp+zsrB2LgZTi6vTrV9DFNeL2fY+Lz7vs7/KwtSXXXZZolSijvq2gLh9XbC0qjwrhM17N+y2224WTfIxWjSDlLbDZodyAcoB7yoljSTyDu0caWMZznqUdGqLlG0kHbMNsqLUeTd0Ix+Wn0629Jtww1P2nFGnsNSkLbR3RF4aVepZWZscow2pkgfynlfXOe7fodn3F+c7kSp5KeNR5XryU1SWTvJaNWzR+6Xd9d1ok8vqbLv8dON8J32HGPeWPjwT/rBK9XWVdw59l6uvvjotZtF9a/rMxXheYsRBQWMwTYH9d6fTOhbrey6bj5i/m9zzTnmQ7ybPfrZ/Yxz8cV9//L59A9k1Tbcx722Vulr2bsiWpdP70o3+R51+kJWjCQ9/z307aHH3ahujL1Xn2bRnoWodoJ9u1xgb+93t75eqebR8sWXSOmMvjEvkCc/5KaeckvudXvTey4uHYygbTJjMtd1229nPRvlII3E7We8nTBYcTuE7lO891quz+gBbjBlYbqQb0g99mGy5mrRj2bg6+R3z/WLp1ilLjHdDjLL0S7tuLLPbTsdh6rTtPs0YTH187NepH9k4Bun3ZKNHj+6uP8YGtHBB1E+y3377BVO24RrSXOh1kkc6HMzwpgPYiQUI7uvev1eJ6TsKvDrm18ysmX/++ZNZL8zm4cXH4KEtWtpJOZidssgiiySWE7w8+SgnnioDwKzPY1ZPfLTTkRwOicF0OPJdlmaT+1IWb9k51vjwA+dYa2GBMpwS+95iRUp9Z0YgnWxmquF2opfCjDjaAGau0XlFmdmpxIij0zQHPbyYtt7h4WiDWnPQH79it0F1S9Uv+aDtoh3lj48d+nf0P+iU15F+aJP7qa435dFPZalTH/w1apM9jZDMJq/bX7eYcAVm7lU5hmcDBioRBi2zk/+SEwP8b7ifl5jfc4N0mwbh2Y99b5u+G5rUj37pf/gyDCcPn4+6+7H7UnXzMYjX4aGFJU8YH7vjjjtyJ8P3otz9ko9elLVXaagP8x/Ssd8vde9fjHdDv5SlLoN+vG5SYIrhT69FCrcOiWOSzbpqKJmYkS3pjAAKCywFcFGD4oL1giQjnwCuAhZffPG0IMymwuUQiqHsTKs0kHZEQAREQAREQAREQARGFIHDDz88WceWTNPHq7rOz4gqpDIrAiIgAiIgAiIwcATUhxm4W6oCiUAlAsOhcBtRa7hVotjlQMcdd1z47Gc/m6SSnS3R5aQHInpM/rEORMrcTg5EYSehQmC1iJUb1m4Ia0atscYayR+/sWjAHQeKatYEYR/3bBIREAEREAEREAEREIH+IbD++usnnjhYIzYr8803X8sEqypriWbj0G8REAEREAEREAER6AYB9WG6QVVxioAI1CEgC7c61HSNCIhALoF55pkn7LvvvmHUqFGJFWNuoPcPonhde+21i07ruAiIgAiIgAiIgAiIwDAQOO+888Icc8yRrM2K0g33r6wTgTuzJZZYIu3f4draXEsOQzaVpAiIgAiIgAiIgAi0EFAfpgWHfoiACPyXgCzcVBVEQARGNAEGX2w9t5VWWilZMxBLUFyJTjvttIl1G1Zur7322ogupzIvAiIgAiIgAiIgAoNMYPrppw9LLbVU8pct5xtvvBG+853vZA/rtwiIgAiIgAiIgAgMOwH1YYb9FigDIjDJE5CF2yRfBQRABERABERABERABERABERABELYYostwsorr5y4B2fNZS9vvfVWuPvuu8Pxxx/vD2tfBERABERABERABIadgPoww34LlAER6EsCw2HhJoVbX1YFZUoEREAEREAEREAEREAEREAEho/ArLPOGuacc87EpeTjjz8+fBlRyiIgAiIgAiIgAiLQAQH1YTqApaAiMOAEpHDL3OCXX345c0Q/RUAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERKCYwHAo3CYvzo7OiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAItCMghVs7QjovAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiUEpHArgaNTIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACItCOgBRu7QjpvAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiUEJDCrQSOTomACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIhAOwJTtgug8yIwEgh88YtfDJtsskmYbbbZwrTTThummGKK8Oyzz4Zdd901evZnnHHG8KMf/Si89957YcKECeGNN94ITz/9dDjzzDPDP+oG7EQAACeKSURBVP7xj+jpKUIREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIH+JiCFW3/fn77I3VxzzRXmm2++JC9jx44NzzzzTF/kyzKx//77h2WXXdZ+ptsPfvCD6X7MHeKdcsr/PDpTTTVV+NCHPhTmnHPOsOSSS4Zddtml7/jELLviqk9g9tlnD/whjz76aP2IdKUIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEDfEZDCre9uSf9laMMNNwwrrbRSkrF77703HH744X2TyVlnnbVF2fbOO++EP/3pT+Ff//pXsm2X0cUXXzwss8wy4ZOf/GR4/vnnA+W74YYbSi8bN25cuOWWW8J0002XKNrmnnvuJPxkk00W9t5777DHHnuUXt+vJ2eeeeaE5Wc/+9kwatSohOFDDz0U7rzzzkos+7Vc/ZKv4447LswxxxxJdk4++eRw00039UvWepaPeeaZJ6y22mpJ/YLFu+++G1566aXw3HPPJc/U/fffn5sXrstTqucG/u9BnmXagqWXXjpglWryyCOPlCrFF1544fCxj30sCY7l6mOPPWaXDtkyGWH55ZdPjxP+t7/9bfrb74wePTrQ3tSV119/PVx99dVDLq/z3MJjjTXWGBIXB8aPHx+eeOKJAKdOpNN80HavuuqqSRL//ve/w2WXXdY2Oe4lbRNCO3zzzTcn+/rXngDW30sssUQa8Lrrrkv3tSMCIiACIiACIiACIiACIiACIiACIiACIhCHgBRucTiWxsJA15gxY5Iwf/vb38Kpp55aGl4nqxNAOWTCoO3XvvY1+1m6ZUCd+/CBD3wgDTfvvPMGXFPutttu4eyzzw5XXXVVei67c8IJJ6SHNttss8AfYgP16ckRsvOVr3wl7LnnngGloQkKEZQEG220UbjvvvvCIYccYqe0bUgARdOkJtSfL3zhC0OK/dGPfjQsuOCCgTrIM8yzd+2117aEW3PNNcPqq6/ecqzdj5lmmilRuGEBO/nk/1uu9IUXXgjbbrtt4eXkE7e0CEqn/fbbrzDs17/+9bDUUkul53ExW6Rw23TTTcPnPve5NGynO2+//fYQhVvd5xYFprVZZfl4+eWXE1e5t912W1mw5N512n585jOfackDylYUpGVCGma5jJWoFG5ltP5zjncSltcLLbRQS/vOpJS77767fQQKIQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiUJnA/0YhK1+igJ0SmGGGGQKDi/x1aqXRaVojJfzGG28czjjjjHD55Zcng8hYblx00UXh+OOPT6zNqpYDyzQTLB6qCIPNp512WouyLXvdDjvskCiassfzfl988cXp4amnnjrdHyk7KCm/9a1vtQzGZvOOZQ5WWRIRqEOANQ/zlG3ZuHDV+vGPfzx7OOpvFMmLLLJIYZxe6VwY6L8nsvFMM800YYEFFmh3WZTzvXhuUVqicDz00EML81w3HyjxvOLZrKiLEmLiiinbCIOVsaSYwCc+8Ymkzf7hD38YsNrspF4Xx6ozIiACIiACIiACIiACIiACIiACIiACIiACZQRk4VZGR+eiE8CF2I477hjyFFMM7vJ30kknBdxdnX766W3T9xZqr776atvwBDj66KPT9N97771wxRVXhF/84heJQpS8TT/99Ek8W221VfjNb36TuJqsFPH7gUbioKa39sGKBuUIg9mf/vSnw9Zbbx3MZSYDuChKf/rTn1bFoXCOwPXXX5+4w6POFblOdMEHZnffffcNuA80we3i+eefH37/+98nh7AqxfILa628dsGusy1uY2+88Ub7Wbgts8raaaedws4771x4bZUTWA7hVjYra621Vq5yGotZ3Nbmibfee/zxx8Nf/vKXIcFeeeWVlmMxn9srr7wyvPXWW4kl4Ic//OFkzU577kkUV4S4AqUOZ6VJPrD4tnS8tXI2DX6vvPLK6WGeIdpsST4Bnqlvf/vb+Sd1VAREQAREQAREQAREQAREQAREQAREQAREoGsEpHDrGlpFnCWA+7UNNtggPcygKUqysWPHBhQ9rM2DFQPy1a9+NVnbyVuPpRcW7EycOLHgzP8OzzfffIG1hkwuvPDCdO0gBsP/8Ic/JMoAU5xtueWWwbuPtOuyW8pi12TP9fNvBsst35SBQVpctSF33XVX8gcjY8Z9kcKt3h2tskZVvZj7+yoG/03y1oC0eoaCffPNNw9//OMfLXjulrbiBz/4Qe65qgexckWB3M6FYVl8KNZM3njjjVT5VuQ2Eres/OUJyixze3nrrbcOcR2ZvSb2c3vuuedmkwif//znE8s2ax9ov7MKt6b5uOOOO8Imm2ySpD3nnHMOyYM/4C0k//GPf/hT2s8QsEkjdpg1Es866yy5BTYg2oqACIiACIiACIiACIiACIiACIiACIhAlwhI4VYBLDPv559//nQmPmsAPfPMM6HMggIrAZPZZ5/ddsMUU0wR/Dk7gXUB6+UUCdZGuE9kvSPcamEB8eCDD+ZaQhTFwfE6ZSmKj/yssMIKATdwKKrKeOAuzCvbitZGYhD7G9/4RqIEYu2wThRuRfn0x7fYYov0JxYjWSUIg7o2wExAvz5TemEXdkzRaFH/85//tN2ubrEqMnniiSdSZZsdY3veeeeFvffeOzlEPlFWUP+9WJ32Fjyd1A8fV539GWecMeDODyEPKHm4d5TJrwdGeRdddNFE0XLNNdeUJhXrmStNpORkbKbDUcc+8pGPtLhuPfzww0tKHMJPfvKT0vMxT2LhhivVurLkkkuml9JObb/99knbgZUuCuqXXnopPR97J9ZzW5av3/3ud8kaX6YwzbPma5oPFHimcEPhiJKPdPPEuxpFcRtTsMDkPYb4NqwoDXs2meTRrq3mnUKbw3ub9x7lK3vX+zQtHVxvmpKR55j3LpaB9BuwGC2y9qOdxnUz72eJCIiACIiACIiACIiACIiACIiACIiACIhA9wlI4VbCGCURg6jZ2eJ2yZgxY8LDDz8cDjjgADuUbDfbbLPAX57gMi3PmgAlnnfNxbUM3B500EHJmkBTTTVVS3Rf/vKXk99YJbH+2dlnn91yPvujblmy8fCbuHbdddd0gJJjuETbc889w8EHH5yrtNluu+0IlggKEAYB8wQlCAOJxMcAKC4NcXEYSxj4NHnsscdsN92ydpsXXFaipOymC0AUU7jR9FKkkPRhmu4zmIuiyuS3v/2t7bZsb7755rDXXnulish11103WQPPAnHfqRMIA+FYrXRaPyyuuttjjz02VYg/++yzAXd/CAoBnmHyc8opp4Rpp502Ob7iiisGXIai3PWKkZjPHPXWu1JMEv7vP9bDu+mmm/yhlv3YTIerjs0777wt5RruHzxXCy64YJINLNyoJ9SXOuIVmLiLZLKAWWmx/+Mf/7hOtG2vifXctk3o/QC4/TSFGxMRWJ/uz3/+c3JpjHygrMLKmXVOEZ7LPIUblsne3ahXoicXNvxH+4FyGLn99tsDv4vE1kbjPO9fb+nor6FN57nzYm4xYcgzXibwtb4CblRZK2///fcPyyyzTNoW2/W0Y9/73vfSNuVXv/pVooize2XhtBUBERABERABERABERABERABERABERABEegugcm7G/3IjR2FFgNiRco2SsYA5Kc+9amuFZIBQKxssso2nyB5WGeddcKpp57qD7fsxywLVgZ77LFHi7LNEkM59Z3vfMd+plsGCj/0oQ8lv3EfV6RsswtwF8eMfgTLwjLxCqOycHbOr/nG+mxejjzyyFzWVZQGDLyamLLHftfZ+nzWub7KNd5ihPAM0haJV0oxEFwkdepHUVx1j2f58/yceeaZqbLN4kX5tssuu9jPZBvrmSMycxHYksB/f1j9zjuXPdYtpr2oY1ll1jbbbJMtXk9/U4+9y8rs/a+aGaxvTf71r38lu7YmHT9QinRLuvHcFuXVrL7sPO48TWLl46GHHrIow0ILLZTu+x1TVHFs/PjxQyxsfdg6+35NQKzsymTDDTdMT48bNy7dt51FFlkk/OxnPxuibLPzbFFcXn755bnW7j6c32f9zGWXXXaIso0w3KfPfOYzPniqGG05qB8iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAJdJSALtwK83tLpzTffTNatYkCVQXSUQKzTYzP/s1FgueIVFHPMMUewQTpmqrOWSlbK1hJCmcNMdays2GINN9dcc4W11147VfiRJwazzz///GzUoUlZspFRFuSpp54Kv/zlL8Prr78eWH/IrEYY+GO2vS8j6zIhlIN1mrx885vfDMstt1yqkMPVFhZ7r732WmJ9hQvNMvHKFXO5VRben/Oz/7F8w5INIW3ElIRlCqYk4Pv/GIg2l4YMGmcVDRaun7ZmjWN5wsVmkXCfZ5llluR0mZKzTv0oSrPuce4fFmQ8cyisTSZMmJAohEePHp1aoGYHqS1s02eOeFAsY61pwoC5t9Kx4+22/cC0XR6LzuPSDpbmppXJASiosAirKyhQiadMSJN2JE9og0477bTkFM8qlmrt3AJm46HNMsG1L3LDDTeENdZYI9lv124lgWr+68ZzW5QVv24aYbwr2Vj5gBuKJMTamOSH+2dtM4fyLJNd0Fq7uBZmvU7qKe34wgsvXOiG0a/RR96zcsghh6RuVKmHWAhj9fvvf/874IaUdTAtHSzCsb5tJ9R5c4eMwhHL+ieffDLhxbuX/oBEBERABERABERABERABERABERABERABERg+AlI4VZwD8zFFadZ68cPyLJ+ms2I9wOvFhVrwFx33XX2M+AOyxRuDLr5c2mgnB0GFlmbhXWNssoQlEVYaOFiygYrV1111VyFW5Oy5GQrcRvo12JiQPHEE09MlX8oIk3hxuCkKaIeeOCBFkVkViFBWrj0w9UfA5WIXZv8yPwjbj/o226dGq98ISpvnQBHE6z0WNvJFG5FbgEtPFuUfaZcQcHI2kT9Ln5twXYWV2+88UZaHF+f0oNuB7eSVeuHuyzaLm5J77nnnuTv5z//eWqNyXN33333JX/rrbdeUrfsHlvisZ454su66OT5NOWZpVd1O9xMq+YzL9ydd94Zll566eQUCnkmADA5YOzYsYE2gQkKXpGTF4c/htLSTyLw5/x+kcKN9pv0WIsQxQft+xFHHOEvbbvPtSZMPECIlzW1sBwkXtziFq2tZdfW2Xbruc3mhQkdXtHlJygQNlY+mEjCe9Gs6XBPm7W29QpMe/dm89v0N5NIeFcjtA957xMmZphlKO8oLNm8YDFp7yzaVKzkqRcmTNqhvjAhgDoyatSo5NnIthUW3m8JjzXggQce6A8n+1jltWvDh1ykAyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAtEJyKVkDlI/mMppBrqK5NZbby06FeU47hWzyjYfsXfPON100/lTyX7ssjCo55UpliAWAiZeieFdQvo1jRgwNwUVA5cMbqK4M8VmHnOsAxioRxnGelw+H1j9ZQdpLT+29co5O8aWAXfLM4oN8oI1lMnMM89su4VbLGZMSUh4BmIZxN9xxx2TPGfvQ2FEPTzhB8wZ8C4Tr3CzddDywndaP/LiaHrMD5SzPpSJP24WqHn1rOkzZ+nF2vYD0yZlQYHN8+kF5Qptw/rrrx/gfdFFFwWzhPXhurXv280llliio2Rw0WvuQnlufL3C6siE9QO7Id14bo8//vhwzDHHJGuXcT9oz7FUNqE95LyXmPl4+umn06izk1iYWGG8eRZuu+22NGzMHb8unF/r06dBfTXx99qOoVQ3oU57ZZsdR3HJOnEma665pu2WbrHczVO2cRHr3jGZQCICIiACIiACIiACIiACIiACIiACIiACIjC8BGThlsM/a22B9dPee++dE7L3h3CBh6LKu6ZD0YPiIE95ELssphDLltxbP5gFAGH8Oj/ebeZaa62VREHex4wZ0+ImDEWad9tlaaG8yloI/f3vf08UdZdccokFK9x6yyxTjuGKCxdfCNYppsRj36RMwWRhsIxCGbj99tsHrA2wclh88cWTP8LgRi97L+zaJ554Ilx66aX2M9nmDdS2BIjw44Mf/GAaCwPZZWK8CGOD33nhO60feXE0PeYV1H7NKa988/e3nUvBTp+5pvnPXh+D6XDVMSvLtttum1iloZAwCyA7xxbL1k033TSssMIKYZ999imdZICSq53SpV19Rkn23HPPJRaytJukecIJJ/gsFe6vssoq6bnsM02+zE2pn2yQXhBhpxvPreU5mz2eexQ5hx56aPZUiJkPJq6wrhnyyU9+siUtFJwm3XTVi3tIXByjDOb9ijtNLGW94GrSJOsWlXeJbxuZpGAKOP9uZh831SY28cR+F20vvvjiolM6LgIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0CcEpHAruBEocsyN1ac+9amAmzrcBv7xj39MBiC75dYqLztYYOFmyyvZ8sJxjPW1vMKBYzHLguIoT1588cX0sB9cNIbeggrXjjYwycz87Jo8DO56V4BpxDk7xL/88ssnM/y9Qi8naMjLI+vtWH5tXSeu9UoBW9MtL05/jPWbsJbxCkc77xVWdsxvsYbotbBenskUU0xhu7lbc/fGSdYhLJJO60dRPE2O+8Fsn9ei4yhis0qtps9ck/xnr43FdDjqmC/L2WefHfhDmY5iDSUPCnR7/gj7kY98JBx55JFh991395e27KMsxYVtUznnnHPCt7/97SQa3PJWVbjxPjDBXaYX3hNMDEB4ZlC+08bFlG48t0X5o93GjWGexMzHFVdcEVDKItNPP33Le8wruaq4XszLa9VjvIssva997WstCjfcorKWGgIXLLK9ZF0Wo7yrImZd3S5stq61C6/zIiACIiACIiACIiACIiACIiACIiACIiACvScghVsBc9wBnnTSSeksfgaFGRzm70tf+lIyIIzVSDct31g7jLXQ8hQ4BdlOFVn+fMyyVFU+Wfq2/pl3SegHJu+++24L2rJFyZC1ZrMBWVyZMUC+9dZbJ4PaH/vYx8Jxxx2XrLvTEknmB4pHL7iwwyoBQZHKmngm3qrN3A/aubwtrsZsnT7O//Wvf02UC/3s5ssrmdop3Ly7Uq+4yrLotH5kr4/x25dr4sSJaZTeqs1bQPmyx3rm0kQj7PQD0wjFSKNgHSv+THh2tthii1SZwTpauPR78MEHLUhXtnfddVdieTrLLLMk7QhKVtwplsmCCy7YMvGBNeg+/OEPt1yCJaVZ02I9G1vh5uu3r7stmfjvj6rP7QEHHJC40SXfKENRFKIIQsGE5S5lyk4yiZ0P2nzuBcIEE1sfzY5xvOr6p4StI6RpCjf/niKuddZZJ43ykUceSfdt5xOf+ITtRt8yYcPzjp6AIhQBERABERABERABERABERABERABERABEYhCQAq3Aoy4rtpoo43CdtttF5ZZZpmAksdbYrCPpQOuADfZZJOCWJodPvbYY1uUbSiFGPREmeMH37AaMYuxvBSHsyxmGeUVH94VmVfE+byXWYRhaXjllVcmA/JmlYb13wYbbBAuv/xyH03LftZNIy7sEKwVUEp68UrO7PpTPpzt+8FYwle1brDrh2Pry1VWf8ibV0B614zDke9uphnrmetmHgctbpQcKLvPO++8tB2jze22wg2OrCu55557JkhXXnnltgo3rFi9fPe73/U/h+yb8mbIiQYHuvHc+nXoTBl6wQUXBNytIigjswq32PlAMWnuOpdaaqlE4cY6ePbepd3xVsoNEBZeSh5QzNP+8+7CnaVZsnnLxrz3jFfiMylhr732KkzHnyh6B/ow3kLcH9e+CIiACIiACIiACIiACIiACIiACIiACIhAfxGYvL+y03+5OffccxNXV6w5hqvD22+/PVHQWE5xf4V1RmxhoBP3aibf//73E2u666+/PjC7/vnnn0//2ilLLI7hKIu5w4OTiXf9OHr0aDvcsjULkZaDmR8o0FA+mvj14uxYdpunyLvwwguHuOH0liEo+NqJzy/xjQQZN25cSzaxLCqSmWeeOT3lXcmlBwdgpxvP3ABg6UkRmEDg14GcZ555epLur371q2AWhFhzYTVbJosttljZ6SHnmFwQuyy9em4POuigYO0lbPbYY4+W8sXOh7dem3feeZO0sCY3KXJtaedjbb1V8pprrplEi8WdTR6ZMGFCuP/++4ckh8W7CbyY6FLlr4oFtcWrrQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQH8TkIVbB/fHu0L7yU9+kqwzw+WLLLJI5ViqKsdY58iEGfAo2vJkySWXzDvc9liMsrRN5P0AuHEcNWpUixs270Zy1VVXTVwv+rg45i2q/LnsPgOac889d3LY3Fdmw/jfsPTKPwaNzXWZhWOtHn+fvPWHhcluvWu3KuH99SizdthhB38osFYRyt1uStaCCAufM888c0iSM800UwszrxgZEngEH+j2MzecaIarjnVSZhQZJv55smPd2mKlbM/f2muvnSqZsunhOtIr1rE49m5KfXjWpzMFDZM1Tj/9dH+60X6vntu//e1viTtM1qVEsDY79dRT07zHzgcTMcy6DAszXAV7q7IbbrghTbubOz/96U8DFnaIuYk0xRvHilyEPvroo5xOBIWbRAREQAREQAREQAREQAREQAREQAREQAREYNIjIAu3mvcchYgJa92UiXdlWHUgzgZrifedd94pjL6dRUbhhe5EJ2Vxl1Xaffrpp9NwNnjJAQarkWmmmSZx68aaQQxob7XVVmHXXXdNzlX5V7aeWN71t956a8vho48+uuU3P1A8mWDN5ctgx8u23t1nWTg7h7vS5ZZbruUP95i9EBSWJjawbr9tyzpUJli8XHTRRfZzoLa9fOZ6DW4461iZ5aTn4C1UfZvpw3Rj/6qrrkrWLyNuFD20SXnilS64+GP9zgMPPDD3z1vx1p0UkZcHO9ar5xaXvWblxiSErJvE2PnwVmLrrbdeso4cZYZ3nlWZ8Yi5ZUKBWT1SZtwF+zqMgjZPsDr3rh+/9a1v5QXTMREQAREQAREQAREQAREQAREQAREQAREQgQEmIIVbzs3FBRiDqQxSF4m3hqmiYLH1XRjAW2211YqiTY97K6kZZ5yxZcDPAjEg2c5dWTfKYulX2ZpijbBekXXMMcekg5OzzDJLOOywwwIuL1k3z9bsqRJ/p2EuueSSlkt23HHHlt8o/rzi6aabbmo5P2g/UDaYzDnnnGHzzTe3n8mW+rPxxhunx5588sl0f9B2Yj1zg8alaXmwijrnnHMCz1aRsI4a7ZzJww8/bLs92bImZDthXTmTsWPH2m7u1iv2ad9iS6+eW9wd+ueCiQFeYufj17/+dRo9bhxNnnrqKdvtydZPQtlmm21Si+dXXnmldAKGd4vJ+m9ldR4ryOOOOy58+tOf7kmZlIgIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiED3CUzZ/SRGXgq4JmSwjD/WCHvggQcCM++Zvc6g68ILL9wyOOwHHYtKy+x3W5MNCy6UbsQ9fvz45BIGcK+99tr08meeeSaxbMMiDgXUSSedFB577LHAmkO4RGTg07vbSi/M7HSjLJkkSn/iuhJlI4rGVVZZJfzgBz9IwqOkRNl1wgknBL8+GCdxQ4lVxVxzzZVaVxQlYopMzk899dRFwdLjDCAzeGtrBHEvydO9996bpOcHSIn7/PPPT68t2+mmkrAs3abnGCDGnZ6x23TTTROFI+7iULaxZpW3/Lr44oubJtm318d65iggVjFegcQx78qU59dcoXIO4V5UWS/wP6FH1n+UuSjVsRil3cOCjfUdcRuIBZh31cjx2267rbCArK9YZZ1ElP1HHXVUYTz+BC6C119//fQ58Ods30/AuOuuu+xw7vaaa65J2jc7yXOVVfbbuTrbXj63uMM0V7O0BVhunXzyyUm2Y+fjxhtvDLvvvnvyzuOdYeIVmHasm9vLLrss4NoY8e1fOze/cFpxxRUDdZT8U+dxNcl7EEtpnoMFFlggaVftvWfb2OXBBWiZBTxKbm89f+edd4YzzjgjdjYUnwiIgAiIgAiIgAiIgAiIgAiIgAiIgAhMUgSkcGtzuxkUzw6M+0twc1W0posPd+KJJybKJTvGoBt/Ji+88EKLwo3jDHQyKIYw6IdyiD8vDOIxaO0HBf15vx+rLD7OKvusP7f66qsneTzooIOCuXFECbnlllsmip0vfOELSVQMtHMcRSGDllgUlIlXUPhB+7Jrdtttt3DeeeeFOeaYIwmGYok/LyhXDz74YH+ocL/K2nGFF/fBCdzEUT9xp4dk66ZlEeXjPffcYz8HchvrmcM1qvHMA4UVpbekJAzPMPVykIX1APnLtmNWZp67fffd134WbqsoKbyLysKI3AnWCGO9tTxBgeqV6kVravprURyadduXvvSlqAo30unVc8tabrhZtPfV8ssvnyrcupGP5557Lp2cYjyrWCBa2Bhb3kFMCpltttlaokMR1054bxx55JHJ+4uwWLLx12th7VSrf3lpZ11hE14iAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLQjMD/ppA3i2egrsbCCgsMvx5LtoBvvfVWYC2XqkqZxx9/POy8886JpRxWbd4yi7j9THNLC2s2Zpy//fbbdijdYgHGzPnsemdYcHmJURaffl4+fXp5+1iQTZgwITn1xS9+MbHO8+GwLLr88suTPwY6kRdffDFwvJ3CjTAmWBCifKwi2267bXKPbX0if83rr78exowZ0+JKzZ/P7hOXiVks2u8q22xdqHJNzDAobambMM8T6vopp5wSfvazn+WdbqmfdepHbqQdHix6Vv1xX4/9PuUzifHMEdfEiRMtylpbn78YTIezjp111lkBV6RleeA5xGps3XXXTRTuWWh1eNq9L0vXp4N1kl1jx+330ksvbYeS9b2y7Wx60u34Ncc++tGPujNDd6vm0V/Z5Lm19tjHV7aPItoExfB2221nP0OTfKSRuJ2s9SCKy+EQ2gIvTIopaiN9OKzhccOLNVze+8XC8m7Dos+7r7Rz2W2d+tHpNVbXs2nrtwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQHUCk40ePfq96sF7GxL3Y8MtzOqff/75kxn3WKw8++yziaIG9469FNaMe/9eJS7P7r777kTZ1mn6w1kW3HNhWWaCi7Djjz/efjba/vznP2+x8GMwHKUZSk7Wj2onsOUPt55YuVSR73//+2HaaadN3AZOM8006SWPPPJI2G+//dLfI3EHF3+LLrpoUtfvuOOOtkrPkVjGKnmO8cxVSWdSCkMbhlKcP5SKtPFMbuBZlTQj0C/Pbb/koxnNeFfjGnmRRRZJrM1QauFSlf5DFaVtvFwoJhEQAREQAREQAREQAREQAREQAREQARGY9AjgaavXIoVbr4lPwukdccQRYfHFF08JYLnAujFYgmStGtJAFXY222yzwF9WsEjw1mfZ83V/Y0l39tlnD7kcS6ntt99eA6lDyOiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACPSOgBRuGdb9YOGWyZJ+NiSAlRvWbnmCxQvu87ACmHzyyZN91sKqIijBNtlkkzDnnHMmVmfTTz994sbu0EMPrXJ5R2FYs+20005L3AZiSUc9vffee0Ov1xnqKNMKLAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAKTCAEp3DI3Wgq3DJAB+TnPPPOEfffdN4waNSpMNtlkhaViDZq111678LxOiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiECWwHAo3KbMZkK/RaDbBJ555pl0PbeVVlopWSNv9tlnDzPPPHOyLhrWbVi5vfbaa93OiuIXAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgcYEtIZbY4SKQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREoF8IDIeF2+T9UnjlQwREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARGIgEp3EbiXVOeRUAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAE+oaAFG59cyuUEREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgZFIYMp+zvRw+NjsZx7KmwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQP8RkIVb/90T5UgEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERGAEEZDCbQTdLGVVBERABERABERABERABERABERABERABERABERABERABERABESg/whI4dZ/90Q5EgEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERGEEEpHAbQTdLWRUBERABERABERABERABERABERABERABERABERABERABERABEeg/AlK49d89UY5EQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARGEAEp3EbQzVJWRUAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAE+o+AFG79d0+UIxEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgRFEQAq3EXSzlFUREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIH+IyCFW//dE+VIBERABERABERABERABERABERABERABERABERABERABERABERgBBGQwm0E3SxlVQREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREoP8ISOHWf/dEORIBERABERABERABERABERABERABERABERABERABERABERABERhBBKRwG0E3S1kVAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARHoPwJSuPXfPVGOREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERhABKdxG0M1SVkVABERABERABERABERABERABERABERABERABERABERABERABPqPgBRu/XdPlCMREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIERREAKtxF0s5RVERABERABERABERABERABERABERABERABERABERABERABERCB/iMghVv/3RPlSAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREYAQRkMJtBN0sZVUEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERKD/CEjh1n/3RDkSAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREYQQSkcBtBN0tZFQEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER6D8CUrj13z1RjkRABERABERABERABERABERABERABERABERABERABERABERABEYQgf8PXTjuIq3dg4oAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) Что делает команда git stash?\n",
+    "\n",
+    "Сохраняет изменение в стэш\n",
+    "\n",
+    "2) Как просмотреть список всех сохранённых изменений (стэшей)?\n",
+    "\n",
+    "В разделе гита в Vscode, нажимаем на 3 точки возле \"SOURCE CONTROL\" -> Stash \n",
+    "-> View Stash (либо команда git stash list)\n",
+    "\n",
+    "3) Какая команда применяется для использования верхнего стэша?\n",
+    "\n",
+    "git stash apply\n",
+    "\n",
+    "4) Как применить конкретный стэш по его номеру?\n",
+    "\n",
+    "Заходим в Stash -> View stash -> Apply stash и далее выбираем нужный\n",
+    "Командна - git stash apply stash@{номер}\n",
+    "\n",
+    "5) Чем отличается команда git stash apply от git stash pop?\n",
+    "\n",
+    "git stash apply - это командна применяет выбранный стэш,\n",
+    "не удаляя его из списка\n",
+    "\n",
+    "git stash pop - сначала восстановит стэш, а потом удалит\n",
+    "\n",
+    "6) Что делает команда git stash drop?\n",
+    "\n",
+    "Сразу удаляет стэш, без восстановления\n",
+    "\n",
+    "7) Как полностью очистить все сохранённые стэши?\n",
+    "\n",
+    "Во вкладке \"Stash\" нужно нажать на \"Drop All Stahes\"\n",
+    "\n",
+    "8) В каких случаях удобно использовать git stash?\n",
+    "\n",
+    "Когда нужно сделать merge или git pull\n",
+    "\n",
+    "9) Что произойдёт, если выполнить git stash pop, но в проекте есть конфликтующие изменения?\n",
+    "\n",
+    "Git применит изменения там, где это возможно, но в местах где \n",
+    "несовмещаются файлы произойдут конфликты\n",
+    "\n",
+    "10) Можно ли восстановить удалённый стэш после выполнения git stash drop?\n",
+    "\n",
+    "Да\n",
+    "\n",
+    "11) Что делает команда git stash save \"NAME_STASH\"\n",
+    "\n",
+    "Сохраняет изменения  \"Staged\" и \"Unstaged\" в стэш \"NAME_STASH\"\n",
+    "\n",
+    "12) Что делает команда git stash apply \"NUMBER_STASH\"\n",
+    "\n",
+    "Применяет изменения, с номером в \"NUMBER_STASH\", при этом не удаляет его из списка\n",
+    "\n",
+    "13) Что делает команда git stash pop \"NUMBER_STASH\"\n",
+    "\n",
+    "Применяет изменения, с номером в \"NUMBER_STASH\", при этом удаляет его из списка \n",
+    "\n",
+    "14) Сохраните текущие изменения в стэш под названием \"SENATOROV ver1\", вставьте скриншот из терминала\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "15) Внесите любые изменения в ваш репозиторий и сохраните второй стэш под именем \"SENATOROV ver2\"\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "\n",
+    "16) Восстановите ваш стэш \"SENATOROV ver1\", вставьте скриншот из терминала\n",
+    "![image-3.png](attachment:image-3.png)\n",
+    "\n",
+    "17) Удалите все стеши из истории, вставьте скриншот из терминала\n",
+    "![image-4.png](attachment:image-4.png)\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/git/stash.py b/git/stash.py
new file mode 100644
index 00000000..05ca48f8
--- /dev/null
+++ b/git/stash.py
@@ -0,0 +1,75 @@
+"""STASH."""
+
+# 1) Что делает команда git stash?
+#
+# Сохраняет изменение в стэш
+#
+# 2) Как просмотреть список всех сохранённых изменений (стэшей)?
+#
+# В разделе гита в Vscode, нажимаем на 3 точки возле "SOURCE CONTROL" -> Stash
+# -> View Stash (либо команда git stash list)
+#
+# 3) Какая команда применяется для использования верхнего стэша?
+#
+# git stash apply
+#
+# 4) Как применить конкретный стэш по его номеру?
+#
+# Заходим в Stash -> View stash -> Apply stash и далее выбираем нужный
+# Командна - git stash apply stash@{номер}
+#
+# 5) Чем отличается команда git stash apply от git stash pop?
+#
+# git stash apply - это командна применяет выбранный стэш,
+# не удаляя его из списка
+#
+# git stash pop - сначала восстановит стэш, а потом удалит
+#
+# 6) Что делает команда git stash drop?
+#
+# Сразу удаляет стэш, без восстановления
+#
+# 7) Как полностью очистить все сохранённые стэши?
+#
+# Во вкладке "Stash" нужно нажать на "Drop All Stahes"
+#
+# 8) В каких случаях удобно использовать git stash?
+#
+# Когда нужно сделать merge или git pull
+#
+# 9) Что произойдёт, если выполнить git stash pop, но в проекте есть конфликтующие изменения?
+#
+# Git применит изменения там, где это возможно, но в местах где
+# несовмещаются файлы произойдут конфликты
+#
+# 10) Можно ли восстановить удалённый стэш после выполнения git stash drop?
+#
+# Да
+#
+# 11) Что делает команда git stash save "NAME_STASH"
+#
+# Сохраняет изменения  "Staged" и "Unstaged" в стэш "NAME_STASH"
+#
+# 12) Что делает команда git stash apply "NUMBER_STASH"
+#
+# Применяет изменения, с номером в "NUMBER_STASH", при этом не удаляет его из списка
+#
+# 13) Что делает команда git stash pop "NUMBER_STASH"
+#
+# Применяет изменения, с номером в "NUMBER_STASH", при этом удаляет его из списка
+#
+# 14) Сохраните текущие изменения в стэш под названием "SENATOROV ver1", вставьте скриншот из терминала
+# ![Снимок экрана 2025-10-06 в 14.29.11.png](<attachment:Снимок экрана 2025-10-06 в 14.29.11.png>)
+#
+# 15) Внесите любые изменения в ваш репозиторий и сохраните второй стэш под именем "SENATOROV ver2"
+# ![Снимок экрана 2025-10-06 в 14.31.52.png](<attachment:Снимок экрана 2025-10-06 в 14.31.52.png>)
+#
+# 16) Восстановите ваш стэш "SENATOROV ver1", вставьте скриншот из терминала
+# ![Снимок экрана 2025-10-06 в 14.36.29.png](<attachment:Снимок экрана 2025-10-06 в 14.36.29.png>)
+#
+# 17) Удалите все стеши из истории, вставьте скриншот из терминала
+# ![image.png](attachment:image.png)
+#
+#
+
+#
diff --git a/github/opensource.ipynb b/github/opensource.ipynb
new file mode 100644
index 00000000..9f252415
--- /dev/null
+++ b/github/opensource.ipynb
@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Opensource.\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) Есть ли у него лицензия? Обычно в корне репозитория находится файл LICENSE.\n",
+    "\n",
+    "Да\n",
+    "\n",
+    "2) Напишите название понравившейся компании и ссылку на репозиторий\n",
+    "\n",
+    "Microsoft, https://github.com/microsoft/vscode\n",
+    "\n",
+    "3) Проект активно принимает стороннюю помощь?\n",
+    "\n",
+    "Да\n",
+    "\n",
+    "4) Напишите второе улучшение которое вы сделали\n",
+    "\n",
+    "Попросил написать файл LICENSE.txt читабельнее\n",
+    "\n",
+    "5) Посмотрите на коммиты в основной ветке, напишите общее количество\n",
+    "\n",
+    "138,646\n",
+    "\n",
+    "6) Когда был последний коммит?\n",
+    "\n",
+    "11 часов назад\n",
+    "\n",
+    "7) Сколько контрибьюторов у проекта?\n",
+    "\n",
+    "2,275\n",
+    "\n",
+    "8) Как часто люди коммитят в репозиторий?\n",
+    "\n",
+    "200-300 в месяц\n",
+    "\n",
+    "9) Сколько сейчас открытых ишью?\n",
+    "\n",
+    "5+тыс\n",
+    "\n",
+    "10) Быстро ли мейнтейнеры реагируют на ишью после того, когда они открываются?\n",
+    "\n",
+    "Да\n",
+    "\n",
+    "11) Ведётся ли активное обсуждение ишью?\n",
+    "\n",
+    "Активного прям нет, но комменты присутвуют немного\n",
+    "\n",
+    "12) Есть ли недавно созданные ишью?\n",
+    "\n",
+    "Да\n",
+    "\n",
+    "13) Есть ли закрытые ишью?\n",
+    "\n",
+    "Да - 197 671\n",
+    "\n",
+    "14) Сколько сейчас открытых пул-реквестов?\n",
+    "\n",
+    "795\n",
+    "\n",
+    "15) Быстро ли мейнтейнеры реагируют на пул-реквесты после их открытия?\n",
+    "\n",
+    "За октябрь 2025 где-то 2-3 дня среднее\n",
+    "\n",
+    "16) Ведётся ли активное обсуждение пул-реквестов?\n",
+    "\n",
+    "Да\n",
+    "\n",
+    "17) Есть ли недавно отправленные пул-реквесты?\n",
+    "\n",
+    "Да, 9 часов назад, до меня\n",
+    "\n",
+    "18) Как давно были объединены пул-реквесты? \n",
+    "\n",
+    "Какие-то 1 день назад, самый ближайший 12 часов назад"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/github/opensource.py b/github/opensource.py
new file mode 100644
index 00000000..e692dec9
--- /dev/null
+++ b/github/opensource.py
@@ -0,0 +1,75 @@
+"""Opensource."""
+
+# 1) Есть ли у него лицензия? Обычно в корне репозитория находится файл LICENSE.
+#
+# Да
+#
+# 2) Напишите название понравившейся компании и ссылку на репозиторий
+#
+# Microsoft, https://github.com/microsoft/vscode
+#
+# 3) Проект активно принимает стороннюю помощь?
+#
+# Да
+#
+# 4) Напишите второе улучшение которое вы сделали
+#
+# Попросил написать файл LICENSE.txt читабельнее
+#
+# 5) Посмотрите на коммиты в основной ветке, напишите общее количество
+#
+# 138,646
+#
+# 6) Когда был последний коммит?
+#
+# 11 часов назад
+#
+# 7) Сколько контрибьюторов у проекта?
+#
+# 2,275
+#
+# 8) Как часто люди коммитят в репозиторий?
+#
+# 200-300 в месяц
+#
+# 9) Сколько сейчас открытых ишью?
+#
+# 5+тыс
+#
+# 10) Быстро ли мейнтейнеры реагируют на ишью после того, когда они открываются?
+#
+# Да
+#
+# 11) Ведётся ли активное обсуждение ишью?
+#
+# Активного прям нет, но комменты присутвуют немного
+#
+# 12) Есть ли недавно созданные ишью?
+#
+# Да
+#
+# 13) Есть ли закрытые ишью?
+#
+# Да - 197 671
+#
+# 14) Сколько сейчас открытых пул-реквестов?
+#
+# 795
+#
+# 15) Быстро ли мейнтейнеры реагируют на пул-реквесты после их открытия?
+#
+# За октябрь 2025 где-то 2-3 дня среднее
+#
+# 16) Ведётся ли активное обсуждение пул-реквестов?
+#
+# Да
+#
+# 17) Есть ли недавно отправленные пул-реквесты?
+#
+# Да, 9 часов назад, до меня
+#
+# 18) Как давно были объединены пул-реквесты?
+#
+# Какие-то 1 день назад, самый ближайший 12 часов назад
+
+#
diff --git a/github/quiz.ipynb b/github/quiz.ipynb
new file mode 100644
index 00000000..98155a84
--- /dev/null
+++ b/github/quiz.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Контрибьютинг в Open Source.\"\"\""
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1.1) Что такое GitHub?\n",
+    "\n",
+    "Платформа, где хранятся проекты разных людей, использует систему Git, \n",
+    "что позволяет разработчикам совместно работать над кодом, изменять его, \n",
+    "отправлять ифнормацию об ошибках и т.п.\n",
+    "\n",
+    "1.2) Как GitHub связан с Git?\n",
+    "\n",
+    "Git следит за изменением ваших файлов(если отправлять туда изменения), \n",
+    "и также Git отправляет изменения на Github, где работают разработчики.\n",
+    "\n",
+    "1.3) Чем отличается fork репозитория от его клонирования (clone)?\n",
+    "\n",
+    "Клонирование - это копирование репозитория на наш ПК и \n",
+    "мы можем с ним работать локально, без отправки в изначальный репозиторий изменений. \n",
+    "Fork - это уже создание полноценной копии репозитория, с которой мы работаем не локально, \n",
+    "и следовательно можем отправлять в изначальный репозиторий фиксы/свои дополнения, \n",
+    "предложения по коду и т.д.\n",
+    "\n",
+    "1.4) Зачем нужны и как работают pull requests?\n",
+    "\n",
+    "PR - место, для предложений изменения кода в репозитории. \n",
+    "Нужно, чтобы мы могли провести код-ревью, предложить какие-то свои идеи, \n",
+    "произвести слияние кода.\n",
+    "\n",
+    "1.5) GitHub использует ваш почтовый адрес для привязки ваших Git коммитов к вашей учётной записи?\n",
+    "\n",
+    "Да\n",
+    "\n",
+    "1.6) Какая команда генерирует SSH ключ для Доступа по SSH к репозиторию (Рисунок 83)\n",
+    "\n",
+    "ssh-keygen \n",
+    "\n",
+    "2.1) Сделал \n",
+    "https://github.com/Clownbtw/Data-Science-For-Beginners-from-scratch-SENATOROV\n",
+    "2.2) \n",
+    "https://github.com/Clownbtw/Data-Science-For-Beginners-from-scratch-SENATOROV/tree/dev\n",
+    "2.7) \n",
+    "https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/pulls?q=is%3Apr+is%3Aclosed\n",
+    "2.8) Заходим в историю коммитов репозитория, выбираем нужный нам коммит и нажмаем на Browse files\n",
+    "https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/tree/bb4c7d4d0d7e97aa48dd1c5e3c814223f404439d\n",
+    "2.9) Нужно для работы в разных ветках, чтобы не было конфликтов в коде и т.п. \n",
+    "Создаем новую ветку от предыдущего PR, делаем изменения, нажимаем Compare & Pull request\n",
+    "\n",
+    "3) Напишите 8 пунктов, которые нужно сделать, чтобы внести вклад в чужой проект.\n",
+    "    1.Делаем форк, нужного репозитория\n",
+    "    2.Клонируем форк на наш ПК\n",
+    "    3.Заводим ишьюс, где прописываем, изменения, что меняем\n",
+    "    4.Открывает в IDE репозиторий\n",
+    "    5.Меняем как мы хотим сделать\n",
+    "    6.Коммитим, ссылаясь на наш ишьюс\n",
+    "    7.Отправляем PR, в проект, там описываем, что сделали, ссылаясь на ишьюс\n",
+    "    8.Запрашиваем ревью кода\n",
+    "\n",
+    "3.1) Какие практики принято соблюдать при создании Pull Request чтобы закрыть автоматический issues?\n",
+    "    Нужно указывать closes и #номер_ишьюса\n",
+    "Какие практики принято соблюдать при создании commit чтобы закрыть автоматический issues?\n",
+    "    Нужно указать fix: #номер_ишьюса или также но с closes\n",
+    "\n",
+    "3.2) Как отклонить/закрыть пул реквест? (предоставьте скриншот где это в гитхабе)\n",
+    "    ![image.png](attachment:image.png)\n",
+    "3.3)Перед отправкой пул реквеста нужно ли создавать ишьюс?\n",
+    "    Мы создаем ишьюс в большинстве случаев, когда хотим обсудить тему и т.п, \n",
+    "    его не создают крайне редко\n",
+    "3.4)В какой вкладке можно посмотреть список изменений который был в пул реквесте? \n",
+    "    Нужно нажать на кнопку Files Changed\n",
+    "3.5)В какой вкладке находится страница обсуждений пул реквеста? \n",
+    "    Во вкладке 'Conversation'\n",
+    "\n",
+    "4) Можно ли открыть пул реквест, если вы ничего не вносили в FORK?\n",
+    "\n",
+    "Нет, нельзя\n",
+    "\n",
+    "4.1)  Что нужно сделать чтобы открыть пул реквест? \n",
+    "\n",
+    "Нажать на кнопку \"Compare & pull request\" между двумя ветками\n",
+    "\n",
+    "4.2) Что нужно сделать Если ваш Форк устарел?\n",
+    "\n",
+    "Нужно зайти на свой форк, нажать на \"Sync fork\" -> \"Update\"\n",
+    "\n",
+    "4.3) Что нужно сделать если в пул реквесте имеются конфликты слияния \n",
+    "\n",
+    "Открываем PR -> 'Resolve Conflicts' -> открывается редактор, \n",
+    "вносим изменения(редактируем), нажимаем 'Mark as resolved' -> 'Commit merge'\n",
+    "\n",
+    "5) Что нужно сделать Для добавления отрывка кода в комментарии к ишьюсу? \n",
+    "\n",
+    "Если нужен однострочный код  - `Текст`\n",
+    "Если нужен многострочный код - ```Текст```\n",
+    "\n",
+    "5.1) На какую клавишу нажать клавишу,\n",
+    "чтобы выделенный текст был включён как цитата в ваш комментарий?\n",
+    "\n",
+    "Нужно нажать на клавишу R, или вставить \">\"\n",
+    "\n",
+    "5.2) Как вставить картинку в ишьюс?\n",
+    "\n",
+    "Скопировать и вставить из буфера обмена\n",
+    "\n",
+    "6) Как понять что ваш форк устарел?\n",
+    "\n",
+    "На странице форка появится \"This branch is X commits behind main repositroy\",\n",
+    "может появиться кнопка \"Sync fork\"\n",
+    "\n",
+    "6.1) Как обновить форк?\n",
+    "\n",
+    "В нашем форке будет кнопка \"Sync fork\" -> \"Update branch\"\n",
+    "\n",
+    "7) Как добавить участников в ваш репозиторий, чтобы команда могла работать над одним репозиторием?\n",
+    "\n",
+    "В репозитории зайти в настройки и там будет \"Access\" -> \"Collaborators & teams\",\n",
+    "далее жмем \"Add people\", вводим данные почту/гитхаб и т.п, выбираем роль и приглашаем\n",
+    "\n",
+    "8) Какой символ нужен для упоминания кого-либо?\n",
+    "\n",
+    "@username\n",
+    "\n",
+    "8.1) Где находится Центр уведомлений, напишите ссылку\n",
+    "\n",
+    "https://github.com/notifications\n",
+    "\n",
+    "9) Что такое и зачем нужен файл README\n",
+    "\n",
+    "README - это файл, содержащий информацию о проекте, код, набор данных и т.д.\n",
+    "Нужен для того, чтобы новые люди могли легко разобраться в проекте и\n",
+    "сделать представление о нем.\n",
+    "\n",
+    "9.1) Что такое и зачем нужен файл CONTRIBUTING \n",
+    "\n",
+    "CONTRIBUTING - это файл, содержащий ифнормацию о том, какие люди могут внести\n",
+    "свой вклад в проект(код, докумантация и т.д), нужен чтобы новички\n",
+    "понимали, что от них ожидается\n",
+    "\n",
+    "10) Как измененить основную ветку?\n",
+    "\n",
+    "Открываем настройки репозитория, выбираем \"Branches\", там будет показана\n",
+    "текущая ветка, нажимаем \"Edit\", выбираем новую -> \"Update\"\n",
+    "\n",
+    "10.1) Как передать проект? какая кнопка?\n",
+    "\n",
+    "В репозитории, который мы хотим передать, открываем найстроки,\n",
+    "листаем до поля \"Danger Zone\", находим кнопку \"Transfer ownership\",\n",
+    "и там выполяем то что просят сделать\n",
+    "\n",
+    "10.2) Что такое файл .gitignore?\n",
+    "\n",
+    "Файл, в котором лежат папки/файлы, которые Git должен игнорировать\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/github/quiz.py b/github/quiz.py
new file mode 100644
index 00000000..df6110d8
--- /dev/null
+++ b/github/quiz.py
@@ -0,0 +1,161 @@
+"""Контрибьютинг в Open Source."""
+
+# 1.1) Что такое GitHub?
+#
+# Платформа, где хранятся проекты разных людей, использует систему Git,
+# что позволяет разработчикам совместно работать над кодом, изменять его,
+# отправлять ифнормацию об ошибках и т.п.
+#
+# 1.2) Как GitHub связан с Git?
+#
+# Git следит за изменением ваших файлов(если отправлять туда изменения),
+# и также Git отправляет изменения на Github, где работают разработчики.
+#
+# 1.3) Чем отличается fork репозитория от его клонирования (clone)?
+#
+# Клонирование - это копирование репозитория на наш ПК и
+# мы можем с ним работать локально, без отправки в изначальный репозиторий изменений.
+# Fork - это уже создание полноценной копии репозитория, с которой мы работаем не локально,
+# и следовательно можем отправлять в изначальный репозиторий фиксы/свои дополнения,
+# предложения по коду и т.д.
+#
+# 1.4) Зачем нужны и как работают pull requests?
+#
+# PR - место, для предложений изменения кода в репозитории.
+# Нужно, чтобы мы могли провести код-ревью, предложить какие-то свои идеи,
+# произвести слияние кода.
+#
+# 1.5) GitHub использует ваш почтовый адрес для привязки ваших Git коммитов к вашей учётной записи?
+#
+# Да
+#
+# 1.6) Какая команда генерирует SSH ключ для Доступа по SSH к репозиторию (Рисунок 83)
+#
+# ssh-keygen
+#
+# 2.1) Сделал
+# https://github.com/Clownbtw/Data-Science-For-Beginners-from-scratch-SENATOROV
+# 2.2)
+# https://github.com/Clownbtw/Data-Science-For-Beginners-from-scratch-SENATOROV/tree/dev
+# 2.7)
+# https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/pulls?q=is%3Apr+is%3Aclosed
+# 2.8) Заходим в историю коммитов репозитория, выбираем нужный нам коммит и нажмаем на Browse files
+# https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/tree/bb4c7d4d0d7e97aa48dd1c5e3c814223f404439d
+# 2.9) Нужно для работы в разных ветках, чтобы не было конфликтов в коде и т.п.
+# Создаем новую ветку от предыдущего PR, делаем изменения, нажимаем Compare & Pull request
+#
+# 3) Напишите 8 пунктов, которые нужно сделать, чтобы внести вклад в чужой проект.
+#     1.Делаем форк, нужного репозитория
+#     2.Клонируем форк на наш ПК
+#     3.Заводим ишьюс, где прописываем, изменения, что меняем
+#     4.Открывает в IDE репозиторий
+#     5.Меняем как мы хотим сделать
+#     6.Коммитим, ссылаясь на наш ишьюс
+#     7.Отправляем PR, в проект, там описываем, что сделали, ссылаясь на ишьюс
+#     8.Запрашиваем ревью кода
+#
+# 3.1) Какие практики принято соблюдать при создании Pull Request чтобы закрыть автоматический issues?
+#     Нужно указывать closes и #номер_ишьюса
+# Какие практики принято соблюдать при создании commit чтобы закрыть автоматический issues?
+#     Нужно указать fix: #номер_ишьюса или также но с closes
+#
+# 3.2) Как отклонить/закрыть пул реквест? (предоставьте скриншот где это в гитхабе)
+#     ![image.png](attachment:image.png)
+# 3.3)Перед отправкой пул реквеста нужно ли создавать ишьюс?
+#     Мы создаем ишьюс в большинстве случаев, когда хотим обсудить тему и т.п,
+#     его не создают крайне редко
+# 3.4)В какой вкладке можно посмотреть список изменений который был в пул реквесте?
+#     Нужно нажать на кнопку Files Changed
+# 3.5)В какой вкладке находится страница обсуждений пул реквеста?
+#     Во вкладке 'Conversation'
+#
+# 4) Можно ли открыть пул реквест, если вы ничего не вносили в FORK?
+#
+# Нет, нельзя
+#
+# 4.1)  Что нужно сделать чтобы открыть пул реквест?
+#
+# Нажать на кнопку "Compare & pull request" между двумя ветками
+#
+# 4.2) Что нужно сделать Если ваш Форк устарел?
+#
+# Нужно зайти на свой форк, нажать на "Sync fork" -> "Update"
+#
+# 4.3) Что нужно сделать если в пул реквесте имеются конфликты слияния
+#
+# Открываем PR -> 'Resolve Conflicts' -> открывается редактор,
+# вносим изменения(редактируем), нажимаем 'Mark as resolved' -> 'Commit merge'
+#
+# 5) Что нужно сделать Для добавления отрывка кода в комментарии к ишьюсу?
+#
+# Если нужен однострочный код  - `Текст`
+# Если нужен многострочный код - ```Текст```
+#
+# 5.1) На какую клавишу нажать клавишу,
+# чтобы выделенный текст был включён как цитата в ваш комментарий?
+#
+# Нужно нажать на клавишу R, или вставить ">"
+#
+# 5.2) Как вставить картинку в ишьюс?
+#
+# Скопировать и вставить из буфера обмена
+#
+# 6) Как понять что ваш форк устарел?
+#
+# На странице форка появится "This branch is X commits behind main repositroy",
+# может появиться кнопка "Sync fork"
+#
+# 6.1) Как обновить форк?
+#
+# В нашем форке будет кнопка "Sync fork" -> "Update branch"
+#
+# 7) Как добавить участников в ваш репозиторий, чтобы команда могла работать над одним репозиторием?
+#
+# В репозитории зайти в настройки и там будет "Access" -> "Collaborators & teams",
+# далее жмем "Add people", вводим данные почту/гитхаб и т.п, выбираем роль и приглашаем
+#
+# 8) Какой символ нужен для упоминания кого-либо?
+#
+# @username
+#
+# 8.1) Где находится Центр уведомлений, напишите ссылку
+#
+# https://github.com/notifications
+#
+# 9) Что такое и зачем нужен файл README
+#
+# README - это файл, содержащий информацию о проекте, код, набор данных и т.д.
+# Нужен для того, чтобы новые люди могли легко разобраться в проекте и
+# сделать представление о нем.
+#
+# 9.1) Что такое и зачем нужен файл CONTRIBUTING
+#
+# CONTRIBUTING - это файл, содержащий ифнормацию о том, какие люди могут внести
+# свой вклад в проект(код, докумантация и т.д), нужен чтобы новички
+# понимали, что от них ожидается
+#
+# 10) Как измененить основную ветку?
+#
+# Открываем настройки репозитория, выбираем "Branches", там будет показана
+# текущая ветка, нажимаем "Edit", выбираем новую -> "Update"
+#
+# 10.1) Как передать проект? какая кнопка?
+#
+# В репозитории, который мы хотим передать, открываем найстроки,
+# листаем до поля "Danger Zone", находим кнопку "Transfer ownership",
+# и там выполяем то что просят сделать
+#
+# 10.2) Что такое файл .gitignore?
+#
+# Файл, в котором лежат папки/файлы, которые Git должен игнорировать
+#
+#
+#
+
+#
+
+#
+
+#
+
+#
diff --git a/log.ipynb b/log.ipynb
new file mode 100644
index 00000000..38cd0be2
--- /dev/null
+++ b/log.ipynb
@@ -0,0 +1,35 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3a7633ca",
+   "metadata": {},
+   "source": [
+    "20/08/25\n",
+    "\n",
+    "Ответил на вопросы из квиза"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/log.py b/log.py
new file mode 100644
index 00000000..e69de29b
diff --git a/python/clean-code/chapter_4_choosing_friendly_names.ipynb b/python/clean-code/chapter_4_choosing_friendly_names.ipynb
new file mode 100644
index 00000000..1305d4c7
--- /dev/null
+++ b/python/clean-code/chapter_4_choosing_friendly_names.ipynb
@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Выбор понятных имен.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выбор имен оченьь сложный процесс и субьективный, но имена нужно выбирать содержательные и краткие.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Схема регистра имен\n",
+    "Бывает три вида:\n",
+    "1) Змеиный регистр(snake_case) - разделяем слова \"_\", также \n",
+    "бывают запись в виде UPPER_SNAKE_CASE\n",
+    "2) Верблюжий регистр(camelCase) - первое слово пишется с маленькой буквы, \n",
+    "второе и следующие с заглавной\n",
+    "3) Схема Pascal(PascalCase) - тоже самое, что и во втором пункте, \n",
+    "только пишутся все слова с заглавной\n",
+    "\n",
+    "## Соглашение об именах PEP8\n",
+    "Существуют правила формирования имен:\n",
+    "1) Все буквы должны быть ASCII - латинскими буквами, без диакритических знаков\n",
+    "2) Имена модулей короткие, состоят только из нижнего регистра\n",
+    "3) Имена классов нужно записывать в PascalCase\n",
+    "4) Имена констант записываются в UPPER_SNAKE_CASE\n",
+    "5) Имена функций, методов и переменных записываются в обычном snake_case\n",
+    "6) Первый аргумент методов всегда должен называться self в нижнем регистре\n",
+    "7) Первый аргумент методов класса записывается, как cls в нижнем регистре\n",
+    "8) Приватные атрибуты классов всегда начинаются с символа \"_\"\n",
+    "9) Публичные атрибуты классов никогда не начинаются с \"_\"\n",
+    "\n",
+    "## Длина имен\n",
+    "Слишком длинные/короткие имена лучше не писать, но лучше писать относительно длинные\n",
+    "и понятные, т.к. код часто читают\n",
+    "Одна из ошибок очень короткие имена - не стоит писать имена, которые начинаются с\n",
+    "одной буквы или нескольких букв(пример: \"g\", \"mon\"), также отсутствие нормального\n",
+    "трактования(start)\n",
+    "Исключение из этого - переменные в циклах, и координаты(x,y)\n",
+    "Длинные имена лучше, коротких т.к. вносят болььше ясности, но\n",
+    "не стоит пропускать буквы в именах переменных(strcmp-string compare)\n",
+    "\n",
+    "## Префиксы в именах\n",
+    "Некоторые префиксы в именах могут быть избыточными.\n",
+    "Наприме в классе Cat c атрибутом weight, то понятно что переменная\n",
+    "с таким же именем будет относиться к весу.\n",
+    "Тажке устаревшая запись считается венгерская запись - strName,\n",
+    "когда мы указываем тип данных в именах. Можно использовать\n",
+    "префиксы has/is, благодаря ним код становится удобнее читать\n",
+    "Еще полезно писать в именах единицы измерения(kg,lbs).\n",
+    "Использование имен с числовыми последовательностями, не особо\n",
+    "приветствуется, но если есть веская причина, то можно использовать\n",
+    "\n",
+    "## Выбирайте имена пригодные к поиску\n",
+    "Необходимо выбирать имена, которые относительно уникальные и понятные.\n",
+    "Например: email не подойдет, а emailAddres уже более понятное и \n",
+    "уникальное.\n",
+    "\n",
+    "## Избегайте юмора/каламбуров\n",
+    "При выборе имен следует избегать шуток/юмора/жаргонов и т.п.\n",
+    "Так как другие разработчики не поймут, смысл или могут вообще \n",
+    "не понять вашу идею. Также нужно избегать встроенных имен,\n",
+    "таких как: list, all, any, int, min, max и т.д.\n",
+    "Худшие из возможных имен - data, temp, var\n",
+    "\n",
+    "## Итог\n",
+    "Выбор имен не влияет на саму работу кода/структуру, но \n",
+    "имена играют важную роль, когда другой человек будет читать\n",
+    "код, то при плохом выборе имен, он мало или ничего не поймет."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/clean-code/chapter_4_choosing_friendly_names.py b/python/clean-code/chapter_4_choosing_friendly_names.py
new file mode 100644
index 00000000..e559abf0
--- /dev/null
+++ b/python/clean-code/chapter_4_choosing_friendly_names.py
@@ -0,0 +1,63 @@
+"""Выбор понятных имен."""
+
+# Выбор имен оченьь сложный процесс и субьективный, но имена нужно выбирать содержательные и краткие.
+#
+
+# ## Схема регистра имен
+# Бывает три вида:
+# 1) Змеиный регистр(snake_case) - разделяем слова "_", также
+# бывают запись в виде UPPER_SNAKE_CASE
+# 2) Верблюжий регистр(camelCase) - первое слово пишется с маленькой буквы,
+# второе и следующие с заглавной
+# 3) Схема Pascal(PascalCase) - тоже самое, что и во втором пункте,
+# только пишутся все слова с заглавной
+#
+# ## Соглашение об именах PEP8
+# Существуют правила формирования имен:
+# 1) Все буквы должны быть ASCII - латинскими буквами, без диакритических знаков
+# 2) Имена модулей короткие, состоят только из нижнего регистра
+# 3) Имена классов нужно записывать в PascalCase
+# 4) Имена констант записываются в UPPER_SNAKE_CASE
+# 5) Имена функций, методов и переменных записываются в обычном snake_case
+# 6) Первый аргумент методов всегда должен называться self в нижнем регистре
+# 7) Первый аргумент методов класса записывается, как cls в нижнем регистре
+# 8) Приватные атрибуты классов всегда начинаются с символа "_"
+# 9) Публичные атрибуты классов никогда не начинаются с "_"
+#
+# ## Длина имен
+# Слишком длинные/короткие имена лучше не писать, но лучше писать относительно длинные
+# и понятные, т.к. код часто читают
+# Одна из ошибок очень короткие имена - не стоит писать имена, которые начинаются с
+# одной буквы или нескольких букв(пример: "g", "mon"), также отсутствие нормального
+# трактования(start)
+# Исключение из этого - переменные в циклах, и координаты(x,y)
+# Длинные имена лучше, коротких т.к. вносят болььше ясности, но
+# не стоит пропускать буквы в именах переменных(strcmp-string compare)
+#
+# ## Префиксы в именах
+# Некоторые префиксы в именах могут быть избыточными.
+# Наприме в классе Cat c атрибутом weight, то понятно что переменная
+# с таким же именем будет относиться к весу.
+# Тажке устаревшая запись считается венгерская запись - strName,
+# когда мы указываем тип данных в именах. Можно использовать
+# префиксы has/is, благодаря ним код становится удобнее читать
+# Еще полезно писать в именах единицы измерения(kg,lbs).
+# Использование имен с числовыми последовательностями, не особо
+# приветствуется, но если есть веская причина, то можно использовать
+#
+# ## Выбирайте имена пригодные к поиску
+# Необходимо выбирать имена, которые относительно уникальные и понятные.
+# Например: email не подойдет, а emailAddres уже более понятное и
+# уникальное.
+#
+# ## Избегайте юмора/каламбуров
+# При выборе имен следует избегать шуток/юмора/жаргонов и т.п.
+# Так как другие разработчики не поймут, смысл или могут вообще
+# не понять вашу идею. Также нужно избегать встроенных имен,
+# таких как: list, all, any, int, min, max и т.д.
+# Худшие из возможных имен - data, temp, var
+#
+# ## Итог
+# Выбор имен не влияет на саму работу кода/структуру, но
+# имена играют важную роль, когда другой человек будет читать
+# код, то при плохом выборе имен, он мало или ничего не поймет.
diff --git a/python/commit.ipynb b/python/commit.ipynb
new file mode 100644
index 00000000..cc7a898f
--- /dev/null
+++ b/python/commit.ipynb
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Про коммиты.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) Опишите своими словами назначение каждого из этих типов коммитов:\n",
+    "feat, fix, docs, style, refactor, test, build, ci, perf, chore.\n",
+    "\n",
+    "1.Тип feat - это добавление новой функции в код.\n",
+    "2.Тип fix - исправялет баг в коде.\n",
+    "3.Тип build - когда изменения произошли в документации.\n",
+    "4.Тип style - это изменение, которые не влияют на работу кода, такие как:\n",
+    "пробелы, знаки препинания \n",
+    "5.Тип refactor - это улучшение программы/кода, без изменения его смысла,\n",
+    "но, делающее его более читаемым, удобным, масштабируемым.Делает код более\n",
+    "понятным\n",
+    "6.Тип test - это добавление новых тестов, либо корректировка/изменение старых\n",
+    "7.Тип build - это изменение, которое влияет на внешние системы или на систему сборки\n",
+    "8.Тип ci - это изменение в файлах влияющих на автоматизацию, изменение в файлах ci\n",
+    "9.Тип perf - это изменение кода, влияющее на его производительность\n",
+    "10.Тип chore - это изменения, не влияющие на работу кода, а влияющие на рутинные задачи, \n",
+    "вспомогательные задачи\n",
+    "\n",
+    "2) Представьте, что вы исправили баг в функции, которая некорректно округляет числа. \n",
+    "Сделайте фиктивный коммит и напишите для него сообщение \n",
+    "в соответствии с Conventional Commits (используя тип fix).\n",
+    "\n",
+    "git commit -m 'fix: correct rounding of numbers'\n",
+    "\n",
+    "3) Добавление новой функциональности:\n",
+    "Допустим, вы реализовали новую функцию generateReport в проекте. \n",
+    "Сделайте фиктивный коммит с типом feat, отражающий добавление этой функциональности\n",
+    "\n",
+    "git commit -m 'feat: add new function generateReport in project'\n",
+    "\n",
+    "4) Модификация формата кода или стилей:\n",
+    "Представьте, что вы поправили отступы и форматирование во всём проекте, не меняя логики кода. \n",
+    "Сделайте фиктивный коммит с типом style\n",
+    "\n",
+    "git commit -m 'style: delete indentation and formatting project'\n",
+    "\n",
+    "5) Документация и тестирование:\n",
+    "Сделайте фиктивный коммит с типом docs, \n",
+    "добавляющий или улучшающий документацию для вашей новой функции.\n",
+    "Сделайте фиктивный коммит с типом test, \n",
+    "добавляющий тесты для этой же функции.\n",
+    "\n",
+    "git commit -m 'docs: add new documentation for new func'\n",
+    "git commit -m 'test: add new test for new func'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/commit.py b/python/commit.py
new file mode 100644
index 00000000..f8a37d67
--- /dev/null
+++ b/python/commit.py
@@ -0,0 +1,48 @@
+"""Про коммиты."""
+
+# 1) Опишите своими словами назначение каждого из этих типов коммитов:
+# feat, fix, docs, style, refactor, test, build, ci, perf, chore.
+#
+# 1.Тип feat - это добавление новой функции в код.
+# 2.Тип fix - исправялет баг в коде.
+# 3.Тип build - когда изменения произошли в документации.
+# 4.Тип style - это изменение, которые не влияют на работу кода, такие как:
+# пробелы, знаки препинания
+# 5.Тип refactor - это улучшение программы/кода, без изменения его смысла,
+# но, делающее его более читаемым, удобным, масштабируемым.Делает код более
+# понятным
+# 6.Тип test - это добавление новых тестов, либо корректировка/изменение старых
+# 7.Тип build - это изменение, которое влияет на внешние системы или на систему сборки
+# 8.Тип ci - это изменение в файлах влияющих на автоматизацию, изменение в файлах ci
+# 9.Тип perf - это изменение кода, влияющее на его производительность
+# 10.Тип chore - это изменения, не влияющие на работу кода, а влияющие на рутинные задачи,
+# вспомогательные задачи
+#
+# 2) Представьте, что вы исправили баг в функции, которая некорректно округляет числа.
+# Сделайте фиктивный коммит и напишите для него сообщение
+# в соответствии с Conventional Commits (используя тип fix).
+#
+# git commit -m 'fix: correct rounding of numbers'
+#
+# 3) Добавление новой функциональности:
+# Допустим, вы реализовали новую функцию generateReport в проекте.
+# Сделайте фиктивный коммит с типом feat, отражающий добавление этой функциональности
+#
+# git commit -m 'feat: add new function generateReport in project'
+#
+# 4) Модификация формата кода или стилей:
+# Представьте, что вы поправили отступы и форматирование во всём проекте, не меняя логики кода.
+# Сделайте фиктивный коммит с типом style
+#
+# git commit -m 'style: delete indentation and formatting project'
+#
+# 5) Документация и тестирование:
+# Сделайте фиктивный коммит с типом docs,
+# добавляющий или улучшающий документацию для вашей новой функции.
+# Сделайте фиктивный коммит с типом test,
+# добавляющий тесты для этой же функции.
+#
+# git commit -m 'docs: add new documentation for new func'
+# git commit -m 'test: add new test for new func'
+
+#
diff --git a/python/cpython.ipynb b/python/cpython.ipynb
new file mode 100644
index 00000000..49fb1eb4
--- /dev/null
+++ b/python/cpython.ipynb
@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Cpython.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) Что такое CPython и чем он отличается от Python?\n",
+    "\n",
+    "Cpython - это стандартная и наиболее широко используемая реализация языка Python. Cpython это в определенной степени компилятор, так как перед интерпретацией он преобразует код Python в байт-код. Python - это спецификация и концепция языка программирования. В итоге их отличия в том, что - Cpython это интерпретатор, написанная на С, а Python концепция ЯП.\n",
+    "\n",
+    "2) Сколько существует реализаций Python, и какая из них самая популярная?\n",
+    "\n",
+    "Всего реализаций 6. Самая популянрная реализация - Cpython\n",
+    "\n",
+    "3) На каком языке написан CPython?\n",
+    "\n",
+    "Написан на C\n",
+    "\n",
+    "4) Кто создал CPython?\n",
+    "\n",
+    "CPython создал Гвидо ван Россум\n",
+    "\n",
+    "5) Почему Python считается быстрым, несмотря на то, что это интерпретируемый язык?\n",
+    "\n",
+    "Так как ядро написано на С, python считается быстрым.\n",
+    "\n",
+    "6) Напишите путь к Интерпретатору CPython на вашем компьютере\n",
+    "\n",
+    "/Users/Shared/anaconda3/bin/python3(на маке почему то выводит без \"C\")\n",
+    "\n",
+    "7) Что содержится в папке include в CPython?\n",
+    "\n",
+    "Содержится файл abstract.h\n",
+    "\n",
+    "8) Где можно найти исходный код CPython дайте ссылку на репозиторий гитхаб\n",
+    "\n",
+    "https://github.com/python/cpython\n",
+    "\n",
+    "9) Как работает интерпретатор CPython при выполнении кода?\n",
+    "\n",
+    "    1. Сначала происходит компиляция кода в байт-код\n",
+    "    2. Потом просходит исполнение в виртуальной машине Python\n",
+    "    3. После исполнения и проверки в ВМ, преобразовывается все в машинный код\n",
+    "\n",
+    "10) Какая команда используется для запуска файла с помощью CPython?\n",
+    "\n",
+    "В cmd вставляем путь до интерпретатора, потом путь до файла\n",
+    "\n",
+    "11)  Можно ли запускать текстовые файлы через интерпретатор Python? Почему?\n",
+    "\n",
+    "Можно, так как интерпретатор выполняет и запускает код из такого файла как скрипт\n",
+    "\n",
+    "12) Как указать путь к интерпретатору и файлу для выполнения кода?\n",
+    "\n",
+    "В папке Python -> Python312 нужно найти python.exe(это и будет интерпретатор) и также путь до файла просто нужно его скопировать\n",
+    "\n",
+    "13) Чем PyPy отличается от CPython?\n",
+    "\n",
+    "Cpython - это стандартная и наиболее широко используемая реализация языка Python. Cpython это в определенной степени компилятор, так как перед интерпретацией он преобразует код Python в байт-код. PyPy - это альтернативная реализация Python, написанная на самом Python\n",
+    "\n",
+    "14) Почему PyPy не может использоваться для всех проектов на Python?\n",
+    "\n",
+    "Он не может использоваться на всех проектах, так как большая часть кода на Python написана на C/C++ (pandas,TensorFlow,NumPy и т.п.).\n",
+    "PyPy более медленный на C-образных библиотеках, Cpython проще поддерживать, так как более популярен.\n",
+    "\n",
+    "15) Где можно скачать PyPy?\n",
+    "\n",
+    "https://pypy.org/download.html?utm_source=chatgpt.com\n",
+    "\n",
+    "16) Как установить PyPy после скачивания?\n",
+    "\n",
+    "Распаковываем архив, находим нужную папку, добавляем в PATH\n",
+    "\n",
+    "17) Как запустить файл с помощью PyPy?\n",
+    "\n",
+    "Нужно указать путь, но в конце поставить .../pypy3 наш_файл.py\n",
+    "\n",
+    "18) Почему PyPy выполняет код быстрее, чем CPython?\n",
+    "\n",
+    "Использует JIT((Just-In-Time) - компилирует код в машшиный код сразу), который превращает часто исполняемый код в машинный\n",
+    "\n",
+    "Практические задания:\n",
+    "\n",
+    "1) Поиск и установка CPython\n",
+    " \n",
+    "python3 --version\n",
+    "Python 3.11.4\n",
+    "\n",
+    "2) Исследование структуры CPython\n",
+    "\n",
+    "Довльно много файлов на С\n",
+    "\n",
+    "3) Создайте текстовый файл example.txt с содержимым: print(\"Hello from CPython!\")\n",
+    "\n",
+    "C txt:\n",
+    "python /Users/artem/Desktop/example.txt \n",
+    "Hello from CPython\n",
+    "\n",
+    "C py:\n",
+    "python /Users/Shared/anaconda3/pkgs/spyder-5.4.3-py311hecd8cb5_1/lib/python3.11/site-packages/spyder/widgets/github/Data-Science-For-Beginners-from-scratch-SENATOROV/exm.py\n",
+    "Hello from Cpython\n",
+    "\n",
+    "4) Задание 4: Установка и использование PyPy\n",
+    "\n",
+    "Вывод одинаковый, что с txt, что c py\n",
+    "\n",
+    "5) Задание 5: Сравнение производительности CPython и PyPy\n",
+    "\n",
+    "У Cpython - 1.5 секунды, у pypy - 0.3 секунды (с окргулением все)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/cpython.py b/python/cpython.py
new file mode 100644
index 00000000..4075a84e
--- /dev/null
+++ b/python/cpython.py
@@ -0,0 +1,112 @@
+"""Cpython."""
+
+#
+
+# 1) Что такое CPython и чем он отличается от Python?
+#
+# Cpython - это стандартная и наиболее широко используемая реализация языка Python. Cpython это в определенной степени компилятор, так как перед интерпретацией он преобразует код Python в байт-код. Python - это спецификация и концепция языка программирования. В итоге их отличия в том, что - Cpython это интерпретатор, написанная на С, а Python концепция ЯП.
+#
+# 2) Сколько существует реализаций Python, и какая из них самая популярная?
+#
+# Всего реализаций 6. Самая популянрная реализация - Cpython
+#
+# 3) На каком языке написан CPython?
+#
+# Написан на C
+#
+# 4) Кто создал CPython?
+#
+# CPython создал Гвидо ван Россум
+#
+# 5) Почему Python считается быстрым, несмотря на то, что это интерпретируемый язык?
+#
+# Так как ядро написано на С, python считается быстрым.
+#
+# 6) Напишите путь к Интерпретатору CPython на вашем компьютере
+#
+# /Users/Shared/anaconda3/bin/python3(на маке почему то выводит без "C")
+#
+# 7) Что содержится в папке include в CPython?
+#
+# Содержится файл abstract.h
+#
+# 8) Где можно найти исходный код CPython дайте ссылку на репозиторий гитхаб
+#
+# https://github.com/python/cpython
+#
+# 9) Как работает интерпретатор CPython при выполнении кода?
+#
+#     1. Сначала происходит компиляция кода в байт-код
+#     2. Потом просходит исполнение в виртуальной машине Python
+#     3. После исполнения и проверки в ВМ, преобразовывается все в машинный код
+#
+# 10) Какая команда используется для запуска файла с помощью CPython?
+#
+# В cmd вставляем путь до интерпретатора, потом путь до файла
+#
+# 11)  Можно ли запускать текстовые файлы через интерпретатор Python? Почему?
+#
+# Можно, так как интерпретатор выполняет и запускает код из такого файла как скрипт
+#
+# 12) Как указать путь к интерпретатору и файлу для выполнения кода?
+#
+# В папке Python -> Python312 нужно найти python.exe(это и будет интерпретатор) и также путь до файла просто нужно его скопировать
+#
+# 13) Чем PyPy отличается от CPython?
+#
+# Cpython - это стандартная и наиболее широко используемая реализация языка Python. Cpython это в определенной степени компилятор, так как перед интерпретацией он преобразует код Python в байт-код. PyPy - это альтернативная реализация Python, написанная на самом Python
+#
+# 14) Почему PyPy не может использоваться для всех проектов на Python?
+#
+# Он не может использоваться на всех проектах, так как большая часть кода на Python написана на C/C++ (pandas,TensorFlow,NumPy и т.п.).
+# PyPy более медленный на C-образных библиотеках, Cpython проще поддерживать, так как более популярен.
+#
+# 15) Где можно скачать PyPy?
+#
+# https://pypy.org/download.html?utm_source=chatgpt.com
+#
+# 16) Как установить PyPy после скачивания?
+#
+# Распаковываем архив, находим нужную папку, добавляем в PATH
+#
+# 17) Как запустить файл с помощью PyPy?
+#
+# Нужно указать путь, но в конце поставить .../pypy3 наш_файл.py
+#
+# 18) Почему PyPy выполняет код быстрее, чем CPython?
+#
+# Использует JIT((Just-In-Time) - компилирует код в машшиный код сразу), который превращает часто исполняемый код в машинный
+#
+# Практические задания:
+#
+# 1) Поиск и установка CPython
+#
+# python3 --version
+# Python 3.11.4
+#
+# 2) Исследование структуры CPython
+#
+# Довльно много файлов на С
+#
+# 3) Создайте текстовый файл example.txt с содержимым: print("Hello from CPython!")
+#
+# C txt:
+# python /Users/artem/Desktop/example.txt
+# Hello from CPython
+#
+# C py:
+# python /Users/Shared/anaconda3/pkgs/spyder-5.4.3-py311hecd8cb5_1/lib/python3.11/site-packages/spyder/widgets/github/Data-Science-For-Beginners-from-scratch-SENATOROV/exm.py
+# Hello from Cpython
+#
+# 4) Задание 4: Установка и использование PyPy
+#
+# Вывод одинаковый, что с txt, что c py
+#
+# 5) Задание 5: Сравнение производительности CPython и PyPy
+#
+# У Cpython - 1.5 секунды, у pypy - 0.3 секунды (с окргулением все)
+
+
+#
+
+#
diff --git a/python/issue.ipynb b/python/issue.ipynb
new file mode 100644
index 00000000..f28fe32a
--- /dev/null
+++ b/python/issue.ipynb
@@ -0,0 +1,196 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Что такое issue и зачем это нужно.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) Что такое Issues на GitHub и для чего они используются?\n",
+    "\n",
+    "Ишьюсы используются для обучения программироованию или рассказывали как работает их код. \n",
+    "Также если мы не понимаем какие-либо строки кода в файле,\n",
+    "с помощью ишьюса мы можем попросить рассказать про них.\n",
+    "\n",
+    "2) Чем Issues отличаются от других инструментов управления задачами?\n",
+    "\n",
+    "Issues более удобны, так как они сразу поступают к человеку и сразу можно задать вопрос\n",
+    "по коду/ блоку кода/ целым файлам, и можно получить ответ в том же месте,\n",
+    "где и просходит разработка проекта/учеба и т.п. \n",
+    "Еще одно удобство, в одном месте мы можем сообщить о баге или ошибке в коде и не надо \n",
+    "открывать множество программ.\n",
+    "\n",
+    "3) Какие основные компоненты (поля) есть у каждого Issue?\n",
+    "\n",
+    "    1.Поле title - здесь мы вводим, кратко информацию о ситуации/проблеме\n",
+    "    2.Поле description - здесь мы вводим информацию о нашей проблеме и \n",
+    "    если нужно что-то по коду, то вставляем код.\n",
+    "    3.Поле milestone - привязка к дедлайну\n",
+    "    4.Поле labels - в этом поле указывается флаг(тема ишьюса, bug и т.п)\n",
+    "\n",
+    "4) Как создать новое Issue в репозитории?\n",
+    "\n",
+    "Нужно выделить строки непонятного для нас кода, \n",
+    "потом сверху возле кнопки '<Code>' будет кнопка Issues, \n",
+    "нажимаем на нее и далее будет зеленая кнокпа New issues, \n",
+    "потом находим ту тему которая больше подходит к нашей ситуации\n",
+    "(Bug report, feature request, other) и выбираем ее.\n",
+    "\n",
+    "5) Какие данные рекомендуется указывать в описании Issue для лучшего понимания задачи?\n",
+    "\n",
+    "Рекомендуется указывать файл с кодом, который мы не поняли или сообщили про ошибку\n",
+    "\n",
+    "6) Какие теги (labels) можно добавить к Issue? Какие из них стандартные?\n",
+    "\n",
+    "Теги: bug, dependencies, documentation, duplicate, enhancement, \n",
+    "good first issue, help wanted - как понял все они стандартные и в целом все теги, \n",
+    "которые можно выбрать в этом поле они стандартные.\n",
+    "\n",
+    "7) Как прикрепить Assignees (ответственных) к Issue?\n",
+    "\n",
+    "В поле Assignees нужно просто нажать и появится список тех кого можно сделать отвественными\n",
+    "\n",
+    "8) Как использовать Labels для классификации задач?\n",
+    "\n",
+    "Исходя из проблемы(темы issues) мы должы подобрать тег для ее классификации\n",
+    "\n",
+    "9) Для чего нужен Milestone, и как связать его с Issue?\n",
+    "\n",
+    "Нужем milestone для привязки issue к времени, \n",
+    "когда мы хотим решить данную проблему, \n",
+    "связать его с issue надо просто нажать на поле milestone и выбрать время\n",
+    "\n",
+    "10) Как привязать Issue к пул-реквесту (Pull Request)?\n",
+    "\n",
+    "1.Зайти в ишьюс и посмотреть, что за ошибка\n",
+    "2.Исправляем ошибку, сохраняем\n",
+    "3.Заходим в ишьюс, копируем тайтл и копируем его в тайт выпавшего окна\n",
+    "4.Обязательно нужно чтобы был milestone\n",
+    "5.В окне нажимаем на зеленую кнопку sign commit changes\n",
+    "    \n",
+    "11) Как добавить комментарий к существующему Issue?\n",
+    "\n",
+    "Нужно перейти сверху во вкладку Issues и выбрать нужный, \n",
+    "после пролистаем ниже и будет поле add a comment\n",
+    "\n",
+    "12) Как закрыть Issue вручную?\n",
+    "\n",
+    "Во вкладке issues сверху, переходим на нужный issue и закрываем вручную\n",
+    "\n",
+    "13) Можно ли автоматически закрыть Issue с помощью сообщения в коммите или пул-реквесте? Как это сделать?\n",
+    "\n",
+    "Можно через коммит, нужно написать в title #(номер ошибки), \n",
+    "в description closes #(номер ошибки)\n",
+    "\n",
+    "14) Как повторно открыть закрытое Issue, если работа ещё не завершена?\n",
+    "\n",
+    "Также находим issue, только уже закрытый и открываем вручную - кнопка Reopen issue\n",
+    "\n",
+    "15) Как найти все открытые или закрытые Issues в репозитории?\n",
+    "\n",
+    "Сверху во вкладке Issue, \n",
+    "после нажатия будет поиск и там будет написано - 'Open', 'Closed' и находим нужный нам\n",
+    "\n",
+    "16) Как использовать фильтры для поиска Issues по меткам, исполнителям или другим критериям?\n",
+    "\n",
+    "1.Если ищем по лейблам, \n",
+    "то нужно указать какие там была проблема(bag, duplicate и т.п.)\n",
+    "2.Если ищем по исполнителям, \n",
+    "то нужно указать какие люди работали над issue(если их конечно указывали)\n",
+    "3.Если ищем по времени(milestones), \n",
+    "то указываем дату либо месяц, когда работали над проблемой\n",
+    "\n",
+    "17) Как сортировать Issues по приоритету, дате создания или другим параметрам?\n",
+    "\n",
+    "Во вкладке Issues, будут соответсвующие вклади(author, labels, project и т.п) ищем нужную нам\n",
+    "\n",
+    "18) Как настроить автоматические уведомления о новых или изменённых Issues?\n",
+    "\n",
+    "Самый простой способ получать информацию - через мессенджеры\n",
+    "\n",
+    "19) Что такое Projects в контексте GitHub, и как связать их с Issues?\n",
+    "\n",
+    "Projects в гитхабе - если кратко, то это таблица для планирования. \n",
+    "Служит для организации работы, можно создавать и настраивать несколько представлений, \n",
+    "отфильтровав, сортируя, срезы и группируя запросы на вытягивание, \n",
+    "чтобы управлять невыполненной работой команды и планами.\n",
+    "\n",
+    "20) Какие сторонние инструменты можно использовать для автоматизации работы с Issues (например, боты, Webhooks)?\n",
+    "\n",
+    "Можно использовать, как пример ТГ-ботов, \n",
+    "чтобы автоматизровать работу с issues \n",
+    "или веб-хуки нужные для создания уведомлений на работу с issue\n",
+    "\n",
+    "21) Как упомянуть другого пользователя в комментарии к Issue?\n",
+    "\n",
+    "Нужно указать имя другого пользователя и он получит уведомление\n",
+    "\n",
+    "22) Как запросить дополнительные данные или уточнения у автора Issue?\n",
+    "\n",
+    "Можно оставить комментарий для уточнения, \n",
+    "либо же попросить, чтобы автор прислал сслыку(permalink) на код\n",
+    "\n",
+    "23) Что делать, если Issue неактуально или его нужно объединить с другим?\n",
+    "\n",
+    "Либо закрыть issue с комменатрием, \n",
+    "либо обьедитить его с другим с помощью duplicate\n",
+    "\n",
+    "24) Как использовать шаблоны для создания Issues?\n",
+    "\n",
+    "Нужно добавить файл шаблоный для ишьюсов (ISSUE_TEMPELATE) в проект, \n",
+    "и тогда новые разработчики будут придерживаться этого шаблона, \n",
+    "что может сделать более читаемым issues\n",
+    "\n",
+    "25) Что такое Linked Issues, и как создать связь между задачами?\n",
+    "\n",
+    "Что такое Linked Issues - это взаимосвязь между задачами. \n",
+    "Создается связь через интерфейс - в разделе linked issue и там будут три типа связи\n",
+    ",is blocked by - данная задача ждет другую, \n",
+    "blocks - данная задача блокирует другую, \n",
+    "relates to - обычная связь без блокировки\n",
+    "\n",
+    "26) Какие метрики (например, время выполнения) можно отслеживать с помощью Issues?\n",
+    "\n",
+    "Можно отслеживать:\n",
+    "\n",
+    "1.Время открытия/закрытия\n",
+    "2.Вовремя ли закрыли(в milestone)\n",
+    "3.Количество открытых задач\n",
+    "4.Сколько людей трудятся над задачей\n",
+    "5.Какие теги были указаны\n",
+    "\n",
+    "27) Какие best practices рекомендуются при работе с Issues в команде?\n",
+    "\n",
+    "1.Писать issues по шаблону на работе\n",
+    "2.Подробно описывать ситуацию/проблему\n",
+    "3.Уведомлять об открытии/закрытии\n",
+    "4.Давать обратную связь по выполненной работе\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/issue.py b/python/issue.py
new file mode 100644
index 00000000..8a437622
--- /dev/null
+++ b/python/issue.py
@@ -0,0 +1,166 @@
+"""Что такое issue и зачем это нужно."""
+
+# 1) Что такое Issues на GitHub и для чего они используются?
+#
+# Ишьюсы используются для обучения программироованию или рассказывали как работает их код.
+# Также если мы не понимаем какие-либо строки кода в файле,
+# с помощью ишьюса мы можем попросить рассказать про них.
+#
+# 2) Чем Issues отличаются от других инструментов управления задачами?
+#
+# Issues более удобны, так как они сразу поступают к человеку и сразу можно задать вопрос
+# по коду/ блоку кода/ целым файлам, и можно получить ответ в том же месте,
+# где и просходит разработка проекта/учеба и т.п.
+# Еще одно удобство, в одном месте мы можем сообщить о баге или ошибке в коде и не надо
+# открывать множество программ.
+#
+# 3) Какие основные компоненты (поля) есть у каждого Issue?
+#
+#     1.Поле title - здесь мы вводим, кратко информацию о ситуации/проблеме
+#     2.Поле description - здесь мы вводим информацию о нашей проблеме и
+#     если нужно что-то по коду, то вставляем код.
+#     3.Поле milestone - привязка к дедлайну
+#     4.Поле labels - в этом поле указывается флаг(тема ишьюса, bug и т.п)
+#
+# 4) Как создать новое Issue в репозитории?
+#
+# Нужно выделить строки непонятного для нас кода,
+# потом сверху возле кнопки '<Code>' будет кнопка Issues,
+# нажимаем на нее и далее будет зеленая кнокпа New issues,
+# потом находим ту тему которая больше подходит к нашей ситуации
+# (Bug report, feature request, other) и выбираем ее.
+#
+# 5) Какие данные рекомендуется указывать в описании Issue для лучшего понимания задачи?
+#
+# Рекомендуется указывать файл с кодом, который мы не поняли или сообщили про ошибку
+#
+# 6) Какие теги (labels) можно добавить к Issue? Какие из них стандартные?
+#
+# Теги: bug, dependencies, documentation, duplicate, enhancement,
+# good first issue, help wanted - как понял все они стандартные и в целом все теги,
+# которые можно выбрать в этом поле они стандартные.
+#
+# 7) Как прикрепить Assignees (ответственных) к Issue?
+#
+# В поле Assignees нужно просто нажать и появится список тех кого можно сделать отвественными
+#
+# 8) Как использовать Labels для классификации задач?
+#
+# Исходя из проблемы(темы issues) мы должы подобрать тег для ее классификации
+#
+# 9) Для чего нужен Milestone, и как связать его с Issue?
+#
+# Нужем milestone для привязки issue к времени,
+# когда мы хотим решить данную проблему,
+# связать его с issue надо просто нажать на поле milestone и выбрать время
+#
+# 10) Как привязать Issue к пул-реквесту (Pull Request)?
+#
+# 1.Зайти в ишьюс и посмотреть, что за ошибка
+# 2.Исправляем ошибку, сохраняем
+# 3.Заходим в ишьюс, копируем тайтл и копируем его в тайт выпавшего окна
+# 4.Обязательно нужно чтобы был milestone
+# 5.В окне нажимаем на зеленую кнопку sign commit changes
+#
+# 11) Как добавить комментарий к существующему Issue?
+#
+# Нужно перейти сверху во вкладку Issues и выбрать нужный,
+# после пролистаем ниже и будет поле add a comment
+#
+# 12) Как закрыть Issue вручную?
+#
+# Во вкладке issues сверху, переходим на нужный issue и закрываем вручную
+#
+# 13) Можно ли автоматически закрыть Issue с помощью сообщения в коммите или пул-реквесте? Как это сделать?
+#
+# Можно через коммит, нужно написать в title #(номер ошибки),
+# в description closes #(номер ошибки)
+#
+# 14) Как повторно открыть закрытое Issue, если работа ещё не завершена?
+#
+# Также находим issue, только уже закрытый и открываем вручную - кнопка Reopen issue
+#
+# 15) Как найти все открытые или закрытые Issues в репозитории?
+#
+# Сверху во вкладке Issue,
+# после нажатия будет поиск и там будет написано - 'Open', 'Closed' и находим нужный нам
+#
+# 16) Как использовать фильтры для поиска Issues по меткам, исполнителям или другим критериям?
+#
+# 1.Если ищем по лейблам,
+# то нужно указать какие там была проблема(bag, duplicate и т.п.)
+# 2.Если ищем по исполнителям,
+# то нужно указать какие люди работали над issue(если их конечно указывали)
+# 3.Если ищем по времени(milestones),
+# то указываем дату либо месяц, когда работали над проблемой
+#
+# 17) Как сортировать Issues по приоритету, дате создания или другим параметрам?
+#
+# Во вкладке Issues, будут соответсвующие вклади(author, labels, project и т.п) ищем нужную нам
+#
+# 18) Как настроить автоматические уведомления о новых или изменённых Issues?
+#
+# Самый простой способ получать информацию - через мессенджеры
+#
+# 19) Что такое Projects в контексте GitHub, и как связать их с Issues?
+#
+# Projects в гитхабе - если кратко, то это таблица для планирования.
+# Служит для организации работы, можно создавать и настраивать несколько представлений,
+# отфильтровав, сортируя, срезы и группируя запросы на вытягивание,
+# чтобы управлять невыполненной работой команды и планами.
+#
+# 20) Какие сторонние инструменты можно использовать для автоматизации работы с Issues (например, боты, Webhooks)?
+#
+# Можно использовать, как пример ТГ-ботов,
+# чтобы автоматизровать работу с issues
+# или веб-хуки нужные для создания уведомлений на работу с issue
+#
+# 21) Как упомянуть другого пользователя в комментарии к Issue?
+#
+# Нужно указать имя другого пользователя и он получит уведомление
+#
+# 22) Как запросить дополнительные данные или уточнения у автора Issue?
+#
+# Можно оставить комментарий для уточнения,
+# либо же попросить, чтобы автор прислал сслыку(permalink) на код
+#
+# 23) Что делать, если Issue неактуально или его нужно объединить с другим?
+#
+# Либо закрыть issue с комменатрием,
+# либо обьедитить его с другим с помощью duplicate
+#
+# 24) Как использовать шаблоны для создания Issues?
+#
+# Нужно добавить файл шаблоный для ишьюсов (ISSUE_TEMPELATE) в проект,
+# и тогда новые разработчики будут придерживаться этого шаблона,
+# что может сделать более читаемым issues
+#
+# 25) Что такое Linked Issues, и как создать связь между задачами?
+#
+# Что такое Linked Issues - это взаимосвязь между задачами.
+# Создается связь через интерфейс - в разделе linked issue и там будут три типа связи
+# ,is blocked by - данная задача ждет другую,
+# blocks - данная задача блокирует другую,
+# relates to - обычная связь без блокировки
+#
+# 26) Какие метрики (например, время выполнения) можно отслеживать с помощью Issues?
+#
+# Можно отслеживать:
+#
+# 1.Время открытия/закрытия
+# 2.Вовремя ли закрыли(в milestone)
+# 3.Количество открытых задач
+# 4.Сколько людей трудятся над задачей
+# 5.Какие теги были указаны
+#
+# 27) Какие best practices рекомендуются при работе с Issues в команде?
+#
+# 1.Писать issues по шаблону на работе
+# 2.Подробно описывать ситуацию/проблему
+# 3.Уведомлять об открытии/закрытии
+# 4.Давать обратную связь по выполненной работе
+#
+
+#
+
+#
diff --git a/python/made-easy/chapter_1.ipynb b/python/made-easy/chapter_1.ipynb
new file mode 100644
index 00000000..a779861b
--- /dev/null
+++ b/python/made-easy/chapter_1.ipynb
@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Упражения.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.5.1\n",
+    "1) Какие предметные области входят в Data Science? \n",
+    "Что между ними общего и в чем различие?\n",
+    "В DS входят - математика, статистика, алгоритмы, сбор данных,\n",
+    "программная инженерия. Общее то, что сбором данных и\n",
+    "математикой/статистикой/алгоритмами занимается аналитик данных,\n",
+    "а программной инженерией, математик и т.д. занимается дата инженер\n",
+    "\n",
+    "2) Как вы понимаете термин «алгоритм»?Как алгоритмы связаны с блок-схемами?\n",
+    "Алгоритм - это последовательность выполнение каких-либо действий/команд.Блок-схема\n",
+    "это ранний способо представить визуально алгоритм\n",
+    "\n",
+    "3) Какую программу можно назвать хорошей? Запишите все характеристики, какие удастся придумать.\n",
+    "Я думаю, что программу можно назвать хорошей, если она организована для пользователей,\n",
+    "устройств, разработчиков. Характеристики: реализован весь функционал, безопасность,\n",
+    "оптимизация под разные платформы/версии устройств, совместимость.\n",
+    "\n",
+    "4)  Какой язык понимает компьютер?\n",
+    "Он понимает машинный код(двоичный)\n",
+    "\n",
+    "5) Чем языки программирования отличаются от языков, на которых мы говорим?\n",
+    "Языки на которых мы говорим, не понимает ПК, также ЯП имеют строгую типизацию,\n",
+    "синтаксис и предназначен для написания \"команд для ПК\".\n",
+    "\n",
+    "## 1.5.2\n",
+    "1) Машинное обучение-это инструмент для извлечения знаний из данных.\n",
+    "Правда\n",
+    "\n",
+    "2) Глубокое обучение-это то же самое,что машинное обучение.\n",
+    "Ложь\n",
+    "\n",
+    "3) Всеи нженеры-программисты также могут считаться специалистами по данным.\n",
+    "Ложь\n",
+    "\n",
+    "4) Статистика-важный инструмент для специалистов по данным.\n",
+    "Правда\n",
+    "\n",
+    "5) Компьютер может принимать решения, выходящие за рамки данных ему инструкций, \n",
+    "подстраиваясь под изменения среды.\n",
+    "Ложь\n",
+    "\n",
+    "6) Компьютеры понимают языки программирования «как есть».\n",
+    "Ложь\n",
+    "\n",
+    "7) Некоторые языки программирования компилируются,\n",
+    "некоторые интерпрети­руются, а некоторые используют и то и другое.\n",
+    "Правда\n",
+    "\n",
+    "8) Все программы выполняются последовательно\n",
+    "Ложь\n",
+    "\n",
+    "9) В IDE есть встроенный текстовый редактор.\n",
+    "Правда \n",
+    "\n",
+    "10) Компиляторы и интерпретаторы - это такие механизмы, \n",
+    "наподобие привода для компакт-дисков.\n",
+    "Ложь\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    },
+    "image-3.png": {
+     "image/png": 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"
+    },
+    "image-4.png": {
+     "image/png": 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"
+    },
+    "image-5.png": {
+     "image/png": 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"
+    },
+    "image-6.png": {
+     "image/png": 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"
+    },
+    "image.png": {
+     "image/png": 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IIIAAAggggAACCHggQGLjQSUQAgIIIIAAAggggAACCKQTILFJ58fUCCCAAAIIIIAAAggg4IEAiY0HlUAICCCAAAIIIIAAAgggkE6AxCadH1MjgAACCCCAAAIIIICABwIkNh5UAiEggAACCCCAAAIIIIBAOgESm3R+TI0AAggggAACCCCAAAIeCJDYeFAJhIAAAggggAACCCCAAALpBEhs0vkxNQIIIIAAAggggAACCHggQGLjQSUQAgIIIIAAAggggAACCKQTILFJ58fUCCCAAAIIIIAAAggg4IEAiY0HlUAICCCAAAIIIIAAAgggkE6AxCadH1MjgAACCCCAAAIIIICABwIkNh5UAiEggAACCCCAAAIIIIBAOgESm3R+TI0AAggggAACCCCAAAIeCJDYeFAJhIAAAggggAACCCCAAALpBEhs0vkxNQIIIIAAAggggAACCHggQGLjQSUQAgIIIIAAAggggAACCKQTILFJ58fUCCCAAAIIIIAAAggg4IEAiY0HlUAICCCAAAIIIIAAAgggkE6AxCadH1MjgAACCCCAAAIIIICABwIkNh5UAiEggAACCCCAAAIIIIBAOgESm3R+TI0AAggggAACCCCAAAIeCJDYeFAJhIAAAggggAACCCCAAALpBEhs0vkxNQIIIIAAAggggAACCHggQGLjQSUQAgIIIIAAAggggAACCKQTILFJ58fUCCCAAAIIIIAAAggg4IEAiY0HlUAICCCAAAIIIIAAAgggkE6AxCadH1MjgAACCCCAAAIIIICABwIkNh5UAiEggAACCCCAAAIIIIBAOgESm3R+TI0AAggggAACCCCAAAIeCJDYeFAJhIAAAggggAACCCCAAALpBEhs0vkxNQIIIIAAAggggAACCHggQGLjQSUQAgIIIIAAAggggAACCKQTILFJ58fUCCCAAAIIIIAAAggg4IFAj8mTJ+/yIA5CQAABBBBAAAEEEEAAAQTKFuCMTdl0TIgAAggggAACCCCAAAK+CPRSIB0dHb7EkxVHc3Oz7fY1vtbWVhtfW1tbVty+dPjuR3zpvim++/H7qO/69f3753t8/D74faQTSDc1v4/69vO9fisZH2ds0n23mRoBBBBAAAEEEEAAAQQ8ECCx8aASCAEBBBBAAAEEEEAAAQTSCZDYpPNjagQQQAABBBBAAAEEEPBAgMTGg0ogBAQQQAABBBBAAAEEEEgnQGKTzo+pEUAAAQQQQAABBBBAwAMBEhsPKoEQEEAAAQQQQAABBBBAIJ0AiU06P6ZGAAEEEEAAAQQQQAABDwRIbDyoBEJAAAEEEEAAAQQQQACBdAIkNun8mBoBBBBAAAEEEEAAAQQ8ECCx8aASCAEBBBBAAAEEEEAAAQTSCZDYpPNjagQQQAABBBBAAAEEEPBAgMTGg0ogBAQQQAABBBBAAAEEEEgnQGKTzo+pEUAAAQQQQAABBBBAwAMBEhsPKoEQEEAAAQQQQAABBBBAIJ0AiU06P6ZGAAEEEEAAAQQQQAABDwRIbDyoBEJAAAEEEEAAAQQQQACBdAIkNun8mBoBBBBAAAEEEEAAAQQ8ECCx8aASCAEBBBBAAAEEEEAAAQTSCZDYpPNjagQQQAABBBBAAAEEEPBAgMTGg0ogBAQQQAABBBBAAAEEEEgnQGKTzo+pEUAAAQQQQAABBBBAwAMBEhsPKoEQEEAAAQQQQAABBBBAIJ0AiU06P6ZGAAEEEEAAAQQQQAABDwRIbDyoBEJAAAEEEEAAAQQQQACBdAIkNun8mBoBBBBAAAEEEEAAAQQ8ECCx8aASCAEBBBBAAAEEEEAAAQTSCfRKNzlTI4AAAtkCvXr1MqNGjTLDhg0zffv2NTt37jT9+vUzTU1N9uU+u3f17927d/ZM6EIgYIEdO3aYrVu32teWLVuy3tVf/fQ72bZtmxkwYIBZvXq1/Z0EvMqEjgACCHghQGLjRTUQBALhCgwePNgmMkpm9DrggAPCXRkiR6AbBJSo69XS0lL03NasWWOee+45+1q7dq3ZtGlT0dMyIgIIIIDAbgESG74JCCBQtICOMo8cOdKMGDEi8xo0aFCX6XUkev369WbDhg1m48aN5pVXXjHt7e2mo6PDaAeOgkCjCeh309zcbJOdffbZxwwcONC0traaoUOH2jObGq7X1KlTLU1bW1smyVGis27dOs7qNNqXhvVFAIGSBUhsSiZjAgQaS6Bnz55mwoQJ5phjjrFnZHQJWbS88cYbdgdMCYvbAeNoc1SIzwiYvAm9khyd6Rw+fLhNbg488ECb9CjxmTRpkuV7/fXX7TweffRRs2TJEvPmm2/CigACCCAQEyCxiYHQiQACuwV0JFnJjJKaIUOGZLGsWrXKPP3002bFihU2qckaSAcCCJQkoAMBei1dujQznZKbMWPGmLFjx5rRo0eb/v37m8MPP9y+Tj31VJvcKMnRmVEKAggggMBuARIbvgkIIJAlMG7cuMwZmh49emSGLV++3O54PfTQQ5l+fEAAgcoIuPtt5s2bZxcwefJkM378eHPEEUfYAw2nnXaaUYLjzuAsW7asMoEwVwQQQCAgARKbgCqLUBGolIBaZtKZGb10nb8rurxs8eLFZuHChWbXrl2uN+8IIFBlAR1Q0EsHG0466SQzceJE+1vVu176reoSNb02b95c5ehYHAIIIOCHAImNH/VAFAjUREAtmulmZV1yFr13xu1E6agxBQEE/BHQAYb77rvPvnS5ms7k6OUaH3Bnce69917beIc/kRMJAgggUHkBEpvKG7MEBLwUmDJlir2UxTVJq5bMdGbmnnvusc/Z8DJogkIAgYyAu1ztN7/5jTn55JPtmRwdoNBvW5et3XXXXeb+++/PjM8HBBBAoN4FSGzqvYZZPwRiAnrWzCmnnGKv1dcgJTQLFiww7lr+2Oh0IoCA5wJ64Odvf/tb+9K9N9OmTbPNSn/oQx8yhx12mLn77rvtQ0A9Xw3CQwABBFILkNikJmQGCIQhoGabldDopc8q8+fPN7fffnsYK0CUCCBQUEAHKPQ688wzzfTp0+0BjEMPPdQmN0pwaCa6ICEjIIBAwAI9Oq/N5Y7ggCuQ0BEoRkBNxr7nPe+xz8nQ+GoQYNasWcVMyjgIIBCwwIwZM2zjAloFPWdK9+eoqXYKAgggUI8CJDb1WKusEwJvC+hJ50pojjvuONvnxRdfNHPnzjU0DctXBIHGEVAT7meccYbZd9997Uo//PDDNsHp6OhoHATWFAEEGkLAJja+bty0U6bia3x6KrRKW1ubffftj+9+xJfuG1PITy2dnX766UYtn6lw2Vk6b6ZGIHQBd3ma1mPDhg3mzjvvtGdvk9ar0PYlaZpq9iO+dNq++7F/Vd/1W8nvH/fYpPvuMDUCXgqceOKJ5txzz7Wx6SzNnDlzzDPPPONlrASFAALVEdD9dE899ZTdNujszQUXXGD22Wcfe9CjOhGwFAQQQKCyAntUdvbMHQEEqi3w/ve/P5PUPPDAA+Y73/kOSU21K4HlIeCpgA5waJugbYOKLlH7yEc+4mm0hIUAAgiUJkBiU5oXYyPgtYCad1Vzryo333yzfXkdMMEhgEBNBKLbhxNOOMFceOGFNYmDhSKAAALdKUBi052azAuBGgn06dPHfP7zn7cP5tO18zNnzswcka1RSCwWAQQ8F9BZG20rtM3Q826++tWvmv79+3seNeEhgAACuQVIbHLbMASBIAQGDRpkrrjiCnPwwQfbZly/8Y1vmGeffTaI2AkSAQRqK6BthbYZagJ66NCh5mtf+5o54IADahsUS0cAAQTKFCCxKROOyRDwQWD48OHm8ssvN01NTeaRRx4x1157rQ9hEQMCCAQmoG2HtiF77rmnufTSS83hhx8e2BoQLgIIIGAMiQ3fAgQCFTj22GPNZz7zGRv9vffea37+858HuiaEjQACPghoG6Jticp5551ndO8NBQEEEAhJgMQmpNoiVgTeFtBDN8866yzbddddd5lbb70VGwQQQCC1gLYl2qaoqIVFkpvUpMwAAQSqKEBiU0VsFoVAdwiMHz/enH322XZWv/vd78xvf/vb7pgt80AAAQSsgLYp2raoqCnokSNH2s/8QQABBHwXILHxvYaID4GIgG7q/dSnPmX76KiqnhxOQQABBLpbQNsWd+bmS1/6klEjJRQEEEDAdwESG99riPgQeFtAzbB+8YtftF26Dp4zNXw1EECgkgLaxrh7btSggJqVpyCAAAI+C5DY+Fw7xIZAREBJjVosUstF3FMTgeEjAghUTEDbGm1z1PLipz/96YothxkjgAAC3SFAYtMdiswDgQoL6KngesaEnjVB62cVxmb2CCCQJaBtjrY9elbWhz70oaxhdCCAAAI+CZDY+FQbxIJAgoBu3tVTwfV0cJ5TkwBELwQQqLiAtj3aBk2ZMsW2llbxBbIABBBAoAwBEpsy0JgEgWoJTJ8+PdPc6i9+8YtqLZblIIAAAl0E3DbotNNOMyeeeGKX4fRAAAEEai1AYlPrGmD5COQQmDhxojnjjDPs0Jtvvtk8++yzOcakNwIIIFB5AW2DtC1SOffcc80xxxxT+YWyBAQQQKAEARKbErAYFYFqCbS0tGQu93jggQeMXhQEEECg1gLR7dHpp59utK2iIIAAAr4IkNj4UhPEgUBE4NRTTzWDBw82L774YuYIaWQwHxFAAIGaCeisjbZN2kZpW0VBAAEEfBEgsfGlJogDgbcFjjjiCHuDrjrnzJmDCwIIIOCdgNs2qTEBbbMoCCCAgA8CJDY+1AIxIPC2QM+ePc0pp5xiu+bPn2+eeeYZbBBAAAHvBLRt0jZKRdssbbsoCCCAQK0FSGxqXQMsH4GIgHYQRo0aZS/zuP322yND+IgAAgj4JaBtlC5J0zbLHZDxK0KiQQCBRhMgsWm0Gmd9vRWI7hzMnTvX2zgJDAEEEHACblvlDsq4/rwjgAACtRAgsamFOstEIEHAXc6xePFis2zZsoQx6IUAAgj4JaBtlbZZ0cto/YqQaBBAoJEESGwaqbZZV28Fojfgzpo1y9s4CQwBBBCIC7htVrThk/g4dCOAAALVECCxqYYyy0Agj0C0yVR3M26e0RmEAAIIeCfgtl2uqXrvAiQgBBBoCAESm4aoZlbSZ4GpU6fah9xt27bN0GCAzzVFbAggkEtA2y5tw/TATm3TKAgggEAtBEhsaqHOMhF4W2DAgAHmmGOOsV0LFizABQEEEAhWwG3DtE3Tto2CAAIIVFuAxKba4iwPgYjAhAkTTL9+/eyRznnz5kWG8BEBBBAIS0DbMJ210TZN2zYKAgggUG0BEptqi7M8BCIC7p//woULI335iAACCIQp4LZlbtsW5loQNQIIhCpAYhNqzRF38ALjxo0zI0eOtOtxzz33BL8+rAACCCDgtmXatmkbR0EAAQSqKUBiU01tloVARMAd0XzooYfMli1bIkP4iAACCIQpoG2ZtmkqbhsX5poQNQIIhChAYhNirRFz8AJDhw7NNBrgdgKCXylWAAEEEOgUcNs0NSKgbR0FAQQQqJYAiU21pFkOAhEB/cPv0aOHWbNmjXnuueciQ/iIAAIIhC2gbZq2bdrGuVYfw14jokcAgVAESGxCqSnirBuBnj17Zi7RWLx4cd2sFyuCAAIIOAG3bdPlaNrmURBAAIFqCJDYVEOZZSAQEdA/+iFDhtg+rgWhyGA+IoAAAsELuG2btnXcaxN8dbICCAQjQGITTFURaL0IuEszli9fbnbt2lUvq8V6IIAAAhkBbdu0jVNx27zMQD4ggAACFRIgsakQLLNFIEmgV69emSaely5dmjQK/RBAAIG6EHDbuFGjRhlt+ygIIIBApQXslqa5ubnSy0k1f1/j0xOWVXyNz6ETn5Mo7707/YYPH2769+9vA3EtB5UXFVMhgAACfgtoG3feeeeZfv36mcMOO8ysXr262wPuzu1ztwfXOUPiK0+V/avy3OJTNeL3jzM28W8B3QhUUGD//fe3c1+1alUFl8KsEUAAAT8E3LbObfv8iIooEECgXgXsGZuOjg4v189lmr7G19raat3a2trwK0PA9/qtRHzuO/P000+XIcYkCCCAQFgC2taNHj3aPs+mO/+XV2L73J2yxJdO0/2vZP+qPMdG/v5xxqa87wxTIVCWwIEHHminW7FiRVnTMxECCCAQkoDb1o0YMSKksIkVAQQCFSCxCbTiCDs8gYEDBxodhXrjjTd4KGd41UfECCBQhoAe1qlt3qBBg8zgwYPLmAOTIIAAAsULkNgUb8WYCKQSUMMBKvpHT0EAAQQaRcBt89Q6GgUBBBCopACJTSV1mTcCEQF3GdqaNWsiffmIAAII1LeA2+aR2NR3PbN2CPggQGLjQy0QQ0MIuMRm7dq1DbG+rCQCCCAgAbfNI7Hh+4AAApUWILGptDDzR6BTIPpgznXr1mGCAAIINIyA2+YdcMABPKizYWqdFUWgNgI8Crg27iy1wQTckUo9dGzTpk0Ntvb1s7q9e/c273vf++wKqS7vvvvuolbu2GOPNcOGDbPj6qGFvjZhWtTKMBICJQpom6ffS9++fY22hStXrixxDoyOAAIIFCdAYlOcE2MhkEqgqanJTr9+/fpU82Hi2groCerTp0+3Qbz11ltFJzannXaabRFPEyqpIbGpbT2y9OoLaNs3cuRI47aF1Y+AJSKAQCMIcClaI9Qy61hzAe0Qq7BDW/OqIAAEEKiBwIYNG+xS3bawBiGwSAQQaAABEpsGqGRWsfYC7igll6HVvi6IAAEEqi+wceNGu1C3Lax+BCwRAQQaQYDEphFqmXWsuYA7SvnKK6/UPBYCQAABBKot4LZ9bltY7eWzPAQQaAwB7rFpjHpmLWss4I5Stre31zgSFu+zwJAhQ8z+++9vn9Des2dPo6PcegbIyy+/XNGw1SjC6NGj7dPhBwwYYF5//XV72eTTTz9t3nzzzYou281cT6U/+OCDzd57723X+dlnnzXbt293g3kPXMBt+9y2MPDVIXwEEPBUgMTG04ohrPoScP/MOzo66mvFWJtuEdAO/cc+9jEzcODAxPnpEsaf/exnZvXq1ZnhamXtq1/9qu1+/vnnzdVXX50ZFv3wz//8z5mGC6644grjjpxrnD322MOcf/75ZuLEifZzdDp9VgMJixYtMjfddJPZtWtXfLDtvuaaa0yPHj0Sh0V7Kkn5yle+Eu1lP0+ZMsWce+65icvXPWnXXnstLQl2UQuvh9v2uW1heGtAxAggEIIAl6KFUEvEGLyAu/zCPYE7+BViBbpNYOjQoeZzn/tczqRGC1LCc9FFFxk9B8QVPRvJlT333NN97PKuszGu6CxQtMyYMcOoKWolOElF/SdPnmw+85nPJA22/YpJajRi0jI++9nPmg9/+MOJwzRNa2urufzyy82RRx6pTkrAAm7b57aFAa8KoSOAgMcC7/xn9DhIQkMgdAGOUoZeg13j1w79fvvt13VAQp98iccFF1yQ2bHXmZnrr7/ePqldO/WHH364OfXUU412BpUYnHLKKeanP/1pwhJK7zVixAgzYcKEzIR33HGHWbhwob30bMyYMfYszjHHHGOHKw59h7du3ZoZXx+iyYouWVu2bFnWcHUcddRRXfqph87UjB07NjNMO74PPPCAPTujM1jTpk0zSsq0jI9//OP27NSOHTsy4/MhTAG2hWHWG1EjEIoAiU0oNUWcQQtwlDLo6ksMXonNZZddljislJ7R5Oi6664zL7zwgp1cz/3Qa9WqVeaSSy6x/Q466KBSZp13XCUrrjz33HNm3rx5rtMmKEpSdM+PO0t02GGHmUcffTQzjj5Ezwa9+uqr5ic/+UnWcHXMnDmzSz+dOfrgBz+Y6a+Hlt54442Z7hUrVpgHH3zQ+mpHWMs588wzza9+9avMOHwIU4BtYZj1RtQIhCKQfP1BKNETJwKBCHCUMpCKqnKY2mHfvHmzUVKgRMIlNdEwNNyVaCLh+uk93xmh6HjRz0rMtFy9brvttuigzGc1IuBK0g5p9HK4nTt3ulELvuvSMhfzG2+8YW6++eYu0+heoFtvvTXTX5fEUcIXYFsYfh2yBgj4LMAZG59rh9jqRoB/5nVTlVkr8thjj2V15+o44ogjss5uuPF0adWVV17pOrPe+/TpY/bdd19z1llnZfV3HdFGANSSmJKeUi7VuvPOO41e8aKER/f06AzNIYccEh+c1R1NtEpJbNxZIM3s4Ycfto0UZM347Q41XKB7cLQceeiyNDVoQAlXgG1huHVH5AiEIEBiE0ItESMCCHgnoB3sYu93ibZMlrQiOhsydepUc+ihh9oml9UdvX8laRqdTdF9LbqsS+OqAYIf//jHxrU+pWmUoCghyFd0SdqkSZPMyJEjzV577ZU5k5JvGjesb9++7mNJTTMrYXNl3bp17mOXd7XEpvVxrcXp0riXXnqpy3j0QAABBBBAQAIkNnwPEKiCgG66jh7drsIiWUQgArqBX62T5UpklEDlGvbHP/7RnHTSSXZNDzzwQPPNb37TJhjbtm2zDQ7k+84pIbr44ouNGhEot/Tv3z8zabxhgcyAhA86w+SKLoXLV6Lz1TN2SGzyafk/LFqf/kdLhAggEJoAiU1oNUa8QQron3lLS0uQsRN05QT0UEq1+KXLv1R0hkLPpFnd+byaF1980baOpvf/+I//SAxCN9Or9TRdNuaKztAUOkujcZVMRZManRlRQwV/+ctf7L0+alBAz5fRM25yFSUarkTvx3H9cr0rmXGXo0WTnKTxo5cuRe83ShqXfv4LkNj4X0dEiEDIAiQ2IdcesQcjsGXLlmBiJdDqCejyM5fUvPzyyzaBie/4uZvsk6JSIvSDH/zA6GGd48ePN8OHD7fNMuteGyUBa9euta2PxRMdnQE6+uijM7OcPXu2+dOf/pTpdh9ynSlyw/UMHlcUf7FFydq4cePs6NFW4ZKmb25uzvQuZRmZifjglQDbQq+qg2AQqDsBEpu6q1JWyEeB+M6qjzESU/UFRo0alVnonDlzujwnRgP1TJdCRZdn5bpES8/BiSc27myJ5tvW1paY1GhY9IyOuuNFl7+5ku9eGTeOe4+2/qbWzn79618nNgrwrne9K3MJ5/bt2xPHcfPkPQwBtoVh1BNRIhCqAM09h1pzxB2UgDtKqRu0KQg4Ad3n4so+++zjPmbedRnW+eefn+nurg/RMzHR+2Si89e9O4MGDYr2yvqs+3dc0qX7gJ5++ums4fk6Hn/8caNmnlWUdJ1zzjldRtelm7oUzhW1kEYJV8Bt+9y2MNw1IXIEEPBZgDM2PtcOsdWNgDtKGb2spm5WjhUpW0D3sbhLsbRzr7MnK1eutDf+H3/88eZ973tfVitlaoVM97W89tprtkW0ches+3hcUQtsut/mpptussmGHgKq5Y4dO9aNYt91mZsui1MLZSeccIJtCtolZlqPUpph1qVyc+fOzSQ0J554onVQYwgbNmwwY8aMMTrT5C7D0/i5nrWTFSQd3gq4bZ/bFnobKIEhgEDQAiQ2QVcfwYci4P6Z04BAKDVWnTjnz59vlMDoDIp24r/whS90WbDObOg+HJ0h0fvXv/51c9999xk1HFBuURLyyCOPmGOPPdbOQg0EJDUSoJv83c39umTsuOOOMzfeeKN5z3vek7Xou+66K6u7mI6FCxfa+3yUSKmMHj3avuLT6j6iX/ziF5kzPPHhdIch4LZ9blsYRtREiQACoQlwKVpoNUa8QQq4yy+SLjcKcoUaNGjtZJdTomczop83bdpkb/7PtbOnVsqUyNxyyy22xbTuXPYNN9xgli5dmjhLJVNKYNR8tGLMV5RgPfXUUzlHyWWm/jNnzjS/+c1vcq6blv2tb33LLFmyJOf8GRCGgNv2uW1hGFETJQIIhCbQo/Mo3K7oA918WgF36trX+NTMqoouH/Gx+O7XSPHpSPd5551nb9JWC1QUBKICOmOjB2XqZn2dldm4caNZvnx51sM2dRmamodWQqBWxaIJUnRepX7WPPVgUL23t7cbNQKwYsWKrNmo9TPdC6MzOErCjjrqKKPLw5TQ6Kb+tEXrrEvydNZGl9rpUjkldbrkjlIfAhdccIFRYxBKmB9++OHUK9VI/z9SYyXMwHc/9q8SKq2EXr7XbyXj41K0Er4ojIpAuQLuKKXbWJc7H6arTwElKcuWLbOvXGuoh26W0vJYrvnE++uelvvvvz/eO6t7/fr1Wd1JTUNnjVBih5I1PT9HL0p9Crhtn9sW1udaslYIIFBrAS5Fq3UNsPyGEHCXGkWf+9EQK85KIoAAAp0CbtvntoWgIIAAApUQILGphCrzRCAmoCfJq+hyIrUqRUEAAQQaRUDbPG37VNy2sFHWnfVEAIHqCpDYVNebpTWowM6dO82aNWvs2kcfjtigHKw2Agg0kIDb5ulSSm0LKQgggEClBEhsKiXLfBGICehZHyp6HggFAQQQaBQBt83jbE2j1DjriUDtBEhsamfPkhtMwCU27gncDbb6rC4CCDSogNvmkdg06BeA1UagigIkNlXEZlGNLbB27VoLcOCBBzY2BGuPAAINJeC2eSQ2DVXtrCwCNREgsakJOwttRAE9bFDPPNIT5t0/+kZ0YJ0RQKBxBLSt0zZPz2ZS0+IUBBBAoJICJDaV1GXeCMQE3OVoY8aMiQ2hEwEEEKg/Abet00NXKQgggEClBewDOt0TQCu9sHLn72t8emCeiq/xOW/icxLlvXenn87YqIwdO9bMmzevvICYCgEEEAhEQNs6FT3ktTu3pW71KzFPN+/ueCe+8hTZvyrPLT5VI37/OGMT/xbQjUAFBdyT1UePHl3BpTBrBBBAwA8Bt61z2z4/oiIKBBCoVwF7xqajo8PL9XOZpq/xtba2Wjd3FN43RN/9GjG+FStWmNdff93079/fTJ482Tz00EO+fW2IBwEEEOgWAW3jVLZs2WKefPLJbn2GTSP+/+iWSnl7Jr77sX+VrrZ9r99KxscZm3TfHaZGoCSB6IM6x48fX9K0jIwAAgiEJOC2cWoNjQdzhlRzxIpAuAIkNuHWHZEHKvDoo4/ayI844gjTo0ePQNeCsBFAAIHcAtq2aRun4rZ5ucdmCAIIINA9AiQ23ePIXBAoWmDJkiXm5ZdftuOfdNJJRU/HiAgggEAoAm7bpm2dtnkUBBBAoBoCJDbVUGYZCEQE3nzzzcw/+okTJ0aG8BEBBBCoDwG3bVNSo20eBQEEEKiGAIlNNZRZBgIxAV2asWvXLjNy5Ege1hmzoRMBBMIW0EM5tW3TNo7L0MKuS6JHIDQBEpvQaox460JAz3Rw//Bdy0F1sWKsBAIINLyA26ZpG6dtHQUBBBColgCJTbWkWQ4CMQF33bl2Avr16xcbSicCCCAQnoC2ZS6xcdu48NaCiBFAIFQBEptQa464gxdYtmyZWbNmjV2Pk08+Ofj1YQUQQAABty3Ttk3bOAoCCCBQTQESm2pqsywEYgLuiKZrQSg2mE4EEEAgKAG3LXPbtqCCJ1gEEAhegMQm+CpkBUIW0D9/PZW7b9++5rTTTgt5VYgdAQQaXEDbMG3LtE0jsWnwLwOrj0CNBEhsagTPYhGQwObNmzONCEybNg0UBBBAIFgBtw1TowHatlEQQACBaguQ2FRbnOUhEBO49957TXt7uz3SeeaZZ8aG0okAAgj4L6Btl87WaFumbRoFAQQQqIUAiU0t1FkmAhGBDRs2mLvuusv2mT59emQIHxFAAIEwBNy2S9sybdMoCCCAQC0ESGxqoc4yEYgJ3H///Wb58uW274wZM2JD6UQAAQT8FXDbLG3DtC2jIIAAArUSILGplTzLRSAmcPfdd5s333zTTJw40YwbNy42lE4EEEDAPwFtq7TN0rZL2zAKAgggUEsBEpta6rNsBCICq1evzuwYnHHGGZEhfEQAAQT8FHDbKiU12oZREEAAgVoKkNjUUp9lIxATcDsH++67r6EhgRgOnQgg4JWAtlHaVkUPyngVIMEggEDDCZDYNFyVs8I+C0Qv59DNuIcccojP4RIbAgg0qIC2Ta7BAHcZbYNSsNoIIOCRAImNR5VBKAhIIHoD7rnnngsKAggg4J2A2zZFGz7xLkgCQgCBhhMgsWm4KmeFQxBwTabqMo+PfvSjIYRMjAgg0CAC2iZp2xRtqr5BVp3VRAABzwVIbDyvIMJrTAE95O7OO++0K3/CCScYvSgIIIBArQWi26M77rjDPpCz1jGxfAQQQMAJkNg4Cd4R8Exg8eLFZu7cuTYqHSE96KCDPIuQcBBAoJEEtA1yZ5DnzJljHn300UZafdYVAQQCECCxCaCSCLFxBebPn28eeOABC/Cxj32scSFYcwQQqLmA2wbNmzfP/OEPf6h5PASAAAIIxAVIbOIidCPgmcAvf/lL8+STT5rBgwebz372s55FRzgIINAIAtr2aBukxgLcZbKNsN6sIwIIhCVAYhNWfRFtgwr88Ic/NOvXrzdjx441f/M3f9OgCqw2AgjUQkDbHG17Vq5caW655ZZahMAyEUAAgaIESGyKYmIkBGov8P3vf9+88cYb5thjjzVnn3127QMiAgQQqHsBbWu0zdm6dau57rrr6n59WUEEEAhbgMQm7Poj+gYSeP31142SG5WpU6eaD3zgAw209qwqAghUW0DbGG1rVK666iqzfft2+5k/CCCAgK8CJDa+1gxxIZAgsG7dOvOTn/zEDjn11FPN+9///oSx6IUAAgikE9C2RdsYle9973tm48aN6WbI1AgggEAVBEhsqoDMIhDoToGlS5eaW2+91c7yfe97H2duuhOXeSGAgN2maNuiosZL1qxZgwoCCCAQhACJTRDVRJAIZAvcd9995rbbbrM9dVSVe26yfehCAIHyBLQtcWdq1PqZa26+vLkxFQIIIFBdARKb6nqzNAS6TeCRRx4xP/rRj+z8dB08raV1Gy0zQqAhBbQNcffU3HjjjSQ1DfktYKURCFuAxCbs+iP6BhdYu3at+cY3vmFbLFLLRTznpsG/EKw+AmUKaNuhbYhaXlRDAU888USZc2IyBBBAoHYCJDa1s2fJCHSLgG7qveKKK+wzJvSsicsvv9wcdNBB3TJvZoIAAvUtoG2FthnaduhZWVdeeaVRIyUUBBBAIEQBEpsQa42YEYgJqBnW//qv/7JPBdfTwS+66CJzwgknxMaiEwEEEHhHQNsIbSu0zXjyySfNt7/9baNm5SkIIIBAqAK9Qg2cuBFAoKuAngquHZPTTjvNfPSjHzXDhw83N998c9cR6YMAAg0toO2DO/ihBgLU+hkFAQQQCF2AxCb0GiR+BGICasnotddeM+eee67dcTnwwAPNnDlzzDPPPBMbk04EEGg0gUMOOcRuG/bdd1+76nPnzjXz589vNAbWFwEE6lSAxKZOK5bVamyBP/zhD/bMzemnn260A/P5z3/e7rzcfvvtjQ3D2iPQwAJnnnmmmT59uhXYsGGD0UGQxYsXN7AIq44AAvUm0KOzacdd9bZSrA8CCOwW2Guvvcy73/1u8653vcv2ePHFF42O0C5btgwiBBBoEIFx48aZM844wx7k0Cr/6U9/Mn/84x/tmd0GIWA1EUCgQQRIbBqkolnNxhbQ5SdKcPbff38LoaO0s2bNamwU1h6BBhCYMWOGmThxol3Tv/zlLzah4bLUBqh4VhGBBhXoMXny5F0dHR1ern5zc7ONy9f4WltbbXxtbW34lSHge/3WW3w9e/Y0p5xyin3ps4qurefytDK+vEyCgOcC0cvO3nzzTXP33Xfblz4XU+pt+1fMOnfnOPil02T/Kp1fI3//uMcm3XeHqREIRkA7NL/73e/MU089ZZObI444wl5vP2XKFLNgwQIzb968YNaFQBFAIFlALSJOmzbN9O3b146wfPlym9CsXr06eQL6IoAAAnUkQGJTR5XJqiBQjIB2cK677jqjhObUU081LS0tRo0MaGdo4cKF5p577jFbtmwpZlaMgwACHgj069fPnHzyyeakk07KJDTt7e3mrrvuss+28iBEQkAAAQSqIkBiUxVmFoKAfwL333+/PXvT2YCIOeaYY4x2jpTo6PXQQw/Z13PPPedf4ESEAAJWQE25d15Obl+ORAclHn30UXPvvfcatXxGQQABBBpJgMSmkWqbdUUgJqAdHz3UU9ffT5gwwb5GjhyZ2Vlas2aNbQ5WZ3J27aIBxRgfnQhUXaBHjx72zIwaBNBv1RX9VpcsWWJfmzdvdr15RwABBBpKgMSmoaqblUUgWUA7QjrCq5eahlWSo7M42nHS65xzzjG6Vn/p0qX2TE7yXOiLAAKVEtCZmfHjxxvdG+eKDjbo7IwSGppwdyq8I4BAIwuQ2DRy7bPuCCQIaAdJL12fr+RGSc6QIUPsDpV2qs477zyzatUq8/TTT5sVK1YYLldLQKQXAikFdJnZmDFjzNixY83o0aOz5vbyyy/bZEZJzfr167OG0YEAAgg0sgCJTSPXPuuOQB4B7TDpyeRKcNwZnFGjRtl7cbSjpZcaHXjjjTdscqNLYdauXWvWrVtnNm3alGfODEIAgajAwIEDzQEHHGCGDx9uz5Aqqdlzzz2jo5jXX3/d6DfmztAU22xz1kzoQAABBOpcgMSmziuY1UMgrYB2oPSkcr169epld7xGjBhh3GvQoEH2qLKOLLuybds2eyRZz3hSkvPKK68YtdKkZ1Jp54yCQKMJ6JJOPVtCrRDus88+Rr+bwYMHm6FDh2ZaMoua6Lejs6E6WOAOGOzcuTM6Cp8RQAABBGICJDYxEDoRQCC3gHasdBmaXq5o50xnctxLR571DA13f44bj3cEEMgtoIRfiYxLZjjrmduKIQgggEAuARKbXDL0RwCBogTUsppeOqOjorM6SnKGDRtmExwlQ2pKuqmpyb7cZ/eu/r179y5qWYyEQAgCO3bsMFu3brUvNb+sz+7dfdbvRGc2X3rpJaNnS3E2JoSaJUYEEPBdgMTG9xoiPgQCE9AO2sqVK41rclaX1PhYdFmQii6P87F0d3xXXPVDu5pXXHpht6xud8fXLUFFZuJ7fK2trTZaX38fEUo+IoAAAsEI7BFMpASKAAIIIIAAAggggAACCOQQILHJAUNvBBBAAAEEEEAAAQQQCEeAxCacuiJSBBBAAAEEEEAAAQQQyCFAYpMDht4IIIAAAggggAACCCAQjgCJTTh1RaQIIIAAAggggAACCCCQQ4DEJgcMvRFAAAEEEEAAAQQQQCAcARKbcOqKSBFAAAEEEEAAAQQQQCCHAIlNDhh6I4AAAggggAACCCCAQDgCJDbh1BWRIoAAAggggAACCCCAQA4BEpscMPRGAAEEEEAAAQQQQACBcARIbMKpKyJFAAEEEEAAAQQQQACBHAIkNjlg6I0AAggggAACCCCAAALhCJDYhFNXRIoAAggggAACCCCAAAI5BEhscsDQGwEEEEAAAQQQQAABBMIRILEJp66IFAEEEEAAAQQQQAABBHIIkNjkgKE3AggggAACCCCAAAIIhCNAYhNOXREpAggggAACCCCAAAII5BAgsckBQ28EEEAAAQQQQAABBBAIR4DEJpy6IlIEEEAAAQQQQAABBBDIIUBikwOG3ggggAACCCCAAAIIIBCOAIlNOHVFpAgggAACCCCAAAIIIJBDgMQmBwy9EUAAAQQQQAABBBBAIBwBEptw6opIEUAAAQQQQAABBBBAIIcAiU0OGHojgAACCCCAAAIIIIBAOAIkNuHUFZEigAACCCCAAAIIIIBADgESmxww9EYAAQQQQAABBBBAAIFwBHpMnjx5VzjhEikCCCCAQDkCl3ztKjvZ1VdeWs7kTIMAAggggID3Apyx8b6KCBABBBBAAAEEEEAAAQQKCfTSCB0dHYXGq8nw5uZmu1xf42ttbbXxtbW11cSn0EJ99yO+QjWYf7jvfvw+8tdfoaGVqt/u2p5WKr5CLsUO9z0+fh/F1mTyeL7XL/El11uxffl9FCuVPF4jf/84Y5P8naAvAggggAACCCCAAAIIBCRAYhNQZREqAggggAACCCCAAAIIJAuQ2CS70BcBBBBAAAEEEEAAAQQCEiCxCaiyCBUBBBBAAAEEEEAAAQSSBUhskl3oiwACCCCAAAIIIIAAAgEJkNgEVFmEigACCCCAAAIIIIAAAskCJDbJLvRFAAEEEEAAAQQQQACBgARIbAKqLEJFAAEEEEAAAQQQQACBZAESm2QX+iKAAAIIIIAAAggggEBAAiQ2AVUWoSKAAAIIIIAAAggggECyAIlNsgt9EUAAAQQQQAABBBBAICABEpuAKotQEUAAAQQQQAABBBBAIFmAxCbZhb4IIIAAAggggAACCCAQkACJTUCVRagIIIAAAggggAACCCCQLEBik+xCXwQQQAABBBBAAAEEEAhIgMQmoMoiVAQQQAABBBBAAAEEEEgWILFJdqEvAggggAACCCCAAAIIBCRAYhNQZREqAggggAACCCCAAAIIJAuQ2CS70BcBBBBAAAEEEEAAAQQCEiCxCaiyCBUBBBBAAAEEEEAAAQSSBUhskl3oiwACCCCAAAIIIIAAAgEJkNgEVFmEigACCCCAAAIIIIAAAskCJDbJLvRFAAEEEEAAAQQQQACBgARIbAKqLEJFAAEEEEAAAQQQQACBZAESm2QX+iKAAAIIIIAAAggggEBAAiQ2AVUWoSKAAAIIIIAAAggggECyAIlNsgt9EUAAAQQQQAABBBBAICABEpuAKotQEUAAAQQQQAABBBBAIFmAxCbZhb4IIIAAAggggAACCCAQkACJTUCVRagIIIAAAggggAACCCCQLEBik+xCXwQQQAABBBBAAAEEEAhIgMQmoMoiVAQQQAABBBBAAAEEEEgWILFJdqEvAggggAACCCCAAAIIBCRAYhNQZREqAggggAACCCCAAAIIJAuQ2CS70BcBBBBAAAEEEEAAAQQCEiCxCaiyCBUBBBBAAAEEEEAAAQSSBUhskl3oiwACCCCAAAIIIIAAAgEJ9Jg8efKugOIlVAQQQACBMgQu+dpVdqqrr7y0jKmZBAEEEEAAAf8FOGPjfx0RIQIIIIAAAggggAACCBQQ6KXhHR0dBUarzeDm5ma7YF/ja21ttfG1tbXVBqjAUn33I74CFVhgsO9+/D4KVGCBwZWq3+7anlYqvgIsRQ/2PT5+H0VXZeKIvtcv8SVWW9E9+X0UTZU4YiN//zhjk/iVoCcCCCCAAAIIIIAAAgiEJEBiE1JtESsCCCCAAAIIIIAAAggkCpDYJLLQEwEEEEAAAQQQQAABBEISILEJqbaIFQEEEEAAAQQQQAABBBIFSGwSWeiJAAIIIIAAAggggAACIQmQ2IRUW8SKAAIIIIAAAggggAACiQK2uefEIfREAAEEEAhWoHfLPmav4YeYpqEjTJ8Bg8wNS56263Lg2Rea7Zs3mq3rnzevrX3G7Gh/Jdh1JHAEEEAAAQSiAiQ2UQ0+I4AAAoELKKEZOO540zLq0Kw12bVrd2evpv5Gr/7DRpjBR00x7aufMpuWPUiCk6VFBwIIIIBAiAIkNiHWGjEjgAACCQLNo480wyZNN6ZHj4Shyb2UALWMHGvWL5pv2lc9njwSfRFAAAEEEAhAgMQmgEoiRAQQQKCQwN6HTzKtR08pNFry8M5EaOhxp5g9+jSZV59YlDwOfRFAAAEEEPBcgMYDPK8gwkMAAQQKCbR0nqlJSmq2bnix80zM783quT81500YY1/6rH4aFi+ah876UBBAAAEEEAhRgDM2IdYaMSOAAAJvC+iemqG6/CxWlLy0r/xzpu8eb1+epsYC9NKwloPHd5lWl7Jta1vHPTcZOT4ggAACCIQiwBmbUGqKOBFAAIEEATUUEL+nZt2CX2WSmmnTpplLLrnE7N1rq33ps/qptK9cajRuVulMgOw8s3rSgQACCCCAgP8CnLHxv46IEAEEEEgU0NmaeOtnagRg60urzaBBg8wnPvEJM3z48Kxp1a3XhAkTzPXXX282do6rsztDJ703M57mSUtpGQ4+IIAAAggEIsAZm0AqijARQACBuICeUxMtum9GZ2FUkpKa6LhKbjSOii5Li99zE5+3HZE/CCCAAAIIeCxAYuNx5RAaAgggkE9AD9+MlvZnn7CdutQsfqYmOp77rHEyl6W9Pa0bFp+36887AggggAACvgqQ2PhaM8SFAAIIFBDoM2BQ1hhbX37edusys2KLG9dN66aLz9v15x0BBBBAAAFfBUhsfK0Z4kIAAQQKCPTq2y9rjB2vbbbd++23X1b/fB1uXDetGzc+b9efdwQQQAABBHwVILHxtWaICwEEEEAAAQQQQAABBIoWILEpmooREUAAAb8Edm7bkhVQ770G2O4XXnghq3++Djeum9aNG5+36887AggggAACvgqQ2PhaM8SFAAIIFBDYvnlj1hhNQ3Y3JrBkyZKs/vk63LhuWjdufN6uP+8IIIAAAgj4KkBi42vNEBcCCCBQQGDr+uezxmg56HDbvWDBArN27dqsYUkdGkfjqrhp3Xjxebv+vCOAAAIIIOCrAImNrzVDXAgggEABgdfWPpM1RtPgfU3LwUfZfnr4Zr7kRsM0jkrLweONpo2W+Lyjw/iMAAIIIICAjwK9fAyKmBBAAAEECgvsaH/FtK9+yrSMOjQz8tBJ7zVq4WzjS6vN1VdfbZ9Toyad3XNtlNDo8jN3pqZp2CgzdNL0zPT6oHlq3hQEEEAAAQRCEiCxCam2iBUBBBCICWxa9qBpGTnWmB49MkMOmHaOWb9ovvny//tcpt+rO3d/bN53jDlJr9M/av7jB9d2JjXvzYxjP+zaZTRPCgIIIIAAAqEJcClaaDVGvAgggEBEQGdWXupMYuJFZ2Fu+dMys6Jts2nf/oZ5qzNh0Uuf1U/DuiQ1nTNRQsTZmrgm3QgggAACIQhwxiaEWiJGBBBAII9Ax6rHTc8+Tab16ClZY23fo7d5ZO1LWf0yHZ3D4qXtsftNe+e8KAgggAACCIQoQGITYq0RMwIIIBATePWJReat7Vt33y8TuSwtNlpyZ+eZHJ31UYJEQQABBBBAIFQBEptQa464EUAAgZiAzrZsbVtnBo47PqtBgdhoWZ1qKED31HD5WRYLHQgggAACAQqQ2ARYaYSMAAII5BJQgrL+gTtssrLX8EPM0LFHm97Ne5vtO3e3HtCnVy+zo+NVs/7px4yadCahySVJfwQQQACB0ARIbEKrsQaP94ILLkglMHv27FTTMzECPgsk/z62GvNW58s1FfNW5xr073wd09mSml6Rwu8jgsHHuhNI/n0Uv5r8Poq3YkwEaiXg/tXVavksFwEEEEAAAQQQQAABBBBILUBik5qQGSCAAAIIIIAAAggggECtBUhsal0DLB8BBBBAAAEEEEAAAQRSC3CPTWpCZlBLgcsuuyzv4r/73e/mHc5ABOpZgN9HPdcu65ZWgN9HWkGmR8A/Ac7Y+FcnRIQAAggggAACCCCAAAIlCnDGpkQwRg9b4Jprrgl7BYgegTwCixcvzjO08CB+H4WNGCNcgbS/j3DXnMgRaBwBm9g0Nzd7vca+xrdt2zbr5mt8rlKJz0nwjgACCCCAQHkC5fwvLWea8qIrbypf42P/qrz6jE/la/26OCsRH2dsnC7vDSFw0UUXNcR6spKNKTBjxoxUK87vIxUfE3sukPb34fnqER4CCHQK2MSmo6PDSwyXyfkaX2trq3Vra2vDrwwB3+u3jFViEgQQQACBOhUoZV/E9/9vvsfH/lW6H5Hv9VvJ+Gg8IN13h6kRQAABBBBAAAEEEEDAAwESGw8qgRAQQAABBBBAAAEEEEAgnQD32KTzY+oaC/CcmhpXAIv3WoDfh9fVQ3A1FuD3UeMKYPEIVECAMzYVQGWWCCCAAAIIIIAAAgggUF0BEpvqerM0BBBAAAEEEEAAAQQQqIAAiU0FUJklAggggAACCCCAAAIIVFeAe2yq683SUgrMnj07cQ7uiek8hyORh54NIjBr1qwGWVNWE4HSBQr9PmbOnGlnevHFF5c+c6ZAAAEvBDhj40U1EAQCCCCAAAIIIIAAAgikESCxSaPHtAgggAACCCCAAAIIIOCFAImNF9VAEAgggAACCCCAAAIIIJBGgMQmjR7TIoAAAggggAACCCCAgBcCJDZeVANBIIAAAggggAACCCCAQBoBEps0ekyLAAIIIIAAAggggAACXgiQ2HhRDQSBAAIIIIAAAggggAACaQRIbNLoMS0CCCCAAAIIIIAAAgh4IUBi40U1EAQCCCCAAAIIIIAAAgikESCxSaPHtAgggAACCCCAAAIIIOCFAImNF9VAEAgggAACCCCAAAIIIJBGgMQmjR7TIoAAAggggAACCCCAgBcCJDZeVANBIIAAAggggAACCCCAQBoBEps0ekyLAAIIIIAAAggggAACXgiQ2HhRDQSBAAIIIIAAAggggAACaQRIbNLoMS0CCCCAAAIIIIAAAgh4IUBi40U1EAQCCCCAAAIIIIAAAgikESCxSaPHtAgggAACCCCAAAIIIOCFAImNF9VAEAgggAACCCCAAAIIIJBGgMQmjR7TIoAAAggggAACCCCAgBcCJDZeVANBIIAAAggggAACCCCAQBoBEps0ekyLAAIIIIAAAggggAACXgiQ2HhRDQSBAAIIIIAAAggggAACaQRIbNLoMS0CCCCAAAIIIIAAAgh4IUBi40U1EAQCCMSrbW8AAEAASURBVCCAAAIIIIAAAgikEeiVZmKmRQCB7hXo06ePOemkk8yRRx5phgwZYvbcc0/To0cPs2XLFtPe3m6effZZ87vf/c50dHR074KZGwIIVFzgwgsvNM3Nzfb3+8Mf/jCzvN69e5vPfe5zplevXuall14yP//5zzPD+IAAAgggULwAiU3xVoyJQEUFpkyZYs455xzTs2fPLsvZa6+9jF777bef0XiPPPIIOz9dlOiBgN8Cw4YNMwMHDrRBnn766fYghX7vH/7wh82BBx5o+7/55pt+rwTRIYAAAh4LkNh4XDmE1jgC06ZNMx/84Ae7rPCOHTuMXn379jV77PHOlaPHHnuseeONN8wvf/nLLtPQAwEE/BTQ2dbzzz/fBnfaaaeZU0891X7WWVlX7rjjDveRdwQQQACBEgVIbEoEY3QEulugf//+5qyzzsrM9q233jJ33nmnuffee23y4gaMGTPGfOQjHzGtra2217vf/W5z3333mfXr17tReEcAAY8FHn74YXuAQgcxmpqa7GWmLlxdanrzzTebp59+2vXiHQEEEECgRAESmxLBGB2B7hbQkVt3xFZJzTe+8Q2zadOmLotZsWKF+fa3v22+9rWvmQEDBtjhhx9+OIlNFyl6IOCvwIMPPmj02nvvvY0uTdu5c6f9DXPfnL91RmQIIBCOgE1sdDOjz8XX+LZt22bZfI3P1SnxOQk/30eNGpUJTJeqJCU1bgRdf79o0SJzyimn2F6jR48299xzjxuc9a56HzlypBk8eLDp16+fefXVV+2NyWqAgFJfAqrjgw8+2O4sr1mzxjYysX379vpaSY/WRr/ZoUOHmn322cdeKrpx40azcuVK89prrxUdpX6PelH8E+jO/5ndOa9KSPkaH/tX3VPbvtavW7tKxMcZG6fLOwI1EtDOkSvLli1zH3O+b926NTPslVdeyXx2H1paWszf/u3fmmjC5IbpXTu8t912m7n//vujvTOfzz33XNsyW6ZHng+/+c1vzIIFC+wYxx13XOb+AV1yc8MNN3SZUq0/ff3rX7eJlgauWrXKfP/737c75V/4whe6jJ+vxwsvvGC++93v2nnpTJaKksIrr7wy32RFD1My6OabNJHOrv3lL38xDzzwgG3MQfdCFVu6a95qSEL1Fb3/ysXQ1tZmrr322ryJ8je/+U3bSpebJtf7hg0b7JlEDf/rv/5rc/zxx9tRn3vuOXPNNdckTqaGLi677DI7bNeuXeaSSy4xhW6Mj36H4jPVPHR245lnnjF/+MMfzJNPPmnUr9jy7//+77aVQcXw5S9/OXEyXeqpSzxVbrnlFruc6Ijvec97zAc+8AGj1guTijyuu+468/rrr2cN/uQnP2mOPvpo2+8nP/mJ+fOf/5w1XB3y+spXvpI5ezt37lxz99132/GiLrl+Wxrxe9/7nv0u6Deuebmis8DaLqho3ZPq4eSTTzZ/9Vd/5Sax33210KZSzPJ1Fln1rUtrVXQPoH6LpSR7dkL+IIAAAikEbGLj6ylwl8n5Gp+710E7ED4W3/18j69adfrf//3ftsUzLe/FF18suFjtzLqinbtoUeJw6aWXZi5Viw5zn7VTplaYtDO8cOFC1zvzriamiy1qntaV6Odc89BytVPvihsvOq0bVujdTRttRa6c+eRaTnS+SePIb/jw4eajH/2o3SHUJYLRpDNpGtevO+b92c9+1owdO9bNssu7tk+XX3650Y70448/3mW4ehTrpe+VKzpj6BIbJc8alpTURb+n+l4n7Uy7ebr3fPHock0tS5df6rV69Wq7I++mLfTu5p2UBLppo+sZ/azh2rlXq4X5ilo2++pXv2qTwOgZM/dd1bQujvh8Pv3pT2eSGg2LLj86TXRe8Xm4dYuOH5+XxonXhZKe6H1+miY6j+jnpOUPGjTIJjUu4VNSc/XVVweb1HTHPofv/998j4/9q/ivu7Ru3+u3kvG9s1dSmhljI4BANwm4o6L5ZqediREjRtij8665WJ0x0NHraFErS+7+G+1s6nkYy5cvt62qaWdw6tSp9siwptG9PUmJTXSHat26dUaX2USLdmbdMqL9C31W/No5TCo6OBA/iq1laFkq2lGKJ3Fr1661w6rxR5Zq0MEV7Rzuu+++5phjjrE7o9qhU4Jz/fXXu1GKfi9n3koaokmNLj/TmSOdsdIlaWplT/WoOD/+8Y/bne2k5MPtCCvYuL9a4osuw62QLmVUAudufpeBziLEy1FHHZXplfQ9ywzM8UHfCd2L4orWR+t2yCGH2F76brzrXe8yf/rTn9woFX3XmSpXdGZ1zpw59lIyxaHf1vTp0+13Qf+wx48fb8/iufELveu36H7XhcatxHCd4Y1+F0pZhi7J08EUl/AoodOZ1Ph2o5R5Mi4CCCBQrgCJTblyTIdABQW0A5fv0ixd3vHjH/+4y5Hy6I7oXXfdZR577DEbpXZqtfOpnUBdkqMzBnoujnbIo0eWNXL0bMJvf/tb88QTT2St6Yc+9CFz4oknZvUr1KGj7X/3d3+XczTtBOnMQrTo/iB3yZB2cuPDo+NW+rN25H//+993WYx2cJU4qLjnkHQZqUCPUuet+ok2Df7QQw+ZG2+8MbMUNTKhhECXBSn5UEJw5plnml/96leZcdwHtzOrxDHuq5vbc13Wp++R+w5Mnjy5S2KjnV19v1SUgOssT6lFSXWSubyVTKkceuihVUlsdJmYs9JvSb89rZeKEj1335q7902Jjp41VUxRQvP+97+/mFErMo6SQyVn5RS56DfqDoZou6SkRi28URBAAIFaCLzzYIxaLJ1lIoBAokA0uUgaQTsSSUd4tZOsG5KVCCQ1KqBLUHSfgivu0hHXrffoZSfRcaPjlPpZ9yW4szxbtmwpdXJvx4/uwLmW7bor2FzzPvLIIzNHx5WQqIngeNG9V7feemumt5KPpOLqOp7cJo0b7aemyF1RQhf/vrr7VDTOU0891eXSJzdtOe/Ry4TKMdc0Lkkpdvk6C+Nu9lcDHy6piU4fjUtnu4otSvjdelT7t6E4o2eiSvke6MDD3//932eSms2bN5tvfetbJDXFVjzjIYBARQQ4Y1MRVmaKQDoBncGIXnqlyzzU8pVLDpSQXHDBBfaMyx//+MfMwv7nf/4n8zn6QTueumZZO7hJyUx0XHf0Vf1K2dGJziP6WXHrMh0V7RBqR1w3U1ei6MZlnVFyRTeXaydfN/nrsr2kHVI3bqnvavRBN+67Ej+z5fqX855v3gcccEBmljoLl2uddJZE9zSpPlXn2pmPjqv+boc6frN7ZgE5PqgxAV32puRa89CN8YsXL86MPWHChMznaBKU6VnmB637CSeckJk6usxMzxwf1MqSu7/roIMOsq2Y5Ri1S289W0b3UCUVJT3ayXff8aRxcvXT73H//fe3g2Wqy0bVQEG+onpzN+jnG6+YYdqGuN+7GhPRZX4621ao6Izy5z73uUyCqAMpOhPcHduLQstmOAIIIJBPgMQmnw7DEKiRgHZyfvCDH3RZupIT3TTuztacffbZRpciRW8G1g6sdv50nb+ek6FLguJH1LvMONIjmvi4Jjcjg0v++JnPfCazA63LoSp5VFrr6S6Rigeqs1m656iYlufi0+rmarUs5YqMok46mv/rX//aDS7pvdR5694eV3S5Vq6ipE5nEdx3ZciQIba5bze+669uHW0vtehyN52JU1FjAi7JUCLrWuDSjm65D5zUPTpRc32Po2dalixZ0uUyyXzroJ1vJSAqSqx1ECDqp7MXugk+X9HvSd8v7djrIIOmcclhvumShinJUitsrvzoRz/KtMjm+iW9jxs3zvzrv/5r0qCS+umyVc1LRb9J/TZdK3b5ZqQzdEpk3Xrr8jO1HhjdBuWbnmEIIIBAJQVIbCqpy7wR6GYB7ZypSVc1mawdCx1tPeywwzI769p5vfjii1Md0Y1eRlPqkfz46uroszsCrEYS1EzvmDFj4qN1a3d0Byua0Ol+E132853vfCdrB7/Yhbud9aTxleRoPXUTfzmllHnr3hdXCj0HRcmcK9oRjzZUEb2vIqnZcDddrncd4XeJjZ6n5M4IlXtGJb4czS+fi5IQnS2JXgIWn0e0W5eQXXjhhbaXkiRdRqVL+WSk73w0UY1O5z6rRbRCZ1PcuMW8f+ITn8gccNBZrfXr1xczWbeMo99F9KzpT3/606ITk+j3T8HIUklSd56x7JaVZCYIINCQAiQ2DVnttVnp3i37mL2GH2Kaho4wfQYMMr367m72d/C2LWb75o1m6/rnzWtrnzE72rs+m6U2EVd+qdqZcjsKOvJZTCKhey9efvnlTMKgI/g6C6EdQTU4EL1MRTtLeraGmtvVc1/0WU0Au0vaktbQXa6jYWnOrmin0z0XQ2cP9HyPShfZaP2iRUfXtROpeJQM6p6CXM9eiU4X/awbxqM3suveFF0uph067dgpadJN1P/wD/9QdJPPbv6lzlvJjLsczX133Lzi74rLlfhZmWhiEz1z4cYv9K7vhlqmU5PX+u7p3h+1rKab0V1xzzhy3aW863LM6A347oyKbszXjrla2VNy8i//8i9FzVY73nfccYe9Ud+dbdAlnq41r3wzmTRpUlZSo4RIv6Xnn3/e/raU0MpB37Niis6UuIY+9LvX86CKLar/eAt2btpiEy9doui+G0uXLjVqcKKUot+zDFwyqKaq1WhANHEuZX71MC7/3+qhFlmHehAgsamHWvR8HbTBHzjueNMy6tDESHs19Td69R82wgw+aoppX/2U2bTswYZIcE466SRzxhlnWBftJF511VWJRvGe7qZv9Xc7aToT4o5wa2dZ80ra0dBOaL6iHXUVzSN6T0a+aZKG6YiwW5YeNKjL62pR9ET4//zP/zT/+I//aBevy4lKLTqqH23uOTq9Ehp3idOxxx6b2IR2dPz451LnrSTVXUKkVqnyFSVzrigZjhYXs/ppJ72cojNw559/vp1UZ2q0o++SZt2DozOM5RbNK8lcO9O6RE3vWpYuqdOyiinz5s2zzWLrMjedZXIHAZQQ67eiyzejLm6eakbdFd3XdNNNN3X5bUQTRTdu0rvOsqppcFeiLay5fvneVVdJLdxpmmISG93T455DpN+4Ls8spSihmTlzpj0I80//9E/2rLF+5/od6EyyErVGKvx/a6TaZl1DEMi/hxPCGhCj1wLNo480oz7wiZxJTVLwSoDsNJ3T1nuJPjhROxwuqci33jpyHb0/wt0zoh01V3TfTVJSo52qfMvQWQiXjMSP8Lt5F/Oum5BdPJqPjpTXskSTKneUubvi0b0errhnrLjutO9J89aZN1d087mrL9fPvevMibsxXPe6RJNU9XdJkY6+R+fppi/mXc0+u/kqsY62hqbn6lSiaF2U9LhSqrkuXdNldD/72c/Mtddea1/auZ8/f37Oy8Gi994kJTWKxX3fXVy53nUgw30HH3300UxT0bnG7+7+0WbXb7jhhpJv+FcDBzrDp8sXdcBA3x8VrdMll1ySubyuu+P2cX78f/OxVoip0QVIbBr9G1DB9d/78Elm2HGn6JRC6UvpnGZo57SaRz0XJR86aqqiHdQvfelLmZabktZbO6S6/MadpdH9JG6nNLqDqwQlXjTNpz71qcy08eHqju6YRpOupHHz9YsmT//7v/+b2fnJN00lh0XXq9B9KaXG4c6eaDqdTenOkjRv1YuOmqtoZ1L3fsSLztxFW2yLP0cmevO3vj8uOYnPp1C3vn9qzllF3z/3HBd1K3moRNFvQJehuaIW7ypd3O9Ny3FneaLL1KWB7ixItH/SZ3cWTQla9PlDSeNWop9bvs78KLFKU3QJ3qxZszKz0AEXtZbWCIX/b41Qy6xjiAJcihZirQUQc0vn2ZbWo6d0iXTrhhdN+7NPmK0vP2/6mt0PuNtm9jBNQ0aYloMON02D32nxSRNrHm9u32o6Vj1u56XmSZOKa5FpxowZSYMz/aL/hDM9a/xBD9J0N2GrRSm1eKRr6FevXm0v5dFOlXYYdERc9xdEE5joJSm65Mo1OasdYj2dXkfNNb0usTnrrLOyzvRotXXZjXZMp06dau9B0aVxrnTHEXedOYoeXXfzrtS7zmZF10H3C+lIuuxc0T0FpZb4fLVzrcugdF9J9OxZNeatRHju3LmZhEatdOnsi5r91pkprasunXL3jmj82267za6yEhp9F3TfkSva0U363WidXVGiqnF034se2hot9913n/1eRvtphzfacEF0WLGftU7RulQ8OnOipqTdGQ8leC6xL3a+5Yyn9dZvU+WLX/yibVFNl7+p37Rp02wrhNHkR4146B6WfAazZ8/OJKjlxJRmGiWy8Qeyljs/JUdaX/eQUf3edA/P//3f/5U7y4pNl/Q9jy7M/R/J9X9GdaZSqf9v0Vj4jAAC5QmQ2JTnxlR5BHTN8dBJ07uMsX7R7037yj9n+vd9+/r/HR2b7f00GtZy8Pgu0w7rnNe2tnV1e8+NEhsdYXfNFGsHSTugeuUruv8gelRczeq6Z4toOu1c6BUvugbenVHRZSlqYEBHoV0/ja8zSfF7MuLzKdStI9K//OUvC43WrcO1Mx89UxGfuXxKuVHbTV9ovhpP9xGVcxN+OfNeuHCh/X7oeSwq2plMuhRKlwn94he/yOxA6zsVfcaMptV3b+LEifqYs+hmfY2jHfV4YqPEWAmGS6Q0EyU7aYtamctXl5q/GqQo92xTKfEpkXQNA2gnXs+0kW00mdGZQNeYgxIetb6nS7V0wCFe9EylNGdE4/MrtVuJbvQBsKVOHx9fLc7p3jX33dJBFZ29jG6f4tOE2s3/t1BrjrgbRYDEplFquorrqYYCOv/jZy1x3YJfma0vrbb9dIRT/wDdNf464qp7CdSCUvvKpWbHa+3mgGmRy2t0xqJznusfqO19Glkr1M0dt9xyi72BW2dcnEuuRaxatcrunMebFtYOnm7q1XNuXBPL0Xlox0tHaZVwXHrppZn7L6Lj6LN2eNSkdK4S3ZGMfo42s6xpczUhu3Pnzsyso9Nner79wV27r85848WXG5+PptV9FToDpSQy37yi0xaar+LTjr4SQ91DVErLUmnnrWWrrvVb0pm46A62WwclcbqHJM0N/G5ehd71+z3uuOPsaFq36L1BhaaNDi/kouFqOVCXUWnnPHrvVHQ+5X6OfjeisWh9dGZO98e4M6bOXHWh79acOXPMxz72sawk0Y0bnZeWod9GUokuP/o5On30c3weikVxRafVONFufR/uueee+KS2W9O7Ep0muszoZzeu3nXPkpI+98BRHVR59tlnq3JGLRpHpT/z/63SwswfgXQCPTpvPt1V7HMA0i2q9KndtcC+xqeHJapUY8ehdD1jLy3SdNX009GsUWd8Mivc9Yvm24RFl5HoqKeaRU0qahXs+uuvt5e7tBx8VOeZm/dmjbZ67k/NX39wdwtiWQNK6PDxUrR4+Lo/RpeIyUuftYOhG3W1Ay2jYupTD9HTEXxdiqVpdYQ42piAjsAr+dFOkHYO9V3WfQK6bKwal/bE15nu8gVUh0qGVd+6PE51qOQ3qXUq/f7cUfV/+7d/M8Xen6KnyuuMjBI5NWkdL0rIzzzzTNv7sccey7njHp8utG79no444gijJtb18Fqd1dTN9O4+Oa2P6kAvNzy0dazneAtdilZo3W/6zdyK/n+r5qMO2L8qVNv5h/vu18jxccYm/3eXoSUK6Dk10WLvqek8C6OSL6nRcPcciKuvvtpesha/5yY+b01T6EnZerZCaEWJiF5pio5o65Wr6KhrNIHRJVTlXEaVa/70r56AjrIrQSk2SalEZNHGGXRJXr0WPbsn+mydpPVUK4BpWhRMmif9KiNQ6v+P+P+g7v7/9sryRZVZUeaKQAMJ0CpaA1V2NVZVD9+MFjUUoKJLZnKdqYmOr3E0roqb1g2Pz9v15x0BBGonoGfCuAYUdLkjCXLt6oIlV1Yg/j/I/Y/i/1tl3Zk7AqUIcMamFC3GLSjQZ8CgrHHU+pmKu/wla2CODo2r+23ctG603fPe4jp5RwCBEgWefPJJ41o7K+XmcbV8pcur3D0tuuxND+bU/VLR+7luv/32EiNidATCEaj8/7dwLIgUAV8FSGx8rZlA4+rVt19W5Dte22y7C90QH53IjeumdcN2z5vExnnwjkCpAg8//LDRq9QSf96Krt92zR+7eekeLj2wk4JAvQpU/v9bvcqxXghUT4DEpnrWLAkBBBCoCwHda6L7tNTql26cV+MUahWLggACCCCAQC0FSGxqqV+Hy965bYvp1dQ/s2a99xpgnz+jG9WLucdGE7qb2jVttGjeZq9oHz4jgEAtBNS09Ze//OVaLJplIlAzgYr/f6vZmrFgBOpHgMYD6qcuvViT7Zs3ZsXRNGR3YwKlPNfCjeumdTOMz9v15x0BBBBAAIFKC8T/B7n/Ue5/VjHLd+O6ad008Xm7/rwjgEBpAiQ2pXkxdgGBret3NxbgRlOTzSpqDEDPYClUNI7GVXHTumni83b9eUcAAQQQQKDSAvH/Qe5/FP/fKi3P/BEoXoBL0Yq3YswiBF5b+4wZfNSUzJhNg/c1ethm+8o/24dv5nuWjXtApyZuOXi80bTRonmbY8ZGe5kQn1OTtQJ0IIAAAgjURKDU/x8V//9WEwUWikB9CZDY1Fd91nxt9OTk9tVPmZZRh2ZiGTrpvUYtnG18abXRwzfV5r+adHatn+meGp2ed2dqmoaNMkMnTc9Mrw+aZzWfypy1cDoQQAABBBpegP9vDf8VACAAARKbACoptBA3LXvQtIzsPLPSo0cm9AOmnWPWL5rfeeZmqU1glMSoyViVjo6OzHg6u6NEKKt0Plld86QggAACCCBQSwH+v9VSn2UjUFiAxKawEWOUKKCjWi91JjHDjjsla0qdhWk56AijpzXvfvjmW3Z475Z9jG6k1PXK8cvPNIISIs7WZFHSgQACCCBQAwH+v9UAnUUiUIIAiU0JWIxavEDHqsdNzz5NpvXod+630dRKXJKSl1xzbnvsftPeOS9XZs+e7T5mvV9zzTW2+6KLLsrqTwcCCCCAAAISmDVrVl6ImTNn2uEXX3xx3vEq9f8t70IZiAACRQmQ2BTFxEjlCLz6xCLz1vatu++XiVyWVtS8Oi8/01kf/QOhIIAAAggg4JMA/998qg1iQeAdARKbdyz4VAEBnW3Z2rbODBx3fFaDAvkWpYYCdB0zl5/lU2IYAggggEAtBfj/Vkt9lo1AsgCJTbILfbtRQAnK+gfusMnKXsMPMU1DR5g+AwaZXn372aXoac56OJmeEaDmNElouhGfWSGAAAIIVEyA/28Vo2XGCJQlQGJTFhsTlSOgfwCvLF9kX5o+qVW0cubLNAgggAACCNRSgP9vtdRn2Qi8I7DHOx/5hAACCCCAAAIIIIAAAgiEKUBiE2a9ETUCCCCAAAIIIIAAAghEBEhsIhh8RAABBBBAAAEEEEAAgTAFSGzCrDeiRgABBBBAAAEEEEAAgYgAiU0Eg48IIIAAAggggAACCCAQpgCJTZj1RtQIIIAAAggggAACCCAQEejVt29fo5fPxff4Wltbfeajfr2uHYJDAAEEEPBJoDv/p/u+/+J7fN1ZF5X4jvnu14jxccamEt905okAAggggAACCCCAAAJVFei1bds209HRUdWFFrsw3x/g6I4ktLW1FbtKVR3Pdz/f46tqZbEwBBBAAAEvBLrjf7rv/998j4/9q3Q/Bd/rt5LxccYm3XeHqRFAAAEEEEAAAQQQQMADARIbDyqBEBBAAAEEEEAAAQQQQCCdAIlNOj+mRgABBBBAAAEEEEAAAQ8ESGw8qARCQAABBBBAAAEEEEAAgXQCJDbp/JgaAQQQQAABBBBAAAEEPBAgsfGgEggBAQQQQAABBBBAAAEE0gmQ2KTzY2oEEEAAAQQQQAABBBDwQIDExoNKIAQEEEAAAQQQQAABBBBIJ0Bik86PqRFAAAEEEEAAAQQQQMADARIbDyqBEBBAAAEEEEAAAQQQQCCdAIlNOj+mRgABBBBAAAEEEEAAAQ8ESGw8qARCQAABBBBAAAEEEEAAgXQCJDbp/JgaAQQQQAABBBBAAAEEPBAgsfGgEggBAQQQQAABBBBAAAEE0gmQ2KTzY2oEEEAAAQQQQAABBBDwQIDExoNKIAQEEEAAAQQQQAABBBBIJ0Bik86PqRFAAAEEEEAAAQQQQMADARIbDyqBEBBAAAEEEEAAAQQQQCCdAIlNOj+mRgABBBBAAAEEEEAAAQ8ESGw8qARCQAABBBBAAAEEEEAAgXQCJDbp/JgaAQQQQAABBBBAAAEEPBAgsfGgEggBAQQQQAABBBBAAAEE0gmQ2KTzY2oEEEAAAQQQQAABBBDwQIDExoNKIAQEEEAAAQQQQAABBBBIJ0Bik86PqRFAAAEEEEAAAQQQQMADARIbDyqBEBBAAAEEEEAAAQQQQCCdAIlNOj+mRgABBBBAAAEEEEAAAQ8ESGw8qARCQAABBBBAAAEEEEAAgXQCJDbp/JgaAQQQQAABBBBAAAEEPBAgsfGgEggBAQQQQAABBBBAAAEE0gn0Sjc5UyNQXYELLrggcYGLFy+2/WfMmJE43PWcNWuW+8g7AnUnUOj7X2iF+X0UEmJ4yAKFfh/u/0iu/zOzZ88OefWJHYGGEOCMTUNUMyuJAAIIIIAAAggggEB9C5DY1Hf9snYIIIAAAggggAACCDSEAIlNQ1QzK4kAAggggAACCCCAQH0LcI9Nfddv3a/dZZddlncdv/vd7+YdzkAE6lmA30c91y7rllaA30daQaZHwD8Bztj4VydEhAACCCCAAAIIIIAAAiUKkNiUCMboCCCAAAIIIIAAAggg4J+AvRStubnZv8giEfka37Zt22yUvsbnCInPSfCOAAIIIIBAeQLl/C8tZ5ryoitvKl/jY/+qvPqMT+Vr/bo4KxEf99g4Xd4bQmDmzJkNsZ6sZGMKuOdwlLv2/D7KlWO6EATS/j5CWEdiRKDRBWxi09HR4aWDy+R8ja+1tdW6tbW14VeGgO/1W8YqMQkCCCCAQJ0KlLIv4vv/N9/jY/8q3Y/I9/qtZHycsUn33WHqwAQuvvjioiOu5A+v6CDyjOh7fPxjylN5RQwqp35zPTG9iMXZUfh9FCuVfjx+H+kMa/H7SBcxUyOAQDUEaDygGsosAwEEEEAAAQQQQAABBCoqwBmbivIy80oL8JyaSgsz/5AF+H2EXHvEXmkBfh+VFmb+CFRfgDM21TdniQgggAACCCCAAAIIINDNAiQ23QzK7BBAAAEEEEAAAQQQQKD6AiQ21TdniQgggAACCCCAAAIIINDNAtxj082gzK6yArNnz67sApg7AgELRH8fvVv2MXsNP8Q0DR1h+gwYZHr362/XbMeW1832zRvN1vXPm9fWPmN2tL8S8BoTOgLFC0R/H8VPxZgIIBCSAIlNSLVFrAgggEABASU0A8cdb1pGHZo15q5duzt7NfU3evUfNsIMPmqKaV/9lNm07EESnCwtOhBAAAEEQhQgsQmx1ogZAQQQSBBoHn2kGTZpujE9eiQMTe6lBKhl5FizftF8077q8eSR6IsAAggggEAAAiQ2AVQSISKAAAKFBPY+fJJpPXpKodGSh3cmQkOPO8Xs0afJvPrEouRx6IsAAggggIDnAjQe4HkFER4CCCBQSKCl80xNUlKzdcOLnWdifm9Wz/2pOW/CGPvSZ/XTsHjRPHTWh4IAAggggECIApyxCbHWiBkBBBB4W0D31AzV5WexouSlfeWfM333ePvyNDUWoJeGtRw8vsu0upRtW9s67rnJyPEBAQQQQCAUAc7YhFJTxIkAAggkCKihgPg9NesW/CqT1EybNs1ccsklZu9eW+1Ln9VPpX3lUqNxs0pnAmTnmdWTDgQQQAABBPwX4IyN/3VEhAgggECigM7WxFs/UyMAW19abQYNGmQ+8YlPmOHDh2dNq269JkyYYK6//nqzsXNcnd0ZOum9mfE0T1pKy3DwAQEEEEAgEAHO2ARSUYSJAAIIxAX0nJpo0X0zOgujkpTURMdVcqNxVHRZWvyem/i87Yj8QQABBBBAwGMBEhuPK4fQEEAAgXwCevhmtLQ/+4Tt1KVm8TM10fHcZ42TuSzt7WndsPi8XX/eEUAAAQQQ8FWAxMbXmiEuBBBAoIBAnwGDssbY+vLztluXmRVb3LhuWjddfN6uP+8IIIAAAgj4KkBi42vNEBcCCCBQQKBX335ZY+x4bbPt3m+//bL65+tw47pp3bjxebv+vCOAAAIIIOCrAImNrzVDXAgggAACCCCAAAIIIFC0AIlN0VSMiAACCPglsHPblqyAeu81wHa/8MILWf3zdbhx3bRu3Pi8XX/eEUAAAQQQ8FWAxMbXmiEuBBBAoIDA9s0bs8ZoGrK7MYElS5Zk9c/X4cZ107px4/N2/XlHAAEEEEDAVwESG19rhrgQQACBAgJb1z+fNUbLQYfb7gULFpi1a9dmDUvq0DgaV8VN68aLz9v15x0BBBBAAAFfBUhsfK0Z4kIAAQQKCLy29pmsMZoG72taDj7K9tPDN/MlNxqmcVRaDh5vNG20xOcdHcZnBBBAAAEEfBTo5WNQxIQAAgggUFhgR/srpn31U6Zl1KGZkYdOeq9RC2cbX1ptrr76avucGjXp7J5ro4RGl5+5MzVNw0aZoZOmZ6bXB81T86YggAACCCAQkgBnbEKqLWJFAAEEYgKblj1ozK5dWX0PmHaOPQujnkpglOC8urPJvvTZJTU6u6Nxs0rnvOw8s3rSgQACCCCAgP8CnLHxv46IEAEEEMgpoDMrLy2ab4Ydd0rWODoL03LQEab92SeMHr751tvJT++WfYwaCtA9NfHLzzSD9Z3z4mxNFiUdCCCAAAKBCJDYBFJRhIkAAgjkEuhY9bjp2afJtB49JWsUJS4ueblxyQo7bNQZn8waJ9rR9tj9pr1zXhQEEEAAAQRCFCCxCbHWiBkBBBCICbz6xCLz1vatu++X6dEjNrRAZ+fZHJ31UYJEQQABBBBAIFQBEptQa464EUAAgZiAzrZsbVtnBo47PqtBgdhoWZ1qKED31HD5WRYLHQgggAACAQqQ2ARYaYSMAAII5BJQgrL+gTtssrLX8ENM09ARps+AQaZ3v/52kh1bXjd6+KaeU6MmnUlocknSHwEEEEAgNAESm9BqjHgRQACBIgSUsLyyfJF9afQrrvqhneqKSy8sYmpGQQABBBBAIDwBmnsOr86IGAEEEEAAAQQQQAABBGICJDYxEDoRQAABBBBAAAEEEEAgPAESm/DqjIgRQAABBBBAAAEEEEAgJkBiEwOhEwEEEEAAAQQQQAABBMITILEJr86IGAEEEEAAAQQQQAABBGICJDYxEDoRQAABBBBAAAEEEEAgPIEekydP3hVe2ESMAAIIIFCKwCVfu8qOfvWVl5YyGeMigAACCCAQjABnbIKpKgJFAAEEEEAAAQQQQACBXAL2AZ0dHR25hte0f3Nzs12+r/G1trba+Nra2mrqlGvhvvsRX66aK66/7378Poqrx1xjVap+u2t7Wqn4cnmU2t/3+Ph9lFqj2eP7Xr/El11fpXbx+yhVLHv8Rv7+ccYm+7tAFwIIIIAAAggggAACCAQoQGITYKURMgIIIIAAAggggAACCGQLkNhke9CFAAIIIIAAAggggAACAQqQ2ARYaYSMAAIIIIAAAggggAAC2QIkNtkedCGAAAIIIIAAAggggECAAiQ2AVYaISOAAAIIIIAAAggggEC2AIlNtgddCCCAAAIIIIAAAgggEKAAiU2AlUbICCCAAAIIIIAAAgggkC1AYpPtQRcCCCCAAAIIIIAAAggEKEBiE2ClETICCCCAAAIIIIAAAghkC5DYZHvQhQACCCCAAAIIIIAAAgEKkNgEWGmEjAACCCCAAAIIIIAAAtkCJDbZHnQhgAACCCCAAAIIIIBAgAIkNgFWGiEjgAACCCCAAAIIIIBAtgCJTbYHXQgggAACCCCAAAIIIBCgAIlNgJVGyAgggAACCCCAAAIIIJAtQGKT7UEXAggggAACCCCAAAIIBChAYhNgpREyAggggAACCCCAAAIIZAuQ2GR70IUAAggggAACCCCAAAIBCpDYBFhphIwAAggggAACCCCAAALZAiQ22R50IYAAAggggAACCCCAQIACJDYBVhohI4AAAggggAACCCCAQLYAiU22B10IIIAAAggggAACCCAQoACJTYCVRsgIIIAAAggggAACCCCQLUBik+1BFwIIIIAAAggggAACCAQoQGITYKURMgIIIIAAAggggAACCGQLkNhke9CFAAIIIIAAAggggAACAQqQ2ARYaYSMAAIIIIAAAggggAAC2QIkNtkedCGAAAIIIIAAAggggECAAiQ2AVYaISOAAAIIIIAAAggggEC2AIlNtgddCCCAAAIIIIAAAgggEKAAiU2AlUbICCCAAAIIIIAAAgggkC1AYpPtQRcCCCCAAAIIIIAAAggEKEBiE2ClETICCCCAAAIIIIAAAghkC5DYZHvQhQACCCCAAAL/n727gJflqPMF3oEAQS5+cQnubsHdFhZn0cVhH7IkkDzcAjyCBEjgPXSB8HAWfzhscAmuQYP7xS8SNI9fLzXU6Ttzzti5p+bmW5/PvTPT3dVd/a3uM/Xvqu4hQIAAgRUUENisYKUpMgECBAgQIECAAAECawUENms9fCJAgAABAgQIECBAYAUF9tpvv/2OW8FyKzIBAgQIzCBw4KMP7Zd+6sEHzZDLogQIECBAYHUE9NisTl0pKQECBAgQIECAAAECEwT2zvSdO3dOmL21k7dt29YXoNXybd++vS/fjh07thZqwtZb91O+CRU35eTW/ZwfU1bkhMU2q36X9fd0s8o3gWPmya2Xz/kxc5WuydB6/Srfmuqa+YPzY2ayNRmOz8efHps1h4IPBAgQIECAAAECBAisooDAZhVrTZkJECBAgAABAgQIEFgjILBZw+EDAQIECBAgQIAAAQKrKCCwWcVaU2YCBAgQIECAAAECBNYICGzWcPhAgAABAgQIECBAgMAqCghsVrHWlJkAAQIECBAgQIAAgTUCAps1HD4QIECAAAECBAgQILCKAgKbVaw1ZSZAgAABAgQIECBAYI2AwGYNhw8ECBAgQIAAAQIECKyigMBmFWtNmQkQIECAAAECBAgQWCMgsFnD4QMBAgQIECBAgAABAqsoILBZxVpTZgIECBAgQIAAAQIE1ggIbNZw+ECAAAECBAgQIECAwCoKCGxWsdaUmQABAgQIECBAgACBNQICmzUcPhAgQIAAAQIECBAgsIoCAptVrDVlJkCAAAECBAgQIEBgjYDAZg2HDwQIECBAgAABAgQIrKKAwGYVa02ZCRAgQIAAAQIECBBYIyCwWcPhAwECBAgQIECAAAECqyggsFnFWlNmAgQIECBAgAABAgTWCAhs1nD4QIAAAQIECBAgQIDAKgoIbFax1pSZAAECBAgQIECAAIE1AgKbNRw+ECBAgAABAgQIECCwigICm1WsNWUmQIAAAQIECBAgQGCNwN5rPvlAgAABAnuMwGMOfe4u+1JPe8xB/7bLfBMIECBAgMCqCuixWdWaU24CBAhsIHDUB4+cuMR68yZmMoMAAQIECDQsILBpuHIUjQABAosIHPOVoydmX2/exExmECBAgACBhgUENg1XjqIRIEBgEYGvf/Xo7q9/+csuq8i0zJMIECBAgMCeJCCw2ZNq074QIECgEkgAc8yYACbTxgU8VVZvCRAgQIDAygkIbFauyhSYAAEC0wt8fcxwtHHTpl+jJQkQIECAQJsCAps260WpCBAgsBSBST02S1m5lRAgQIAAgYYEBDYNVYaiECBAYNkCP/3Jj7qf/uTHo9XmfaZJBAgQIEBgTxMQ2OxpNWp/CBAgMBA45qtfHE2p348mekOAAAECBPYAAYHNHlCJdoEAAQLrCdSPdq7fr5fHPAIECBAgsGoCAptVqzHlJUCAwIwC9aOd6/czrsbiBAgQIECgaQGBTdPVo3AECBBYXKB+tHP9fvE1WwMBAgQIEGhHYO92iqIkBOYXOOyww+bPLCeB44XA7/u9dK4cLyrbTi4gcMABByyQW1YCBLZSoA9stm3btpVl2HDbrZbv2GOP7cveavkKrPIVCa8ECBAgQGB9gWV+Zy5zXeuXer65rZZP+2q++hzmarV+Szk3o3x6bIqu1z1CYP/9998j9sNOECBAgMDuFTj88MN37wZtjQCBpQv0gc3OnTuXvuJlrLBEcq2Wb/v27f1u7tixYxm7u/R1tO7XevmWXiFWSIAAAQLNCyyjzdH691vr5dO+Wuw0ab1+N7N8Hh6w2LEjNwECBAgQIECAAAECDQgIbBqoBEUgQIAAAQIECBAgQGAxAYHNYn5yEyBAgAABAgQIECDQgIDApoFKUAQCBAgQIECAAAECBBYTENgs5ic3AQIECBAgQIAAAQINCAhsGqgERSBAgAABAgQIECBAYDEBgc1ifnITIECAAAECBAgQINCAgMCmgUpQBAIECBAgQIAAAQIEFhMQ2CzmJzcBAgQIECBAgAABAg0ICGwaqARFIECAAAECBAgQIEBgMQGBzWJ+chMgQIAAAQIECBAg0ICAwKaBSlAEAgQIECBAgAABAgQWExDYLOYnNwECBAgQIECAAAECDQgIbBqoBEUgQIAAAQIECBAgQGAxAYHNYn5yEyBAgAABAgQIECDQgIDApoFKUAQCBAgQIECAAAECBBYTENgs5ic3AQIECBAgQIAAAQINCAhsGqgERSBAgAABAgQIECBAYDEBgc1ifnITIECAAAECBAgQINCAgMCmgUpQBAIECBAgQIAAAQIEFhMQ2CzmJzcBAgQIECBAgAABAg0ICGwaqARFIECAAAECBAgQIEBgMQGBzWJ+chMgQIAAAQIECBAg0ICAwKaBSlAEAgQIECBAgAABAgQWExDYLOYnNwECBAgQIECAAAECDQgIbBqoBEUgQIAAAQIECBAgQGAxAYHNYn5yEyBAgAABAgQIECDQgIDApoFKUAQCBAgQIECAAAECBBYTENgs5ic3AQIECBAgQIAAAQINCAhsGqgERSBAgAABAgQIECBAYDEBgc1ifnITIECAAAECBAgQINCAgMCmgUpQBAIECBAgQIAAAQIEFhPYe7HschPYeoEznvGMo0L88z//8+j9tG+OO+647m1ve1v3l7/8ZdosliNAgAABAgQIEGhMQGDTWIUozuwCF7jABUaZrnOd64zez/LmyCOP7H73u9/NksWyBAgQIECAAAECDQkYitZQZSgKAQIECBAgQIAAAQLzCeixmc9NrkYFfvWrX3Wvfe1rpyrd3e52t6mWsxABAgQIECBAgED7AgKb9utICWcQ+M1vftN99rOfnSGHRQkQIECAAAECBPYEgb332WefLv9aTq2Xb/v27S3z7fH1e4pTnGJT/bdt29ad+9zn7s5ylrN0P/rRj7pjjjmm+/Wvf72p2xyu/PSnP3133vOetzv1qU/dffvb3+6+8Y1vdH/4wx+Gi63U5xOd6ETdec5znu50pztdd6pTnar77W9/2+3YsaP7yle+crx8kMNWHWfZ7jnPec4ux9jJTnay7pe//GV/nOcYWzSljnMP3JnPfObuhCc8YffjH/946vNn33337fJgkNOc5jTdn/70p+5nP/tZ9/Wvf73LxQuJQAROe9rTdhe+8IW7k5/85N1PfvKT7pvf/GZ//C6qs8zv9NbbL62Xb5l1sehxMS5/637Hx/LpsRl3pJpG4G8C5zrXubp73etefWNvCJKg4oUvfGH35S9/eThr9Pmud71rd8lLXnL0eb03+++//9jZV7nKVbpb3vKW3QlOsOvtcAkCnvWsZ3U///nPR3mvdrWr9ctnQp729tznPrf70pe+NJqfNze/+c27a1zjGv2017/+9d173/ve/n3579KXvnR35zvfuXzsXvSiF3Wf+cxnRp+vcIUrdLe//e1Hn9d787znPa/74he/uGaR7EvyX+Yylxm7X3/961+7j33sY90rX/nKfh+SOUHdv//7v69Zz0YffvCDH3RPetKT+vo75JBD+sVjdfDBB2+Udar5szhkhTlmHvSgB+2y7nmOswQgG+1TnO9///v3x3E2muMh9XH00Uf3ZTjlKU/Z3f3ud+8SQIxLKe+b3vSm7oMf/OC42RtOyxMKr3Wta42t41wcyHG1c+fOXdZz9atfvbvRjW7UneQkJ9llXiak8fr85z+/D4TrBR73uMd12aekBz7wgWOD4xve8IbdDW5wg1G2973vfd3rXve6rj5Xc16P6/XNhY3U31577dXnf/Ob39y9613v6t/X9TFa+QZvnvCEJ/SBXlks+3uzm92su9jFLtY31Med82XZGBx22GH9x6c//eljjcuyw9ecXw94wAP6ydOYXfOa1+zLVdaT4y4XeB784Af3F3vK9Glen/nMZ/bB6TTe663vbGc7W3ef+9yndxoulws///Ef/7HLxadZfPP378UvfvFw1T4TILACAnsfe+yxY79cWih7riQmjfvya6F85UpCGpgtptb9llW+zbiCe93rXre78Y1vPLFa8yV573vfu3vPe97TveENbxi73IlPfOKx08dNTGMpDc865Yu7fuJbPS/vc/w98pGP7AOsz3/+8/3s97///d3FL37x7nznO1/fALvnPe/ZpfHyi1/8YpQ9V9FLqt9n2pnOdKbuTne6U5ndffrTn14T1GTGMM9o4TFv9t5712snWf+lLnWpMUv/96Q06Pbbb7++kZrALGncev576cn/F//0FJQ0z3pK3uHrrOsat/y8x9lG+5T5Bx54YHfWs551VOyXvexlo6AmdXjQQQf1PWWjBQZvcozf+ta37hvNOa5mSXe5y13WreP01D3kIQ/pj818B5WUYPEWt7hF+Tj2NYHgQx/60D5v3WtZH5c5hoaPb68D+qw4wXOCmqRyrOT9uHrK9JxLJajJ53p7dX1k3jSpzhPrRz3qUd20vc91edcLgMaVo16+3odxZgkUb3KTm6xZTfGpy7BmgXU+jMtbpq2Tbc2sHCO3u93t1tRFvUB6H/N38bGPfeyo7TCrb+p50e/1ZX2/1fu2zPetl0/7arHabr1+N7N8u7Y6FrOUm8DKC2RITh3UJLBOAJMrgblSmCuYGRKWlPef+tSnuu985zu77HfdcMkV6mEAlgCkbijVK0hPTR3UZNsf/vCH+96Z9F7kSngaJWmM5OpiGnoZrpP07Gc/u3v0ox/dN1pThlydTS/FsKFXby/v8+V/wAEHjMqUq7L/9//+3+Fi/ZCiMjE9IN/97nfLx/71/Oc/f3fSk550zbTy4RznOMeaBu9b3/rWLo3mlC350ouTHqOkDDHJen7/+9/3jYzhVfQMX9v3770Nf/zjH3fpmRqWq5RhM15Tt6njcekSl7jEuMn90K9lHGfDlafBmaCl/L5TAuYXvOAFXQl+s/z1rne9UVCT4+alL31p37OWYQtxv8bfevTSQ5F0/etfv6+j/sMU/2XYZh24pscuj1NPHacHMz0yOe7TiL/yla/c/dd//ddorbe97W1H77/whS/0DwLJ0LjUc8qVx7knb74Uc/58/OMfHy2/3pus94pXvOJokfRSprdy2hSDDHuaJqVHpLau81z0ohddc/6UeQm66qAmQ/ZyMSLrKinHex2olumf/OQndwnG6mNueN78+c9/Llk3fE2PXh0I1Rk++tGPdmc/+9nrSd2FLnShUZD4rW99q8vDXOq0aLCQIWepy/J3M8MTU5c51xPQ5HzK38WcA//6r//a92hn+4v41uX3ngCB9gUENu3XkRLuZoE73OEOoy1m3PZTnvKULg3npIzx/8AHPtBl6Fi+SJPueMc7dhlWMkz1Fc2Xv/zl3U9/+tM1izz5yU8eO9wmwchNb3rT0bJpQLziFa8Yff7qV7/afeQjH+mHgqThny/yDPspV5/TgHzqU5/aX7XMvDSI7nGPe/TD0kYrGfMmQ71KQJJgIkNd6oZVyVLvV4Yp1Q3TLLNeT1MapyVlOM073vGO8rFLQzb/znCGM/QBZGakoZTAMQ2YDBGqU/wz5CgpDabh/HrZzX7/ta99rTviiCPGbubwww8fO31Zx1m98gQmGSJUGuGpvwS6OWbqVAfN73znO0e9cglyjjrqqO4Tn/hEf9znWEyDO0Fv3TtSr2v4/kpXutJoUo7TDCksKXWecymBVVKOh3L8JJAqjeiUI8FYOf5yv0+55ye9XCXvNIHNsPcowXR93PUrW+e/WGYI27QpQe6kYzE9CblwMkwZflZSfiz47W9/e/k4ek0vV4YWDtO4iw9leFr8JpVluJ7h58te9rJ9QDmcXj6XYXjlc14zVK8EX/l7lAsyy0y3uc1tRsfI9773ve5pT3va6IJNjq1MK0NWc6Ekf6tyvC3iu8zyWxcBApsvcILN34QtEFgdgQQCueJcUhpXJagp0xI4ZHpJuTJeenDKtLxm3H1JszxsIF/CJXjItl/1qleV1YxeczW3vuKcoVt1ypXS//N//s9oeFsakNe+9rXrRda8v9WtbjW6+prGUBpGCW7GpdKFnHnDK7Ljlq+n5UprrsDnX+7fGJfyEIGSasMybU94XeZxVjxi9bCHPWwU1OQ4TXA6DGqyfOo2dZCAML2Rw5S89ZX9BDbTplxVTxCUfwkihqk+ZrKdknJclWMjDfsS1JT5ea2HJW90U2yOtdwjV/ce5VHwswQ12WYuCpQegs36Ed/atwR62fZWpdjWvWfTBrWbWd7UQXrpSso9WvXxk+m58JRjKCnLp4c4qTXfvlD+I0BgUwT02GwKq5WuqkCe3lRSGn0ZjjUupXGWoRb7/n0oVIaolS/UsnxplGco0DA4KsuMe826SsrV83ENvMzPPQK5ByKN5Hxx52p3vWyuYL761a/ucpUzKWPlx11BzfCvq171qv0y+S9BW4bCTErlBu3MH+7zpDxleq5G598wpRGSK+Ppocn9QZuV0uhOEFdS6iZB4ve///0uvS61X1lmM16XeZylfAkKcs9KGc6U4y29dpOO3/TijEvpocnY9gTKdWNw3LKTppX7osbNzzDKujeyfihFnoaXIZTjUvYvPXQZijZNyvF03/ved82x9JKXvKTviZomf1kmDqUHIj2uGVaXoXTLTqmnMqwrx8a4oa3L3uZ668swrvxdSUqvbM7JMrRxvXzzzMtwxNw3VVKCqB/+8Id9D139YJT05KZek/K3edgDXvLn2C5/Q3NeJ7XmW8rqlQCB5QsIbJZvao0rLFDuK8guTGoUlt3Ll+++fw9ski/DqOpUrijPEtQkf93ozdCKSSmN8lzBLsOO8sU/LHPuy8lVy3J/QRmmUdZZ3+ORaQk6hvtRli2vde9UhojNk9KDdPnLX75vrKYxXnqo5lnXLHnScK+DuDpvejFyr8lG+1/nmff9so+zhz/84aNhhCnTE5/4xH743nrlSyCcYWO5Cp6HRqQe4rPs9E//9E/9vTQJlEpjOdvIfSg5PocpZUkdJQjKMMqcR6VBO1x20ufcK1YCkiwzT1CTCxP/8i//MtpEniiXRvhmpDykowQ2OUdzHuaerbqnpNxMvRnbr9eZYYq5FygpPVQZUpbhjZuVMsQu/8alHCOpuzgUnyy33oWX/A0c/h1syXfcfppGgMDyBAQ2y7O0pj1AoB7/Xg+ZGbdr9ZCpuhcjy5Z7X/K+Xi6fN0p14LBRj0g9XCyNwOEXeraVq8wlsNlo28NHQ49bPgFUSRuVryxXXtNwTqOzDBEp03fnaz18pW7Ip84y7ChBwTjHZZZxWcdZyjQuKMxjv+uhisOypw5TD+nB2uyUbZWepHpb+X2a9MTUw8vyRLRl9IjUQU22mftycq/WLD3D7tExAABAAElEQVRyd7nLXUaBXm5QX68xXe/XPO8z/CzHYh5znfqse7XmWd+8eVKGPIq5pHHDvcq8Zb3mAk2plwSw5T6rrD/Dcu92t7v194kl4C1p1r87rfiW8nslQGDzBAQ2m2drzSsoUA9vSKCwXqobhcN7aOqhFbM2iPKlXYZS1EHOuLKUm/0zb1wglh+/TMOgpPRGpEFZGn65/yJX0Ut5M3wnw4HqgKnkzWsaHWW/cxU1jZJZUh71XAc1adTmynSGjOR3ZzJ8Lr/bk+Fxm5FST7mBu07pGUgjNo3sNKxyb0H5jZB6uWW+X9ZxVpcpN62XACJPNUuPYh48MUypw/QKlHrM/ByjsU+eUg9x2ugcGK573OccY+n9SOM1x15poOYYv9/97jf6PZ704NVBTXo6U6YMy0q5MowyV+1TV9Ok2iPbTIO9vjduvXWkx6I8YCHreeMb37je4gvPS51k/7c6ZWhr+Zvyuc99buz9WcsuY3pk8mS3ktJDl4c15BhOuuAFL9gPg0s9lFTKWD5v9NqK70blNJ8AgcUFBDaLG1rDHiSQBlRJdc9EmVa/lgZapqUxWKd6aMWs4+VThjIUpB6yVK+/vE9jvKQ8wa1OpXckX+pJabzmh+vSeKkDm1yNfsxjHtM3ijNcKE9eyg9bjkv1cJDh9sYtX09LOeofLJ00PKiUt867me9zw3F+ODA33ifV9bpZ213WcVbKl6fm5Z6r7EMZspTf+kgAlf2rU54WVXoY8/SxQw89dGwP1bLqIcPN6iFn8c39QAki8z7HXILk8qS0lDX3luVpauVKfil/GfpZPk96zdPSMqwwQ8nK8LEMuUuDedw9XvV6EuiX+9IyPcHQsBz18st4n/Oi1FvWl/ta8uO/qZ+y7Zx7w9+UWca2yzryN6H07Ga78duKlN81Sm9jLoCUB7nkHp962GsC5Ekpw1zLQwbyxL/cp9OC76Tymk6AwHIF/rvFs9x1WhuBlRWoA5TcLDvphtk0DEsvR3a23KRadrxuwA8blmWZSa91GXLz8qQGZh7HWu5ZSMOwNIDKejOsqm7ApvE+rocljZh6XoKp9JqMS/lxvJJyNX2WVHqhkic3/+aRwuNS3aMzbv5mTKt7UOa9aX6WctV1vMhxlm2mFyo9M6n/PDCgfnJXHr1dD3vL8nXQnXzjht3luCq9P8kzbcrvL+Ux5vk36Yc2s736B2NLgz69iyWNC2oyry57WXbca3mSYB6eUf++0A1ucIMNewNz31k5BjJ8rTxmetx2ljWt/luSoOw///M/+3uQEtykxyv/vvW3h5VsZsrfi5LyePr8TdnKVAcy+TtW/41NkDfp72J+syYBWv6VB7i04LuVlrZN4PgkILA5PtW2fd1QII38+slhGfZSgoeSOV+o9Tj0NNLrhlqG+JTGZBqbedrWLCk3zJYHDqSBNa6BmC/6OvjI1fo65dHOuXJZUn7Lor6XoUwvr2ls1vdk5B6N+rcfynL1D/9NCkzKssPXuiFSD4Oql8t26wZuPW8z35er+tnGrOP35ynXMo6zcdvNEMIEN+U+ovTaHXjggaPhRclT18O4K9/pScnwxbzOmvKwixyz+ZcHE9Q9imVdmVaGWCbQLr1X9fbGHR8JjEuPQlnXNK957Hn9dK088av8BtW4/KXMadjXvx81btllTSsPAMn6ck/cVqSy37lgkYBuK1P+5l7kIhcZFSG93qnDUo+ZXw9bLAtm2Frpac85VoKhFnxLGb0SILC5Aoaiba6vta+gwMte9rLRsKT0XuReg/wYXQKeNK4SNJTAJbtXhmxkWE2G09Tz0nBLQ2pcqm/6zjJZNleq84X85je/eRTQ5AlRKceHPvShfmhRhhJlOyV/lq9/EybDN+ohK+9///u7o48+elwR1kx73/ve1zcmyr0Fadw+7nGP6xv6t7/97ftfN6+v4o9rWGSF9XC1PJ43wVB+eb4ekpcrqbnfJvubIC5lztX0su1SsKwr+1kCvTJ93teM30/wVFLKkV6AmJaUewt2R5r3ONuobOl9SmM+99EkWMg+5odMDznkkL5XJz2I5bHJGfKYXpYMFcuyGcKTY6duCGZ7CQTK0Kj1tp9ehQRXuQcijc+cOxn6VR5KEev0iJTgKsMZSxCWK/Tl3MlwyDy2Nw3ZTLvWta7VB0p18JMGbLYz6X6wUs6sP8Ptcu9YAq6sIzb/63/9rzUXJMry5TVDJZd13JV1Tnqt72XaHYH1pHLkQsy8P+g5aZ0bTc8FlBLI5rjIhY3LXe5yo2A8ZSq93vkNogyxTLrZzW7WH9v5+5ZeyvRg10MIM5yv9FC34ruRhfkECCwuILBZ3NAa9jCB3IuSHwhMQzspX4r1b5/Uu/uBD3ygS2MuKUHP8Kb3XDEfTusXHvxXlnnDG97QBzb5ss5wtjLGPA3CccNw8sWdBnJpgCXwuPe97z1ae/Ylj2udNuUenIMPPrhvMKSRkSdnPeEJT+gbGsN1lDIPp9efM6ws/zIcKFfzM8wmjZak5B+3jjTsyhX9DMXL8LeUYxkpQVLd0zVcZxrSm32jeNnmvMdZyb/ea7zT25CANClBwD3vec8uvzGT34vJfpbgJfdc5d8w1TffZ5hSyptjYaP0jGc8o/8F+gQQCSTSAM2/YUpAnkcol5RgPj2kSSlvApEc33UwUx8bCXjyBLsMoywN3z7zmP/yZML0ZOXenhzXCbrSk5XAfdyQq/Sypud0d6US0GV7WxnY5ALJ8EEom22QH1Ctf0R1uL1cOMp9N0kZOpm/i/m9q6Rc4Knvzeon/u2/BPf1xZ5WfEv5vBIgsHkChqJtnq017yaBcsU3m8vVvWlTuZqX5ev3+ZyrzLnqPa7Rk/lplD3/+c/vXvOa1+Tj0lPKc/jhh/eN7GHZysbSOM1V5/xGQ0n5pfW6J6e+d6YsU3vV7zM/AVL2u2xzvaCurG+W14zdn9Qjkm2nMf74xz9+NORkvXWXMmaZ9ep9uI/DdSZvniiXOk9Dd7111Xnr9dbv62XyvpRz3HrnPc7q7ZX1D7ebG/Df/e53jyZnaOI1r3nNfv9ybCVQGZfSsH7a057WBww5zmdNuX8o99jUQzqH68hwp6c85SlrfmQxx3Eao7VTCWqyj+mxfOxjH7vmCVpZb+n9qfPV78u2s7/Pec5zysd+mFyCvaTaM3nzmONxqV5v/b7OP6k+htspeXJBpDzlK+ush7XWZSjLZ1q97XqZWd/X68mQ2ve85z1jV1HvU51nuHA9r84zXK7el+G8fM7f3Rwj6WWsn5iWeQnO07s8bv2ZluMoAXgpy1b6prwSAQK7V2Cvv10RPW69sfe7tzhrt1bG/LZavnLTa74QWkyt+y2zfOXxvPvvv//SqyJXttNbkh/OTOMoV8Prm83LBjMUogw7S49PGq3TpAyLySOHkx7xiEfsci9MGncZipYyJNDIkK6UoX786TTbmXeZXN3OUJ6kNFbT6J0mZfhQ+T2O3MSdhmlJuYKa8fB5zRXi9ObkBuk65ab6XPFPQ3t3X0Wuy7G73k97nC2zPLmpOsdVhqulQZ2eivphAulxTD3kGMwxPynQn1Sm7FOGE+b4TQCSdedm/EmN96wnZcn9FTnfcqU+w9Vy30kdZOU8yL8yf9L2Td9zBfK3IT09+R7OsZFjK39D6odnzLr3CfiTltFDvMzvt1n3Y5rlWy+f9tU0tTh5mdbrdzPLZyja5OPCHAK9QHpG8m+rUq5C5ibYciPsVpVjmdtNIzlj4NdLk3oU1suzyvO24jjLVfH8m5RyZb1+gtuk5SZNL/v02c9+dtIiu0xPwzRDFtdL6WEb97tN6+Uxb88SSJA97nea9qy9tDcECMwqYCjarGKWJ0CAAAECBAgQIECgOQE9Ns1ViQKtqkCGQpSnP613FXy4f7nnJEMp0jNTbpIdLrOVn3PVPk9Vy3CkWR5dnSFzxWNP6m3ayrqwbQIECBAgQGCygMBmso05BGYSyH0i9c3J02bOjbD512rKTbi5YXfWlCdVbfS0qlnXaXkCBAgQIECAwCQBQ9EmyZhOgAABAgQIECBAgMDKCAhsVqaqFJQAAQIECBAgQIAAgUkCAptJMqYTIECAAAECBAgQILAyAgKblakqBSVAgAABAgQIECBAYJKAwGaSjOkECBAgQIAAAQIECKyMgMBmZapKQQkQIECAAAECBAgQmCQgsJkkYzoBAgQIECBAgAABAisjILBZmapSUAIECBAgQIAAAQIEJgkIbCbJmE6AAAECBAgQIECAwMoICGxWpqoUlAABAgQIECBAgACBSQICm0kyphMgQIAAAQIECBAgsDICApuVqSoFJUCAAAECBAgQIEBgkoDAZpKM6QQIECBAgAABAgQIrIyAwGZlqkpBCRAgQIAAAQIECBCYJCCwmSRjOgECBAgQIECAAAECKyMgsFmZqlJQAgQIECBAgAABAgQmCQhsJsmYToAAAQIECBAgQIDAyggIbFamqhSUAAECBAgQIECAAIFJAgKbSTKmEyBAgAABAgQIECCwMgJ7p6Tbtm1rusCtlu/YY4/t3VotX6lU5SsSXgkQIECAwPoCy/zOXOa61i/1fHNbLZ/21Xz1OczVav2Wcm5G+fTYFF2vBAgQIECAAAECBAisrEDfY7Nz584md6BEcq2Wb/v27b3bjh07+M0h0Hr9zrFLshAgQIDAigsso83R+vdb6+XTvlrsJGq9fjezfHpsFjt25CZAgAABAgQIECBAoAEBgU0DlaAIBAgQIECAAAECBAgsJiCwWcxPbgIECBAgQIAAAQIEGhAQ2DRQCYpAgAABAgQIECBAgMBiAgKbxfzkJkCAAAECBAgQIECgAQGBTQOVoAgECBAgQIAAAQIECCwmILBZzE9uAgQIECBAgAABAgQaEBDYNFAJikCAAAECBAgQIECAwGICApvF/OQmQIAAAQIECBAgQKABAYFNA5WgCAQIECBAgAABAgQILCYgsFnMT24CBAgQIECAAAECBBoQENg0UAmKQIAAAQIECBAgQIDAYgICm8X85CZAgAABAgQIECBAoAEBgU0DlaAIBAgQIECAAAECBAgsJiCwWcxPbgIECBAgQIAAAQIEGhAQ2DRQCYpAgAABAgQIECBAgMBiAgKbxfzkJkCAAAECBAgQIECgAQGBTQOVoAgECBAgQIAAAQIECCwmILBZzE9uAgQIECBAgAABAgQaEBDYNFAJikCAAAECBAgQIECAwGICApvF/OQmQIAAAQIECBAgQKABAYFNA5WgCAQIECBAgAABAgQILCYgsFnMT24CBAgQIECAAAECBBoQENg0UAmKQIAAAQIECBAgQIDAYgICm8X85CZAgAABAgQIECBAoAEBgU0DlaAIBAgQIECAAAECBAgsJiCwWcxPbgIECBAgQIAAAQIEGhAQ2DRQCYpAgAABAgQIECBAgMBiAgKbxfzkJkCAAAECBAgQIECgAQGBTQOVoAgECBAgQIAAAQIECCwmILBZzE9uAgQIECBAgAABAgQaEBDYNFAJikCAAAECBAgQIECAwGICApvF/OQmQIAAAQIECBAgQKABAYFNA5WgCAQIECBAgAABAgQILCYgsFnMT24CBAgQIECAAAECBBoQENg0UAmKQIAAAQIECBAgQIDAYgICm8X85CZAgAABAgQIECBAoAGBvVOGbdu2NVCUyUVotXzHHntsX+hWy1dEla9IeCVAgAABAusLLPM7c5nrWr/U881ttXzaV/PV5zBXq/VbyrkZ5esDm7IBrwRWXeDwww9f9V1QfgIECBAgQIAAgTkE+sBm586dc2Td/Cwlkmu1fNu3b+8RduzYsfkYc2yhdb/WyzcHuSwECBAgsOICy2hztP791nr5tK8WO4lar9/NLJ8em8WOHbkbETjggAOWXpLNPPGWUdjWy+eLabFaXnb9PubQ5/YFesxB/7ZYwf6ee9nlW0qhqpW0Xj7nR1VZc7xtvX7n2CVZCBBYgoCHBywB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxAQ2CwB0SoIECBAgAABAgQIENhaAYHN1vrbOgECBAgQIECAAAECSxDYa7/99jtuCeuxCgIECBBoWODARx/al+6pBx/UcCkVjQABAgQIzC+gx2Z+OzkJECBAgAABAgQIEGhEYO+UY+fOnY0UZ20xtm3b1k9otXzbt2/vy7djx461BW/kU+t+yrfYgdK6n/Ojzfpd1t/T1o+/1svn/Gjz/FisVP/I3frx13r5nB//OJbmedd6/W5m+fTYzHPEyEOAAAECBAgQIECAQFMCApumqkNhCBAgQIAAAQIECBCYR0BgM4+aPAQIECBAgAABAgQINCUgsGmqOhSGAAECBAgQIECAAIF5BAQ286jJQ4AAAQIECBAgQIBAUwICm6aqQ2EIECBAgAABAgQIEJhHQGAzj5o8BAgQIECAAAECBAg0JSCwaao6FIYAAQIECBAgQIAAgXkEBDbzqMlDgAABAgQIECBAgEBTAgKbpqpDYQgQIECAAAECBAgQmEdAYDOPmjwECBAgQIAAAQIECDQlILBpqjoUhgABAgQIECBAgACBeQQENvOoyUOAAAECBAgQIECAQFMCApumqkNhCBAgQIAAAQIECBCYR0BgM4+aPAQIECBAgAABAgQINCUgsGmqOhSGAAECBAgQIECAAIF5BAQ286jJQ4AAAQIECBAgQIBAUwICm6aqQ2EIECBAgAABAgQIEJhHQGAzj5o8BAgQIECAAAECBAg0JSCwaao6FIYAAQIECBAgQIAAgXkEBDbzqMlDgAABAgQIECBAgEBTAgKbpqpDYQgQIECAAAECBAgQmEdAYDOPmjwECBAgQIAAAQIECDQlILBpqjoUhgABAgQIECBAgACBeQQENvOoyUOAAAECBAgQIECAQFMCApumqkNhCBAgQIAAAQIECBCYR0BgM4+aPAQIECBAgAABAgQINCUgsGmqOhSGAAECBAgQIECAAIF5BAQ286jJQ4AAAQIECBAgQIBAUwICm6aqQ2EIECBAgAABAgQIEJhHQGAzj5o8BAgQIECAAAECBAg0JSCwaao6FIYAAQIECBAgQIAAgXkEBDbzqMlDgAABAgQIECBAgEBTAgKbpqpDYQgQIECAAAECBAgQmEdAYDOPmjwECBAgQIAAAQIECDQlILBpqjoUhgABAgQIECBAgACBeQQENvOoyUOAAAECBAgQIECAQFMCe+23337HNVUihSFAgACBpQsc+OhD+3U+9eCDlr5uKyRAgAABAi0I6LFpoRaUgQABAgQIECBAgACBhQT2Tu6dO3cutJLNyrxt27Z+1a2Wb/v27X35duzYsVkEC623dT/lW6h6u9b9nB9t1u+y/p62fvy1Xj7nR5vnx2Kl+kfu1o+/1svn/PjHsTTPu9brdzPLp8dmniNGHgIECBAgQIAAAQIEmhIQ2DRVHQpDgAABAgQIECBAgMA8AgKbedTkIUCAAAECBAgQIECgKQGBTVPVoTAECBAgQIAAAQIECMwj0D88YJ6M8hAgQIBA2wKPOfS5uxSwnvaYg/5tl/kmECBAgACBVRXQY7OqNafcBAgQ2EDgqA8eOXGJ9eZNzGQGAQIECBBoWEBg03DlKBoBAgQWETjmK0dPzL7evImZzCBAgAABAg0LCGwarhxFI0CAwCICX//q0d1f//KXXVaRaZknESBAgACBPUlAYLMn1aZ9IUCAQCWQAOaYMQFMpo0LeKqs3hIgQIAAgZUTENisXJUpMAECBKYX+PqY4Wjjpk2/RksSIECAAIE2BQQ2bdaLUhEgQGApApN6bJaycishQIAAAQINCQhsGqoMRSFAgMCyBX76kx91P/3Jj0erzftMkwgQIECAwJ4mILDZ02rU/hAgQGAgcMxXvziaUr8fTfSGAAECBAjsAQICmz2gEu0CAQIE1hOoH+1cv18vj3kECBAgQGDVBAQ2q1ZjykuAAIEZBepHO9fvZ1yNxQkQIECAQNMCApumq0fhCBAgsLhA/Wjn+v3ia7YGAgQIECDQjsDe7RRFSQjML3DYYYfNn1lOAscLgd/3e+lcOV5Utp1cQOCAAw5YILesBAhspYAem63Ut20CBAgQIECAAAECBJYioMdmKYxW0orA/vvv30pRlIMAAQIEVkjg8MMPX6HSKioBAuME9NiMUzGNAAECBAgQIECAAIGVEhDYrFR1KSwBAgQIECBAgAABAuMEBDbjVEwjQIAAAQIECBAgQGClBAQ2K1VdCkuAAAECBAgQIECAwDgBgc04FdMIECBAgAABAgQIEFgpAYHNSlWXwhIgQIAAAQIECBAgME5AYDNOxTQCBAgQIECAAAECBFZKQGCzUtWlsAQIECBAgAABAgQIjBMQ2IxTMY0AAQIECBAgQIAAgZUSENisVHUpLAECBAgQIECAAAEC4wQENuNUTCNAgAABAgQIECBAYKUEBDYrVV0KS4AAAQIECBAgQIDAOAGBzTgV0wgQIECAAAECBAgQWCkBgc1KVZfCEiBAgAABAgQIECAwTkBgM07FNAIECBAgQIAAAQIEVkpAYLNS1aWwBAgQIECAAAECBAiMExDYjFMxjQABAgQIECBAgACBlRIQ2KxUdSksAQIECBAgQIAAAQLjBPbeZ599uvxrObVevu3bt7fMp36brh2FI0CAAIGWBJb5nd56+6X18i2zLjbjGGvd7/hYPj02m3GkWycBAgQIECBAgAABArtVYO9jjz2227lz527d6LQb27ZtW79oq+UrVxJ27Ngx7S7t1uVa92u9fLu1smyMAAECBJoQWMZ3euvfb62XT/tqsVOh9frdzPLpsVns2JGbAAECBAgQIECAAIEGBAQ2DVSCIhAgQIAAAQIECBAgsJiAwGYxP7kJECBAgAABAgQIEGhAQGDTQCUoAgECBAgQIECAAAECiwkIbBbzk5sAAQIECBAgQIAAgQYEBDYNVIIiECBAgAABAgQIECCwmIDAZjE/uQkQIECAAAECBAgQaEBAYNNAJSgCAQIECBAgQIAAAQKLCQhsFvOTmwABAgQIECBAgACBBgQENg1UgiIQIECAAAECBAgQILCYgMBmMT+5CRAgQIAAAQIECBBoQEBg00AlKAIBAgQIECBAgAABAosJ7L1YdrkJ7H6BC1/4wt15znOefsOf/exnu+985zsbFuKUpzxld/WrX71f7he/+EX3wQ9+cMM8Flg9gb322qu70Y1u1OX1V7/6Vff+979/9XZCiQkQIECAAIG5BAQ2c7HJtJUCV7va1boLXvCCfRGOO+64qQKbs5zlLN11rnOdPs/vfvc7gc1WVuAmbvsEJzhBd93rXrffwu9//3uBzSZaWzUBAgQIEGhNwFC01mpEeQgQIECAAAECBAgQmFlAYDMzmQwECBAgQIAAAQIECLQmILBprUaUhwABAgQIECBAgACBmQXcYzMzmQx7ssCJTnSi/sEEpzvd6bpTnepU3W9/+9tux44d3Ve+8pXuL3/5y5686yu3b2c84xm7c5/73H09/fCHP+w+//nPj92Hk570pN1FL3rR7vSnP333k5/8pPva177W/frXvx67bD3xxCc+cXeuc52rO+c5z9n95je/6Y455pjuxz/+cb3Iuu/zAIMLXehC3ZnPfOZ+ua9//evdd7/73e6vf/3ruvnMJLBZAmc4wxm6s571rP25cMITnrD72c9+1n3729/uz4vN2ua06130fJt2O5YjQGDPFhDY7Nn1a++mFMhN57e//e27y1zmMl3eD1Maox/72Me6V77ylV0eWDBMeZjBve997+HksZ8TJD3rWc8azXvc4x7X5altSQ984APHBlDXvOY1u5vd7GajPIccckj3ox/9qHvwgx/c5cEIs6RnPvOZXRrZd73rXbtLXvKSfdYXvvCFXZ4wt4xU789wfbHLU+li+aEPfWiqAGO4jpOf/OTdPe5xjz6oqef94Q9/6J73vOfVk7r99tuvu81tbrNLnWb7r3jFK8YGGdu2bevuc5/7jHVN+d/ylrd073rXu9ZsJx+e/vSn99sp5bjXve7VneQkJ1mzXI6jl73sZd0nPvGJNdOvcIUr9MdfmZiA+mEPe1j5uOY1wfeTn/zk0T5lnQ94wANGy8xbr/PmKxse7kOZPu419fTFL35xl1kPfehDuzOd6Uy7TB834fGPf3x/0aHMi3XOkYtd7GJdjpFx53FZ9pvf/GZ32GGHlY9Tvc57jp/sZCfrcr4m/fznP+8OPvjgqbZXL5R9zXG5UfrpT3/a5fwbpvOe97zdHe5wh+60pz3tcFb/OeV68Ytf3H3rW98aO3/SxGWc6/Oeb6VM9XFbpk163X///ftZudBxz3ves3//xz/+sXvQgx409u96Lk486UlP6hJ0JeW4Pfroo/v3/iNAoE2BXVtwbZZTqQhsqsCd7nSn7nKXu9zExlAaSWkkp7E6Lu299/TXCMqXZFlPGqoljWuMJei5yU1uUhbpX8v2hutas9CED+PylmkTssw0ud6fYcY0FNK4usENbtA3wNLjMkvKVeYDDzxwl6Am60jD9n73u99odempud3tbje2Ti9/+ct3d7nLXUbLljfnP//5+4bnpGAx5b/xjW/cB6DDuiqfSzmGQU22kWX+9V//tbv5zW9eNtm/Dv3TMM+V9XEpwWjZVubX7/O5PiaG6838SWnefGV969V7Waa8TipXXYay7KTXHAslxfpRj3pUd6UrXakPAIYmZbnyOst2Sp5JZS7z69d6/XU5Z1lHvb5p842rg/Rs3ve+950Y1GQ7OSfT6D/b2c5Wb3bD9+O2VzJNc64vcr6V7dTWZdqk15Qp6Utf+tLookbyl6dsDvNlell/LiDkopREgEDbAtO3xtreD6U7ngqc5jSnGQ31CcGkBmmGYExK5zjHObpLXepSo9lvfetb+8cEZ+hZvnjTi3PpS1+6n5/f0EmDOY8SrlPd8Mgwp1wRrlMaDmc/+9nrSVO/v/vd775L47Vk/uhHP7rLejP8qXwZ5wpsfs+lThlat7tSLP/85z/3m0ujIsP7cmW/NPrvdre7dY94xCOmLk4e5ZxhgiXl6ulRRx3Vf7zFLW7Rr7/MK6+pi3e+8519r9i1rnWtLg29pEtc4hJdrmSn9yopjeGUpzRE05A58sgju69+9aujcmf5pAxPu/71r9+97W1v6z8P/8u+/ulPf+re/e53d9/4xjf64zLbzv4nXeMa1+g+/OEPrzu0LY30//zP/xyuurviFa+4y7QWJhS3lCU9ABl2V6ecSzl31kv1Osb1IOb8G9eYTqB4ilOcYrTqDBlMz2DqsKTYTwoWyzLrvW7mOb7edjOvDtSGLvvss093gQtcYOIqEkiX/KmXI444oq+b7du3d/G83vWu16VXKcvk/HrRi140cV3rzZj1XF/W+VYfMxkummGjdbr4xS/e/65VPS1/27/85S/3+5/pOacS7AxTfa5lvuHIQyGfCbQnILBpr06UaAaBBB35V1KGZs2a8uVeUhrB73jHO8rH7gtf+EL/L4FRuZqZwOFTn/rUaJm8qRtbGWKTIWt1SsMjw5tmTZe97GW7fffdd2K2cUOiMqyiNOBe97rX9WPoJ65gk2ckMEgDv04JMjKEJY3/DEOJ3XCZevn6fYKDkhLUZThZSXHPeuvGc+4fqIccZQhajpEy3Cm9L2X+ta997VHelCfDvXJPTknJm+XL7+SkQfie97ynO/bYY8sio9c0gDKEpQSRCY4+8pGPdA95yENGV84zNOhpT3vaKM/wTXpmhoFNGnGz9nIN17tZn0swnfXnB3D/67/+a82mcvyv1wDPwnUjNcMjhylBcBrkw5ThZyUl2Hz7299ePo5e86O+97///UefZ32zWef4NOUogUmGTQ1dTn3qU687vK2+2PP85z+/+8EPftBvMsFf/iUYSC9o0iLH1qzn+rLOt/q4e/nLX95lOF6dch6XCyn19Pe9732jwKb+DqiXyd/6knKuSwQItC9gKFr7daSEmyyQBvYvf/nL/t+b3vSmsVvLPQ8l5ermMNVXc9P4WEbKldjb3va2o1Xl3o09ISUQqO9TKo22jfYtN/+XBkryv+Y1r1mTJcHIsEH7qle9as0yuYJfB0PpeSnpqle9annb98TUQU2Z8eY3v3l0RTjlTu/TuJT7h0pQU+an/urylEC5zC+vpZ7TAzFsxNdXn8tyJd9Wv9b3gAx7CactWzkW6uNjmrzluMiyw4BqmvzTLLMZ5/g0280yZduz1nmCsdRF/r7lIk0Jaurt1nVVB2/1MvO+X+9cX9b5Vv89nuahIGVf0mNTPLPfpTe2zM/n4pH9KD27Zb5XAgTaFNBj02a9KNWUAmXIS4YVJX3mM58ZmzNDwTLkbFzKFd5xQ4oS8CRfrtqd73znG5d1NK2+ajht78Mo84Q3GUJSvlhzBTxlKMOoJmSZe/KVr3zl/glgZQX5ws+TxjKMKsbLSgnWcoN3acDmqUylcbHRNuqr/elZG+dcNz7SOP7+97+/y2ozPC/bTGM45UjwkCCkNMyT773vfe8u+cqE97///d0//dM/9R/rq+Flfl4nXd1NYyqBb46X9E6MG9aYnqcy9DH18oY3vGG06jI05ne/+13/xL5h4DNa8O9vhvWawC5XtDNMbDhccph31s/lARjJl4b0PKn02Mwa2ORBGmWoZ55C953vfGeeza+bZxnneO6dutWtbjXaTvYzQ+ZynOZpffXQubJQ/gbkb1FSfYGlzF/vNefIpIcV5PiP1fD+vfXWN8u8jc71ZZ1vJbCJ5awXlT796U/3905mv65ylaus2b2cOyV9/OMfL2+9EiDQuIDApvEKUrz1BTIkLFfRy3CiSePDp3miUYYj5KbyXMXP1fK6IbN+KbouX+IljRuaVOZN+5pGfJ7ck5RGbIaUzTPMbtrtZZhO/o1LeYzyS17ykqkDkHodj370o0e9M7nqXBohWSbDtV7wghfUi6/7vm44D3tDSsa6l2W9Rk4a3iVILI/1LsFWvNcbS/+9732vbG60jtGEv79ZLxjMVeX0PiVlyGAdjGVa1n+Ri1ykD7wS4JTAJuUrAXbusxheYU7eYVqvXlPGnC/LCgIyJKqkBKzzpNIzsZ7/uPWmgVoCm3//93/vL1RkiFUdNG8UBI5bbz1tGed4Are6p6Jef+7be+lLX9r3rNTTc3GlpLp3pUyb5jXn3TX+dl9X/g7mHrV8Lsf7NPmnWWaWc73e/qLnW6mX9c73SeXPBYg8FCZpOBwt52BJ613oKMt4JUCgDQGBTRv1oBRbKJDGxgEHHDCxR2eaotUN9lmvqg7Xn/LkEaYlpfE5a0Ov5J32NVc7y9XiXB2uGz25fyE31T/72c+ednWj5cpV2dGE6k22kV60cb0q1WKjt6UBkwk7d+4cTa/f1E5lf+r55X1dR1lvCTQyf/hgiJKnvNY3J9c3rJf56203y2TbZXu1c8mf18997nP9U/oSdCVgSCCWhlZZPj140wQ2db1mvaVHJO/TYM49J4985CM33Ocsv1GqH9Axb49N2b9xvXHrbT/Dz7JvN7rRjfoLEje96U3XW3yuecs6x+tjtK6P9N7lMeZPfOIT+0e5l0LuW91jl96dWVMejJKnPhbbYf4cr5PmDZdd7/Ms53o5/rO+Rc63mJVe7fqcXq+c9bz09OVCQy6a1MMZ8/tV5cJWgvR5A/V6W94TILB7BAQ2u8fZVhoWyJd+PUwtjeZc7U2DO2PSM2Tnlre85ZqHFAx3p+5NmNToHuaZ9PnWt7716Cb2NHBz4/lmp/TIfPKTnxxtJo39G97whv1V3kzMld70cMzyA5XJl4cblIZcGk9xSkMtN+8ngMo9RLliO3zSU/IOU91LUzdC6uWmbaCVxlDyZr11ndUPH6jXXd7XwUwd5JT5ZdhQ+Tx8rddf9zDVyyVwyePHkzL8LEMl85S0pDQE616jfuKE/4b1mrJl2OYd73jHvkEYhzwQYfiQggmrmzg57hlmlZRekgRUs6Z6yNWsV9+z/fS2bmZaxjmeRnQCyTolQL3L3x49nsCgnBOlBzrL5Xwpadp6L8sngLjzne88GsqWekkPXYZjZqhphiTmdb2HWJR1bfQ6y7le3+Bfnw/jtrHe+ZYApKRZ/zaVfHkISR4EUqdyrmVa7peTCBBYHQGBzerUlZJugkAaROVHKrP6NASHP56Y6Rs1mOsrkJMaq1nPRilDk8p9FLlqnaEpW5EynO71r399H/CVJyVlGNSsjYc8YW7c1ff8jk0Cp6Q0IqYJbOobn8tT34Y29VXjcsV1uEw+14+MTmCTq9blynWuzOdKegnIhvnrbY/zSOM05aiDpXod5ZHPmTapZyMNz3IfUAKcPBSh3GOUYHfelIZt7kNLYJgfpE2qG87zrrcMA0v+eY//MjQw61hvKF/mD1PO4XqoWQLD3M+UY6/0oKWMi9xPsqxzfFj2DEXMj+aWH2QtT+wry9UPuJj1vqhr/G34WQm0Uy8JYIY9JOudJ6UM07zOcq7nYsYyzrd6+Oy8Qypzz9wwsMlDOpJyvghspql9yxBoR8BT0dqpCyXZAoH6yVRp4I4LalKsukdnXDHrRlndszBu2fWmZShKSXl0aX2PQJm+O1/rIRj1FetFy1DfjFs3itdbb64sl5TG3rhgs76ZP8HJuJ6dco9B1hXf0vAtvS9pCF7talcrm9rltZ5XB1v1gmlQjksJEkuZNqrbPMUqKeVNcFOGLS2joVUHH/UQq3FlnmZa/XS4WRvfZf11I7Wu6zJ/vdf6yn2OrfRA5d6wBDfp8cy/BIuLpGWd4+PKUPdglOMjy6UXqxzTaWRPOt7GrTPT6qD1ta997S5BTZaZZkhjlps3TTrXl3G+1RelhveqTVveXIDIkLQ6lTooFxjqed4TINC2gMCm7fpRuk0WqBvHZSjNcJNpyNZX+Ifz08guX4RpeJSG8nC5aT6XHoc0Doe/lTNN/mUuk0ZVfQPtvFdEx5WpPMUu86a9Op8rzSVoTCM/v4MxTMNp5Tdn6uXqp1LVvR91wJCnno2r8/Q0lTpKPee3bcalq1/96qPfqynz41l6STItPy66XqrLc7vb3a5fNMFQfptn0VQ/8Wlcr9Os66/rc9LFgY3WWQdHs66jvsE+T5VbdlrmOT6ubHV91L14abiXHpd5/raUYDjbzI8ZD1OGgdXH5HD+Mj7Xx0Z9rtfH9zznW/5el160nIt5qty86QMf+MDYrB4aMJbFRAJNCxiK1nT1KNxmC9SN9Vy5zv02+XHNjPHP1fU0ZMsQoFKW9DBk+EaeopP3daOkvk+lLD/ra76khz/CN+s6Zl0+DwgogV2CvdJLUMa/p0zzXBHNI1TLkK6sN70+ca2vsE8zDK3sz6tf/eruvve9b/8xN4qnXBlKkqdp5YbxMmyuLH+d61yn/x2PjKNPneUX6svTj3IFPMPtSsoPh+YHQBOkZtmHP/zhXaZ95Stf6TJ8LA3vkjd5csP6cFhPWVeCmIc+9KGjp3Nl+FqCrNIAz7bzpLv1Uu7zynGYspQAPL0Qs6S6XtNAzv0KCVbr4XTzHrMpUxrFsa/vg0hQNy7VPXOplzR43/u3x2rn3pjkL2XKPucx47OkaYb3zbK+smwCi2We47l3re7xy9+c9FSd//znL5vsHxyR7WY4VN2bkoA6f5+GqX6oRhyzTHpa3/KWt/T3B5Yen1vc4hb9hYGcx9luhrzm71uOr5KyrlimN6Wct2XeRq+znuvznm8ZqpehYyWoSblyPuXx+ONSvX9ZJsvmb3w9RPaoo47qH8NdgsisJ/PrCx/j1m0aAQLtCQhs2qsTJdqNAmkYZ6hEuVH7Mpe5zNiHBOQqanmcbR4PmkZuvvTqq5H5Isyvyy+a8iOhs/zQ3KLbS/48OSn/JqXc6zPPY6zzmzXrpTygITcdT5sypOhLX/pS/9tCaYTknon17pvIMumhqXtpyrZy30r9JKUcC3kC3b3uda8+kMjV7twHVO4FKvnymqvn4377qF4mDapJT+dKg36aOk7vQ10v9VXueluT3m9Urwna6qFCk9Yzbnp8ynlTz885tFHK0M78y4WF4eOPP/zhD2+UfZf5dSO37vHYZcEZJ+Rx28s8x3NM5EEkk1J6NN74xjf2wUld71k+FwU2sk2dZJkE3Als3v3ud/cBTILQbDuPwh6mBJI5TxKM5/Wxj31s9773vW/DwHu4nlnP9XnPtwwfHjqU/R6Wafi55Msj1OvAJu8T8JXHqSdfhoImCJIIEFgtAUPRVqu+lPZvAvlCLGnaL546T/0+68m9LJOuzOVLP79U//jHP37dIVNZ5zOe8Yw1DeVSxrzWVz+H268/Z6jVpB93rPe1zlNvJ+/reXWe4XJ1mYbz8jnDnjIk7pBDDlnzxLRxy9bT6u3X08v7rDfDn9L7cuihh5bJU78+5znP6cYNHcm+vuxlLxutJ427NOyGKculzhPYDFOCpvygYX0PSr1M8uZK85Oe9KQ1dVovE9c0TsfZZ94RRxwx+m2akq+ui/p93cgf9mQU5+F26vxl/fVrGnG5oh+DZz3rWaNZdb76/WiB3fAmw/PqXrThJss+Z3p5n4Zu3bM46ZHI9T6VvMP1r/c5eeY5x+vtjlt/1pvfp0mg/LjHPW60X+OWnXVaAqWcL5N6FtMrmEDmNa95zdjjdaPtbeS40bm+jPNtozJOOz/BXJ2OPPLI+qP3BAisiMBef7v6fNykp/ds9T6Useytlq88haeM+99qr+H2W/dbZvnK41H333//IcPUn3PVN481zmuupufRqsNHLecG4gxVKleFMzQpjYdc7dvoS37qglhwKoEMp8mPmGaoTRqzGVKVBlx5dG3eP+QhD+nrM1e/M9Qu9ZReivpq7aSNZf0Z2rbv3x63m6E5GR6VY2JSPR9++OH9qtKQfeADH9j/aGu2m2Mmx0jujUn+jRq6k8rT2vRc4S+BafatuG9Uzgz3Kz1ZCW5zLiUwyT0S8/4A5UbbnHd+ekm24hy/y98e/1x6bJ785CdP/VtPT3nKU/qemXLsl/1Oj032I71k6ZVJYJvewPq7NcPQ8rcvgXIe3jDpOC/rXPbrtOfbZS972dGws1yc2KjntJQzvVVlaN8jHvGINfueZbLv5VHcOd+zzCJpmd9vi5RjUt7Wy6d9Nanmppveev1uZvkMRZvuGLHU8UAgTybKY2LXS8MbrXPvhrQ1Anlk7PDm/QxJGabU6yzD3Ur+rD/DUcrTycr0aV8zdG8ZQxOn3d6qLrcZN/svyyIXOPaEczxBykbHco7XWX8nZ1nOWc+i59uiZakfNDLrkM9Fty0/AQLLEzAUbXmW1kSAAAECBAismEAemFD/wGvugZMIEFhNAT02q1lvSk2AAIEtFciQutwTk6FNszxqNw8MyL0VSXl4hLSrQHzK086mechEWUMeEZ9Gev27OGXenvKa35wpx88sv5mU+ygzBDVD7dI7leP2UY96VD/MNE/kK08ejNOke5L2FEP7QWBPFugDmzLWrdUdbbV85SlRrZav1KfyFQmvBAgsSyDDm5773OfOvLrc55R/0mSBPH44/2ZNedDJnp4yXC4PRJg15eEA9QMCEsjk8evlEez1+pb5nbnMddVlXNb7VsunfbWcGm61fsvebUb59NgUXa8ECKy8QK7GlhvQd/dV62wvN9SXX1RfeUw7QGAPFsjfijxpMOfsn//85/43fspv/uzBu23XCOzxAn1gUz8ZpaU9LpFcq+Xz1I7FjpbW63exvZN7KwTSi5DhJVuR8qheiQCB1RBIYPM//+f/XFPY8mTDZbQ5Wv9+a7182ldrDs2ZP7Rev5tZPg8PmPlwkYEAAQIECBAgQIAAgdYEBDat1YjyECBAgAABAgQIECAws4DAZmYyGQgQIECAAAECBAgQaE1AYNNajSgPAQIECBAgQIAAAQIzCwhsZiaTgQABAgQIECBAgACB1gQENq3ViPIQIECAAAECBAgQIDCzgMBmZjIZCBAgQIAAAQIECBBoTUBg01qNKA8BAgQIECBAgAABAjMLCGxmJpOBAAECBAgQIECAAIHWBAQ2rdWI8hAgQIAAAQIECBAgMLOAwGZmMhkIECBAgAABAgQIEGhNQGDTWo0oDwECBAgQIECAAAECMwsIbGYmk4EAAQIECBAgQIAAgdYEBDat1YjyECBAgAABAgQIECAws4DAZmYyGQgQIECAAAECBAgQaE1AYNNajSgPAQIECBAgQIAAAQIzCwhsZiaTgQABAgQIECBAgACB1gQENq3ViPIQIECAAAECBAgQIDCzgMBmZjIZCBAgQIAAAQIECBBoTUBg01qNKA8BAgQIECBAgAABAjMLCGxmJpOBAAECBAgQIECAAIHWBAQ2rdWI8hAgQIAAAQIECBAgMLOAwGZmMhkIECBAgAABAgQIEGhNQGDTWo0oDwECBAgQIECAAAECMwsIbGYmk4EAAQIECBAgQIAAgdYEBDat1YjyECBAgAABAgQIECAws4DAZmYyGQgQIECAAAECBAgQaE1AYNNajSgPAQIECBAgQIAAAQIzCwhsZiaTgQABAgQIECBAgACB1gQENq3ViPIQIECAAAECBAgQIDCzgMBmZjIZCBAgQIAAAQIECBBoTUBg01qNKA8BAgQIECBAgAABAjMLCGxmJpOBAAECBAgQIECAAIHWBAQ2rdWI8hAgQIAAAQIECBAgMLOAwGZmMhkIECBAgAABAgQIEGhNQGDTWo0oDwECBAgQIECAAAECMwsIbGYmk4EAAQIECBAgQIAAgdYE9k6Btm3b1lq51pSn1fIde+yxfTlbLV9BVL4i4ZUAAQIECKwvsMzvzGWua/1Szze31fJpX81Xn8NcrdZvKedmlE+PTdH1SoAAAQIECBAgQIDAygr0PTY7d+5scgdKJNdq+bZv39677dixg98cAq3X7xy7JAsBAgQIrLjAMtocrX+/tV4+7avFTqLW63czy6fHZrFjR24CBAgQIECAAAECBBoQENg0UAmKQIAAAQIECBAgQIDAYgICm8X85CZAgAABAgQIECBAoAEBgU0DlaAIBAgQIECAAAECBAgsJiCwWcxPbgIECBAgQIAAAQIEGhAQ2DRQCYpAgAABAgQIECBAgMBiAgKbxfzkJkCAAAECBAgQIECgAQGBTQOVoAgECBAgQIAAAQIECCwmILBZzE9uAgQIECBAgAABAgQaEBDYNFAJikCAAAECBAgQIECAwGICApvF/OQmQIAAAQIECBAgQKABAYFNA5WgCAQIECBAgAABAgQILCYgsFnMT24CBAgQIECAAAECBBoQENg0UAmKQIAAAQIECBAgQIDAYgICm8X85CZAgAABAgQIECBAoAEBgU0DlaAIBAgQIECAAAECBAgsJiCwWcxPbgIECBAgQIAAAQIEGhAQ2DRQCYpAgAABAgQIECBAgMBiAnsvll1uAm0JHH744W0VSGkIECBAgAABAgR2i4Aem93CbCMECBAgQIAAAQIECGymgB6bzdS17t0mcMABByx9W9u2bevXuXPnzqWvexkrbL1827dv73dzx44dy9jdpa+jdb9ll+8xhz63N3zMQf+2FMtll28phapW0nr5nB9VZc3xtvX6nWOXZCFAYAkCemyWgGgVBAgQIECAAAECBAhsrYDAZmv9bZ0AAQIECBAgQIAAgSUICGyWgGgVBAgQIECAAAECBAhsrYDAZmv9bZ0AAQIECBAgQIAAgSUICGyWgGgVBAgQIECAAAECBAhsrYDAZmv9bZ0AAQIECBAgQIAAgSUICGyWgGgVBAgQIECAAAECBAhsrYDAZmv9bZ0AAQIECBAgQIAAgSUICGyWgGgVBAgQIECAAAECBAhsrYDAZmv9bZ0AAQIECBAgQIAAgSUICGyWgGgVBAgQIECAAAECBAhsrYDAZmv9bZ0AAQIECBAgQIAAgSUICGyWgGgVBAgQIECAAAECBAhsrYDAZmv9bZ0AAQIECBAgQIAAgSUICGyWgGgVBAgQIECAAAECBAhsrYDAZmv9bZ0AAQIECBAgQIAAgSUI7LXffvsdt4T1WAUBAgQINCxw4KMP7Uv31IMPariUikaAAAECBOYX0GMzv52cBAgQIECAAAECBAg0IrB3yrFz585GirO2GNu2besntFq+7du39+XbsWPH2oI38ql1P+Vb7EBp3c/50Wb9LuvvaevHX+vlc360eX4sVqp/5G79+Gu9fM6PfxxL87xrvX43s3x6bOY5YuQhQIAAAQIECBAgQKApAYFNU9WhMAQIECBAgAABAgQIzCMgsJlHTR4CBAgQIECAAAECBJoSENg0VR0KQ4AAAQIECBAgQIDAPAICm3nU5CFAgAABAgQIECBAoCkBgU1T1aEwBAgQIECAAAECBAjMIyCwmUdNHgIECBAgQIAAAQIEmhIQ2DRVHQpDgAABAgQIECBAgMA8AgKbedTkIUCAAAECBAgQIECgKQGBTVPVoTAECBAgQIAAAQIECMwjILCZR00eAgQIECBAgAABAgSaEhDYNFUdCkOAAAECBAgQIECAwDwCApt51OQhQIAAAQIECBAgQKApAYFNU9WhMAQIECBAgAABAgQIzCMgsJlHTR4CBAgQIECAAAECBJoSENg0VR0KQ4AAAQIECBAgQIDAPAICm3nU5CFAgAABAgQIECBAoCkBgU1T1aEwBAgQIECAAAECBAjMIyCwmUdNHgIECBAgQIAAAQIEmhIQ2DRVHQpDgAABAgQIECBAgMA8AgKbedTkIUCAAAECBAgQIECgKQGBTVPVoTAECBAgQIAAAQIECMwjILCZR00eAgQIECBAgAABAgSaEhDYNFUdCkOAAAECBAgQIECAwDwCApt51OQhQIAAAQIECBAgQKApAYFNU9WhMAQIECBAgAABAgQIzCMgsJlHTR4CBAgQIECAAAECBJoSENg0VR0KQ4AAAQIECBAgQIDAPAICm3nU5CFAgAABAgQIECBAoCkBgU1T1aEwBAgQIECAAAECBAjMIyCwmUdNHgIECBAgQIAAAQIEmhIQ2DRVHQpDgAABAgQIECBAgMA8AgKbedTkIUCAAAECBAgQIECgKQGBTVPVoTAECBAgQIAAAQIECMwjILCZxzTEzAAAQABJREFUR00eAgQIECBAgAABAgSaEhDYNFUdCkOAAAECBAgQIECAwDwCApt51OQhQIAAAQIECBAgQKApgb3222+/45oqkcIQIECAwNIFDnz0of06n3rwQUtftxUSIECAAIEWBPTYtFALykCAAAECBAgQIECAwEICeyf3zp07F1rJZmXetm1bv+pWy7d9+/a+fDt27NgsgoXW27qf8i1UvV3rfs6PNut3WX9PWz/+Wi+f86PN82OxUv0jd+vHX+vlc37841ia513r9buZ5dNjM88RIw8BAgQIECBAgAABAk0JCGyaqg6FIUCAAAECBAgQIEBgHgGBzTxq8hAgQIAAAQIECBAg0JSAwKap6lAYAgQIECBAgAABAgTmERDYzKMmDwECBAgQIECAAAECTQkIbJqqDoUhQIAAAQIECBAgQGAeAYHNPGryECBAgAABAgQIECDQlIDApqnqUBgCBAgQIECAAAECBOYRENjMoyYPAQIECBAgQIAAAQJNCQhsmqoOhSFAgAABAgQIECBAYB4Bgc08avIQIECAAAECBAgQINCUgMCmqepQGAIECBAgQIAAAQIE5hEQ2MyjJg8BAgQIECBAgAABAk0JCGyaqg6FIUCAAAECBAgQIEBgHgGBzTxq8hAgQIAAAQIECBAg0JSAwKap6lAYAgQIECBAgAABAgTmERDYzKMmDwECBAgQIECAAAECTQkIbJqqDoUhQIAAAQIECBAgQGAeAYHNPGryECBAgAABAgQIECDQlIDApqnqUBgCBAgQIECAAAECBOYRENjMoyYPAQIECBAgQIAAAQJNCQhsmqoOhSFAgAABAgQIECBAYB4Bgc08avIQIECAAAECBAgQINCUgMCmqepQGAIECBAgQIAAAQIE5hHYe55M8hAgQIBA+wKPOfS5uxSynvaYg/5tl/kmECBAgACBVRXQY7OqNafcBAgQ2EDgqA8eOXGJ9eZNzGQGAQIECBBoWEBg03DlKBoBAgQWETjmK0dPzL7evImZzCBAgAABAg0LCGwarhxFI0CAwCICX//q0d1f//KXXVaRaZknESBAgACBPUlAYLMn1aZ9IUCAQCWQAOaYMQFMpo0LeKqs3hIgQIAAgZUTENisXJUpMAECBKYX+PqY4Wjjpk2/RksSIECAAIE2BQQ2bdaLUhEgQGApApN6bJaycishQIAAAQINCQhsGqoMRSFAgMCyBX76kx91P/3Jj0erzftMkwgQIECAwJ4mILDZ02rU/hAgQGAgcMxXvziaUr8fTfSGAAECBAjsAQICmz2gEu0CAQIE1hOoH+1cv18vj3kECBAgQGDVBAQ2q1ZjykuAAIEZBepHO9fvZ1yNxQkQIECAQNMCApumq0fhCBAgsLhA/Wjn+v3ia7YGAgQIECDQjsDe7RRFSQjML3DYYYfNn1lOAscLgd/3e+lcOV5Utp1cQOCAAw5YILesBAhspcDe++yzT5d/LafWy7d9+/aW+dRv07WjcAQIECDQksAyv9Nbb7+0Xr5l1sVmHGOt+x0fy6fHZjOOdOvcMoH9999/y7ZtwwQIECCwugKHH3746hZeyQkQ6AX2PvbYY7udO3c2ybFt27a+XK2Wr1xJ2LFjB785BFqv3zl2SRYCBAgQWHGBZXynt/791nr5tK8WO4lar9/NLJ+HByx27MhNgAABAgQIECBAgEADAgKbBipBEQgQIECAAAECBAgQWExAYLOYn9wECBAgQIAAAQIECDQgILBpoBIUgQABAgQIECBAgACBxQQENov5yU2AAAECBAgQIECAQAMCApsGKkERCBAgQIAAAQIECBBYTEBgs5if3AQIECBAgAABAgQINCAgsGmgEhSBAAECBAgQIECAAIHFBAQ2i/nJTYAAAQIECBAgQIBAAwICmwYqQREIECBAgAABAgQIEFhMQGCzmJ/cBAgQIECAAAECBAg0ICCwaaASFIEAAQIECBAgQIAAgcUEBDaL+clNgAABAgQIECBAgEADAgKbBipBEQgQIECAAAECBAgQWExAYLOYn9wECBAgQIAAAQIECDQgILBpoBIUgQABAgQIECBAgACBxQQENov5yU2AAAECBAgQIECAQAMCApsGKkERCBAgQIAAAQIECBBYTEBgs5if3AQIECBAgAABAgQINCAgsGmgEhSBAAECBAgQIECAAIHFBAQ2i/nJTYAAAQIECBAgQIBAAwICmwYqQREIECBAgAABAgQIEFhMQGCzmJ/cBAgQIECAAAECBAg0ICCwaaASFIEAAQIECBAgQIAAgcUEBDaL+clNgAABAgQIECBAgEADAgKbBipBEQgQIECAAAECBAgQWExAYLOYn9wECBAgQIAAAQIECDQgILBpoBIUgQABAgQIECBAgACBxQQENov5yU2AAAECBAgQIECAQAMCApsGKkERCBAgQIAAAQIECBBYTEBgs5if3AQIECBAgAABAgQINCAgsGmgEhSBAAECBAgQIECAAIHFBPZeLLvcBDZH4MIXvnB3nvOcZ+aV//M//3Of5xe/+EX3wQ9+cOb8Muy5AnvttVd3oxvdqMvrr371q+7973//nruz9owAAQIECBwPBQQ2x8NKX4VdvtrVrtZd8IIXnLmo17nOdfo8v/vd7wQ2M+vt2RlOcIITdNe97nX7nfz9738vsNmzq9veESBAgMDxUMBQtONhpdtlAgQIECBAgAABAnuagB6bPa1G98D9+dKXvtR95CMfWXfP7na3u60730wCBAgQIECAAIE9W0Bgs2fX7x6xd9///ve7z33uc3vEvtgJAgQIECBAgACBzRHoA5tt27ZtztqXtNZWy3fsscf2e9hq+Qr/KpZv773/EXOf+MQn7pa9D6c//em78573vN2pT33q7tvf/nb3jW98o/vDH/5QyDb1Nfd6nO1sZ+vOfe5zd3mfbX/3u9/t/vKXv2zqdo9PKz/jGc/Y+57qVKfqfvjDH3af//znx+7+SU960u6iF71ol+PhJz/5Sfe1r32t+/Wvfz122Xpijslznetc3TnPec7uN7/5TXfMMcd0P/7xj+tFNnx/jnOco9t33327k53sZLv9GNywcHvgAvkbknPuLGc5S/ejH/2or7Np6npZFFv5N2dZ+3B8Wc8yv2+Wua7N8G+1fNpXy6ntVuu37N1mlO8frceyFa8E9mCBq1zlKt0tb3nLPqAY7uaOHTu6Zz3rWd3Pf/7z4az+cx5mcO9733vsvOHEr3zlK/266ukJYu5whzt0l73sZevJo/df+MIXuiOOOKL705/+NJr2b//2b12eEJf0xz/+sXvsYx/b7dy5czQ/bx784Af3jbW8z/yf/exneTtKd7zjHbvLXe5y/ee//vWv3aMf/ei+8X7Xu961u+QlL9lPf+ELX9h99rOfHeUpb9IIfNCDHtQ/SSzT3vzmN3fvete7upvd7GbdNa95zbLYVK/vec97uje84Q3dFa5whe72t799n+eoo47qXv7yl0+Vf5qFTn7yk3f3uMc9+gZsvXyC1uc973n1pG6//fbrbnOb2+xyLHzsYx/rXvGKV3SxGqb8Eb7Pfe4z8q7nH3fccd1b3vKW3qeenvcxPOtZz9pPfuYzn9nd9ra37bZv3z5crHvf+97Xvf71r++yrpIS+BxyyCHlY//6qEc9qn+y25qJf//w0Ic+tDvTmc40mpXtffOb3+ye+tSnjurxSU96UveDH/xgtEz95oADDuiDtkz78Ic/3L3qVa+qZ499Xx9LwwWyL3lYQ3pds3+TtjvMl8/1sZ3jP/udddXpYhe7WF/nZdpzn/vc7uijjy4f+325173u1QeQo4l/f5PjIsf+l7/85eGs/vOi53xWssjfnATeT3ziE8eWbThxWFfTnGcnOtGJ+r8ZOcaSEqA/4xnPWLPqeQ3q4zZ/Uw8++OA16530Ydp89bHxhCc8YXRhYRn7/bjHPa4v3i9/+cv+vBmWNX/L7373u3e5OJGUY/ylL31p99WvfnW4qM8ECOxmgT6wGTaUdnMZJm6uRHKtlq80TNIgbjG17rde+f785z+PSNOgWcYxkAbpBS5wgdF6h29Sn4985CP7hs64K/x1L9Iw7/BzrujX6ZSnPGV30EEHdelBmJTSc5DAJA3Qn/70p/1iL37xi/tAJF/2WecDHvCA7vGPf/yaRne9rWEZr3SlK42CmqwwDfZylXq9fKWM97znPUeN4UxLQ6h+7T9M+V/JW5exLsOUq5m42AlPeMLuwAMP7E53utPtssxJTnKS7n73u99oehqMt7vd7Uaf6zeXv/zluyyfBm+dzn/+83f/43/8jy7bGZfyGOkb3/jGXRrahx122Jo6KvuefPe97313CabK+q5+9av3x2gdyIzbXur1bW97W8k2ek1gVwc1mRHv9AamR6nMS2P71a9+9ShfeZNypheppAR506T16jEuOX4TSOZfyv32t799mtV2L3nJS/rgJgtnG2lM/u///b9HeVNPd7rTnUaf0zCvg5o8BS91Miklfy5WlKB7uFx9rA7nDT+PM9idf3PqYyxlq8s+rmxZ5ta3vvWagG/ccvV6kme9VOevj9tZ1jFtvmm2VS9Tl3ua/c7yCWCG3z0p3wMf+MDRhYosl4szn/zkJ/N2lNb7fhsttIVvWi+f9tViB0fr9buZ5dNjs9ixI/eKCKQhVwc1GX6WK5y5kpghade61rX6Rnu+yO585zt3uepd95xkN+sv5wQHuQpep9Oe9rTd2c9+9nrS6H0a0XVQky/B9JCkwZmGcBp8SWkApqH2tKc9rf+c7vinP/3pfXlStjTaM/+Iv/XsbJRyNfFf/uVfRovlavm0DdVkuv71r99ln8alPNCh/GEq8zMsK0P7knKlM8Z1Sp7NTGnE1kFNGrjpEUq6xS1usca/lCN1+M53vrNL4JljIEPYki5xiUv0x8XXv/71/nPs84CK0uhKb86RRx7ZX6FNveYqcY6jpDjEblzgkflZV1KOvwTQpzjFKboEKhnalpTgI487X+93dtLrN2795TjqVzT47wMf+EDfkM3k7N+4wObSl770KJDNI9OHx/hglWM/Zp/qfAkis95SNze84Q27lOW3v/3t2Pz1xPTuvPvd7+7KY9zPd77z9YH6xz/+8X6x9ICWxmvO1+c///mj7Bn6VQc1aaAmgMlxmaGg6XEsx2vef+pTn+q+853vjPLnzSLn/DL+5pTjLWVJ79KwZyk9qqUBmGVmSfn7kON2o7SIwUbr3or50+73uLLlWMvFk/J3Ij01uQAy7kLYuPymESCw+QICm803toUtFkjj4KY3vemoFB/96Ef7nosyIcMH8tS1DG1IIyxXPvNDn6973evKIv1rfUX0i1/8YvfKV75yzfwETrlCO0xpfJThZJn3ohe9qPvMZz4zWixD0D796U/3vQG5up2GcXoHyrCG3PvxH//xH12G0yRd6lKX6tLgXu8HSHPlPj0UWV9S7uMZ7k8/Y8J/CWjSAJ2UUub8q1PMSgP0E5/4RPf//t//q2dv+vsEJiUN6zj1leElqd+S0sBNz0pJCfpyDJRejTSKy/xrX/vao7xpQD/5yU/u78mp82b58js517ve9fpGdBknXpYrr8NjINtOQJ0AIOkmN7lJX7/jhsNlfhrtqeNhcJDepkkpQd6tbnWr/phIMJXG2fC+oDowmiUIrreZY3l49TpD9DIUqQT3CQLHDX2s11PeZ/hjXEqQnYsEaeCf+cxn7gO0slyC/XqYWoKeknIOPeUpT+mHc2Zazp8EV/vvv39/vmVahmxmSFOd5j3nl/U3Jz1KJX3ve9/bpRcxPXwJ2mdN+buQIZvTpHkNpln37l5mlv0elm2fffbph5SW4zDn5rOf/ez+vrzhsj4TILB1Av996XDrtm/LBDZdID0i5apuhrWNu2fgF7/4RX9vQylM3cAr0+orl1nPtKk09rN8hsrUQU1ZRxpq9ZPf6jxZJg3zNA5LSgM1AdC4lB6B3CdRGkW/+tWvdrnfZ1y+eloaPSUoypX71lMa+mV/cxX1Na95zZoiJxgZDn8aHgdpqGSoXkm171WvetUyue8pSUN5mNIAz4MEklIHk66GJ++4YyDbTtmT0pgsAVY/4e//1Q+4GB6jCdpKnnq5kj8GpQcq06585SuXWf1rGuOl1ygT3vve9/bTl/Ff9qsO8sqxNc26kzf3vhWblDPDx+pHvCeYqgPt+OVBASW94AUvGAU1ZVp6SzO9pAR6pQenTJv3nF/W35wSCKY8ZQhpKdsirze60Y1GQeZG5/e8BouUb7PyzrLfdRnSk55e/BLU5NjJRY88bEQiQKAtAYFNW/WhNJsgkGEnJeWq9aSr4LlCncZfUhrJZchQyVuCo3wuy5V5672WYQtZ5h3veMfERd/61reO5o0bXpIhU6XxloZh7tXIF+4w5crzGc5whn5yyplhbbOUNw3mcqN77vUpw36G21nG5zQ+E6SVfze/+c37m63LTbnTbqMeZphhUOP2t27Up5Gcx4gP07e+9a3R0/FS/6UeyrC75FuvwV8PH0tP3bj0oQ99aNzkvuFdl3HcsMZctS/H77B3pv48adhfXfb0/NUpD5IoAUfuG0ywv4yUQCTbKudBDBOoz5JSnjqwz/FZet/SMH/Zy162ZnXpzSkpefMUtHEpQX/qvKT6b0WmzXvO1+tZ5G9OhkiWlLIuI+UiQLlwkmNpGOAPtzGvQb2e9C6WczyveYBLelhz3g7/ztb5Mi95x/1bL1+9jvJ+1v0u+XLuP+xhDxsFgrmold6/4bDFsrxXAgS2VsBQtK31t/XdIFA3ctIwnJTS4Mo4/HJVLsFB3SDKUISS6qvPZdqk17K+zF/vy7AeFlQa0sN15grzwx/+8H4oUoKv+ibzLJsv4DrlSnfud5k2JVCq78vJk8SGV/anXdc0y+WKdN0bUueJR/a3dqnn1+/rBuCkh3nUvSzr9bjFqzTCU74M9yqNqDSic7V2UqqPr7KO4bJ5/PSklH3NfSRJJbgcLpsn7l3oQhfqe2dyTJZjsfQQ5al4wxueyzoSUCToS49GzNLYKw+quOIVr1gW64dpjT7M+CZPmstT85ISKGXYWwmYMi1DOMcFnpm3XsrT+HJvUemVKsvmKWjD9dVBZX0Olzz1a+pj378/MCH5ysWDLDPvOb+svzmnOc1pRkWd9LTG0QJTvsmQ1lIfGZ66UY/NvAZ1cRLcTjrPM3wwTxSr3UveHDvD4YFl3qyvs+531p99z9/b2iBPFBw+eXLWslieAIHNE9Bjs3m21tyIQD28ZKNGfj1Gvx4Gkl2pe0eG9zast6v1l2K9/mGeBFblanw9rr1eLvOHN+XX8+v3aezN8mjd5L3LXe4yukE+V/enCSrqbc7zPoFC/pV9L+tIYJAhdZMsynJ5rY0nNerrgGS4rXpddd1mvWn8l7Re/WWZMhQt79MoG5fq9Q/n12Uvjc/hMrkfrKTyGO8EuSUQWq+HLcdYfaNzHlqQlMDtPOc5T/8+y9Tb6CfO8F/KkqAp/xKgD/cjQUQJFGdYbb/osLcrXt+qelzK+uo626ino66POkDOuuY955f1N6fuuVxGYzr35JSAOwFf7jPaKM1rMFxvOc/r8zDLpOctQ1+HAesw/yKf59nvbC+9VfXflkzLgz0kAgTaFdBj027dKNmSBBLMlKEhdYNj3OrL8JbMGzaI6kZP3QAdt556Wq6ol8ZB1j+pcZwGYGnwDa9Al/XlJurLXOYy5WP/RK98aacxmZSnPqWxm0Z1AoIMV8vjo6dJedx0GdKVBvob3/jGabIttEzujTjiiCNG68j+p/z5nZe8j9s1rnGNsb8NM8r0tzd1L02xqOfnfbEdTh9+rgOprLeu6/r4GObL5zqYqYOcetlhQ6melyE3JZWelPK5vCYwSWCW/cnwszROS4CTZRKUlKFGJU/9mmOkPKQgPSBvetOb+ifzFZ/cB7Zej1a9rnHv8zS6umcyJunByLDDHOPpGcp9G7lKP0uKfR6qUKcETnlgQ3pz6lTbDS9Q1MvlfW0+vI9l3nN+WX9zSrCactam+TxrilXpSUvwWj9Bbr11zWtQrzOueZR+nfIAiVxIKcFvzvnysI6yXIKgSQ9JyXFUD5MreYav8+53vZ6cy+Xczt/b9PLlASUSAQLtCQhs2qsTJVqyQL6E0mhPqoeojNtMvgRLqocuZVp9FXg4r+QZ95p7FUpgk/smytPOhsuWK6mZXjemy3KZX/9mRx7fnPsO0kgtjfk0avOF+5CHPKRvROaKbxqDabyul9KYzxCikjIEbL1ejbLcsl+zzdyTkMZUeVRvfRP4pO3VPVN1Y7Bevq7b9RpE5bHEyZvAJmUqgUTqMcNqhledy3bqbU/q7cqV6fpempI3r+WenryflD9lyU3LCUJzPKXuyjC0DFfaqFcyDeT0UqRBn0Z/hjuVnptsN4HPIilPxBs+FS3ry8MY8vsfSRe/+MX711n+y2/Y1EFnyZsbwvPgjdqrHu5X7jcryw9f656C+jjKcvOe88v4m5MgsAxFS50Pg67hfmz0OT+iWoLXBIJ18Lde3nkN1ltn5uUcyI/HluGzdT2UvDlOJz3N8SIXucia+il5hq/z7ndZT4ZO5v7LPDygnJ8JwuI36Twueb0SILD7BQxF2/3mtribBerGSm6ML1/uw2Lk6nVpOOWpUmlM1KkOPOoegnqZce/rBlceAzwp3eAGNxjNGq4/5br//e8/GtaTdeYX6selDDF57WtfO5qVRxXXj5sezajeJIgowVF+zyOPh97KVAeOdUAyqUx1QzYN6HF1XAe1CU7K/tbrTFBTgtD6GCi9L2lsrjcUpZ5XH3f1Nibds5Q6Lj1mWb42qPPnfRkqlvIkqCkPGhgXUAzz5nM9XC3lyePFk9JTOOuN/X3GKf7LEMrSExT7jXq/6lWmZ6rce5Tpz3nOc0b31cQgj1nPa0m1fc7b+twty+Q1AXT9JLjhAyXqfMNzsl7P8H29/Xn/5lzwghccHcfTBiHDcpTPsStDDdMTXT+opCwz6XVeg0nrq6fX+zXufKyXnef9Ivud7SWYzIWifBek57u+HylP5quDvnnKJw8BAssXENgs39QaGxPI0J26QfX/27sXIFmq+nD858oVeWRBHitBQS4iEAVJgFJvLOQVHoKYisGKMTGkYhUxMUaT/KiUeaigEtFgFKOpGFP6DzGpwtKYCAR5CAjEMsqjghB5wxV8wF4gsAJXBO/fb8sZeufO7t098zq78+mq2Znp6dP97c+ZOdvffpzudd+H2MCJnnry0H0Pj9hYzv94Y6OlO+nJ5Xo9f/nLX+6Mjn+0+ehRZ+RPXsRpGdErVR7i5o/tIf6J5lMhYuMz9nTG6STzDXF6UvtmfnEO+0Kn4eXkITbm210ezzf/YY9vH0Ho3tjstew4vS9veEbSEslc99A9Lt9zpj1d9NiUh3b32+1rO44//vjOzSbztPEciWl2jO9H93coTxsJVuxt7h5i2Tkhiw2qhS4Wj9hy/cf3Nm/Ux00/FzO0e0cLh7zc6IY6z3cx81nKNLHe+UhZ+Mx3Smb3PMM07l2Th0jeote3f/7nf86jmu929KiXh/iNtK9Fi1Oe8k6LPE2sc+zNz0N8f9o9wfXzmx9Em9Puznux9/zJ69L9nNuOGB/3xFpsHfdj0B1Dr/ftJH9zRxp7ld/cuNL17jXfOKU4epjMR2ujnYkjkAudWtprPsYRIDBcAaeiDdfX3CsQiI2cuMdITmiid57YyIqN1dhjGHur40hK3uiK6fOpW5FsxN7w9j/gxe4Vz6sePWVFT1Z5b/zJJ5/c7HGPjZXYwItEp72nPzbk20lJbDDnva0xz7jTda9T1fLy8nNswLznPe9pjkDEP+G4GWHcpHKhpCw2FnMSmOczzOeoh7zusXEeG7FxmlJ7L/FivT/72c821xRFvHF6UqxndL8c13PEDVq7T2mL61Bi73XskY26jw3jfGQrNvzaR8Siq+3onjaS25g2ekqKcVGvcTpXHDXJZWP5kcwutOEePTRd/pPOGSJBiY2v+H7l70eUby873ncPsXEVR9Xie5GTkliXhZKh9jxiAz6++917nLsT6naZxb6Oe7jk61aiTuN1dzLX64L/+eb/5je/ubOO7cQ7fj/Rk1beURDXPsR3JSc00QV0Ps0plh/XeMQpWPF5XHMXiW57/fM1P4P4zffT5hx77LHN9z8c8xA7RNqnoebx7VMf4/sT08Q1TnE6YK8hvuuLuVZnEAbt5cfGf/6dx/g4Khrf3XykMMa1dyTE+0EOi13vzS0zfjPR02S++XGsRyQ3Z5xxxoLt6ubm63MCBAYnILEZnKU5VSwQG7jxzzpv3MY/1XaykEOPDdrYIMob93H9ys///M/nj5vTX/JpQJ2Ri3gR8/zTP/3TzlGXuPC13b1unkVsuP1/rYvp4x//cccdlz9uNtRjw2UxQ2xcffzjH0+nnHJKs0c/up2ODZ/2/Nvzies22j1mtT8b1utIYNpHyrqXE0c95rsmqXvamC725EdXyLFBHdcWdV9s3i4T0+R7a7THx+u4mWe7t6xIkj796U+nSEgikYhEMeqlXTd5HnFE74ILLshv532OThHi0T1E+TgdcHNDHJ1pf4cXU6Y9z7goO19MHuPjKFH7FKr2tEt5Hfes6b5HTrt8/LYi6V7MEMlKPs0upo/fUXyv83D22Wc3yXo+mvp7v/d76S//8i+bvepxumbUYz7FMxLQ9hG5PI94jiOcOdka1G++tM2JHS/5yF+Occ1PepKLx0JDHHWOjkXiFL9eiU20LZH8L2YYlEFeVuwMWOh3Hgn5sDorWcp653gXeo7ONeKo9m/8xm80k8U1XHFEPLrGNxAgMH4Bp6KNvw5E0EOgfVRhsadNtGfTLh/jYx5nnXVW889zvvnFP9fTTz89RU9dvYaY50c/+tE5G7zt6fIpCjGue/mxN/1d73pXWuiUktj7H3uV87UVsfEcG9J5iA21XhfStpfVjiHKxdGifPQp3scGZ94T3J425hEb7r2G9vzbr7unbX/Wft09XXu53Z/F+9jwDYPY+I0N2aUMce1Fry5sc8Ka5xVHUy655JL8tvMc0/3rv/5rs0HcGfnUi0iaTjvttE79dH8eZeMoTtznYqF1/NznPte5CWj3POIozgc/+ME5o9vzeuKJJzqfxfe0/V1u9x7V9m+/7hT+yYtIjNrlY6926dCOsdc8ok7j9xUdE8R3vJ009po+xuWjaPnz2KDs/v3ERmscwcxD7EFvJ2uRYEZyH9P1GiJJit7Bok56DWFX+psP237bnF4xLXZcd53E77t7XMyr/Z3q9V0pNei1rHbsMd9oF6OO2keS2+Xa38922Xjdnm6+1zFd6XpH2fmWHztc2qcYx9HaXjspYh4GAgRGK7DqJ+fxblzMaS2jDeunS8t7rWqNL/eQks/tH4fRQsus3W+Q8eVuQuN0q80Nsac+Tk2Jvd2xFzdOzYiNpnyBeLt87AWNf1qxURY94PT6x9+efjGv41z/OHc9jh7F3v84pSj2FuejRIuZh2kWFogN3DhFKeo5TruKU5QimYlz5GOI19FzXJyKFMlenC4V9RvJZfuIwHxLiflH/cVe9PjeRB1GEjnf9yNOXcu9c0XiEx08RIIZRyNioztOj4rvYb7Z5nzLHfT4M888s3PtSRzpqLWtHcR6xxHL+M1H19OxkyB+8+2L1/MyhvGbX0qb8773va85YhMb67kXuRzbfM/xPc7dKccR3bhpaT/DMAz6iWdUZSMRjSHun9XvMMj/b/3G0qt87fHZvupVa4sfV3v9DjM+p6It/ntiyhUiEHvh4jqWxVyUnnvFGeSqx4ZzbETrKnSQqnPnFb0XdV+8H0fAuofYsO2+B0r3NL3ex/zj+o5ed0vvNX33uEiA4uhD9xGI7umG+T4Sv3xBfSRVKzmpCcfYORGPzQ3D+M0vpc3ZXHyj+HwYBqOI2zIIECDgVDTfAQIECEyYQBxBaF/z0Ou0vAkjsboECBAgsAIEHLFZAZVoFQgQILAYgehhLE67i9Pi8n1k4kjNOI8cLSbuSZomTpuMTjUWcx1Sdolp4xqwGEqPIuZ5eSZAgMByFpDYLOfaEzsBAgSWIBDdA8d1Ze2hfT+Y9nivxyOwua6+e0UV14xFxxkGAgQITLqAxGbSvwHWn8CECMR1DtELUwy9LhofJkNcz5W7JI6N0HENcRPEuDA8LOJITfSyFx0mGAgQIECAwEoQkNishFq0DgQIbFYgLtiPLrfHMcx376BRx5J7hRv1ci2PAAECBAiMQkDnAaNQtgwCBAgQIECAAAECBIYqILEZKq+ZEyBAgAABAgQIECAwCgGJzSiULYMAAQIECBAgQIAAgaEKSGyGymvmBAgQIECAAAECBAiMQkBiMwplyyBAgAABAgQIECBAYKgCEpuh8po5AQIECBAgQIAAAQKjEJDYjELZMggQIECAAAECBAgQGKqAxGaovGZOgAABAgQIECBAgMAoBCQ2o1C2DAIECBAgQIAAAQIEhiogsRkqr5kTIECAAAECBAgQIDAKAYnNKJQtgwABAgQIECBAgACBoQpIbIbKa+YECBAgQIAAAQIECIxCQGIzCmXLIECAAAECBAgQIEBgqAISm6HymjkBAgQIECBAgAABAqMQkNiMQtkyCBAgQIAAAQIECBAYqoDEZqi8Zk6AAAECBAgQIECAwCgEJDajULYMAgQIECBAgAABAgSGKrA65j41NTXUhfQ781rj27BhQ7NqtcaX3cWXJTwTIECAAIGFBQb5P3OQ81o46rJPa43P9lVZfXaXqrV+c5zDiM8Rm6zrmQABAgQIECBAgACBZSvQHLGZnZ2tcgVyJldrfNPT043bzMwMvwKB2uu3YJUUIUCAAIFlLjCIbY7a/7/VHp/tq/5+RLXX7zDjc8Smv++O0gQIECBAgAABAgQIVCAgsamgEoRAgAABAgQIECBAgEB/AhKb/vyUJkCAAAECBAgQIECgAgGJTQWVIAQCBAgQIECAAAECBPoTkNj056c0AQIECBAgQIAAAQIVCEhsKqgEIRAgQIAAAQIECBAg0J+AxKY/P6UJECBAgAABAgQIEKhAQGJTQSUIgQABAgQIECBAgACB/gQkNv35KU2AAAECBAgQIECAQAUCEpsKKkEIBAgQIECAAAECBAj0JyCx6c9PaQIECBAgQIAAAQIEKhCQ2FRQCUIgQIAAAQIECBAgQKA/AYlNf35KEyBAgAABAgQIECBQgYDEpoJKEAIBAgQIECBAgAABAv0JSGz681OaAAECBAgQIECAAIEKBCQ2FVSCEAgQIECAAAECBAgQ6E9AYtOfn9IECBAgQIAAAQIECFQgILGpoBKEQIAAAQIECBAgQIBAfwISm/78lCZAgAABAgQIECBAoAIBiU0FlSAEAgQIECBAgAABAgT6E5DY9OenNAECBAgQIECAAAECFQhIbCqoBCEQIECAAAECBAgQINCfgMSmPz+lCRAgQIAAAQIECBCoQEBiU0ElCIEAAQIECBAgQIAAgf4EJDb9+SlNgAABAgQIECBAgEAFAhKbCipBCAQIECBAgAABAgQI9CcgsenPT2kCBAgQIECAAAECBCoQkNhUUAlCIECAAAECBAgQIECgPwGJTX9+ShMgQIAAAQIECBAgUIGAxKaCShACAQIECBAgQIAAAQL9CUhs+vNTmgABAgQIECBAgACBCgQkNhVUghAIECBAgAABAgQIEOhPQGLTn5/SBAgQIECAAAECBAhUICCxqaAShECAAAECBAgQIECAQH8CEpv+/JQmQIAAAQIECBAgQKACAYlNBZUgBAIECBAgQIAAAQIE+hOQ2PTnpzQBAgQIECBAgAABAhUIrI4YpqamKghl/hBqjW/Dhg1N0LXGl0XFlyU8EyBAgACBhQUG+T9zkPNaOOqyT2uNz/ZVWX12l6q1fnOcw4ivSWzyAjwTWO4CZ5111nJfBfETIECAAAECBAgUCDSJzezsbEHR4RfJmVyt8U1PTzcIMzMzw8coWELtfrXHV0CuCAECBAgsc4FBbHPU/v+t9vhsX/X3I6q9focZnyM2/X13lK5E4I/+6I8GHskwf3iDCLb2+Pxj6q+WB12/p575iSagU095c3+BPVV60PENJKjWTGqPz++jVVkFL2uv34JVUoQAgQEI6DxgAIhmQYAAAQIECBAgQIDAeAUkNuP1t3QCBAgQIECAAAECBAYgILEZAKJZECBAgAABAgQIECAwXgGJzXj9LZ0AAQIECBAgQIAAgQEISGwGgGgWBAgQIECAAAECBAiMV0BiM15/SydAgAABAgQIECBAYAACEpsBIJoFAQIECBAgQIAAAQLjFZDYjNff0gkQIECAAAECBAgQGICAxGYAiGZBgAABAgQIECBAgMB4BSQ24/W3dAIECBAgQIAAAQIEBiAgsRkAolkQIECAAAECBAgQIDBeAYnNeP0tnQABAgQIECBAgACBAQhIbAaAaBYECBAgQIAAAQIECIxXQGIzXn9LJ0CAAAECBAgQIEBgAAISmwEgmgUBAgQIECBAgAABAuMVkNiM19/SCRAgQIAAAQIECBAYgIDEZgCIZkGAAAECBAgQIECAwHgFJDbj9bd0AgQIECBAgAABAgQGICCxGQCiWRAgQIAAAQIECBAgMF4Bic14/S2dAAECBAgQIECAAIEBCEhsBoBoFgQIECBAgAABAgQIjFdAYjNef0snQIAAAQIECBAgQGAAAhKbASCaBQECBAgQIECAAAEC4xWQ2IzX39IJECBAgAABAgQIEBiAgMRmAIhmQYAAAQIECBAgQIDAeAUkNuP1t3QCBAgQIECAAAECBAYgILEZAKJZECBAgAABAgQIECAwXgGJzXj9LZ0AAQIECBAgQIAAgQEISGwGgGgWBAgQIECAAAECBAiMV0BiM15/SydAgAABAgQIECBAYAACEpsBIJoFAQIECBAgQIAAAQLjFZDYjNff0gkQIECAAAECBAgQGICAxGYAiGZBgAABAgQIECBAgMB4BSQ24/W3dAIECBAgQIAAAQIEBiAgsRkAolkQIECAAAECBAgQIDBeAYnNeP0tnQABAgQIECBAgACBAQisWrt27cYBzMcsCBAgQKBigf/37jOb6D502ikVRyk0AgQIECBQLuCITbmdkgQIECBAgAABAgQIVCKwOuKYnZ2tJJy5YUxNTTUjao1venq6iW9mZmZu4JW8q91PfP19UWr38/uos34H1Z7W/v2rPT6/jzp/H/1F9XTp2r9/tcfn9/H0d6nkVe31O8z4HLEp+cYoQ4AAAQIECBAgQIBAVQISm6qqQzAECBAgQIAAAQIECJQISGxK1JQhQIAAAQIECBAgQKAqAYlNVdUhGAIECBAgQIAAAQIESgQkNiVqyhAgQIAAAQIECBAgUJWAxKaq6hAMAQIECBAgQIAAAQIlAhKbEjVlCBAgQIAAAQIECBCoSkBiU1V1CIYAAQIECBAgQIAAgRIBiU2JmjIECBAgQIAAAQIECFQlILGpqjoEQ4AAAQIECBAgQIBAiYDEpkRNGQIECBAgQIAAAQIEqhKQ2FRVHYIhQIAAAQIECBAgQKBEQGJToqYMAQIECBAgQIAAAQJVCUhsqqoOwRAgQIAAAQIECBAgUCIgsSlRU4YAAQIECBAgQIAAgaoEJDZVVYdgCBAgQIAAAQIECBAoEZDYlKgpQ4AAAQIECBAgQIBAVQISm6qqQzAECBAgQIAAAQIECJQISGxK1JQhQIAAAQIECBAgQKAqAYlNVdUhGAIECBAgQIAAAQIESgQkNiVqyhAgQIAAAQIECBAgUJWAxKaq6hAMAQIECBAgQIAAAQIlAhKbEjVlCBAgQIAAAQIECBCoSkBiU1V1CIYAAQIECBAgQIAAgRIBiU2JmjIECBAgQIAAAQIECFQlILGpqjoEQ4AAAQIECBAgQIBAiYDEpkRNGQIECBAgQIAAAQIEqhKQ2FRVHYIhQIAAAQIECBAgQKBEQGJToqYMAQIECBAgQIAAAQJVCUhsqqoOwRAgQIAAAQIECBAgUCIgsSlRU4YAAQIECBAgQIAAgaoEJDZVVYdgCBAgQIAAAQIECBAoEZDYlKgpQ4AAAQIECBAgQIBAVQISm6qqQzAECBAgQIAAAQIECJQISGxK1JQhQIAAAQIECBAgQKAqAYlNVdUhGAIECBAgQIAAAQIESgQkNiVqyhAgQIAAAQIECBAgUJXA6q222irFo+ah9vimp6dr5lO/fdZO7d+/2uPz++jvCzjo+h10fQw6vv60Ni1de3yDro9NBfobU7uf+FZ2/fp9rOz6Hcbv1xGb/r4zShMgQIAAAQIECBAgUIHA6g0bNqTZ2dkKQtk0hKmpqWZkrfHlPQkzMzObBl/BmNr9xNffl6R2P7+POut3UO1V7d+/2uPz+6jz99FfVE+Xrv37V3t8fh9Pf5dKXtVev8OMzxGbkm+MMgQIECBAgAABAgQIVCUgsamqOgRDgAABAgQIECBAgECJgMSmRE0ZAgQIECBAgAABAgSqEpDYVFUdgiFAgAABAgQIECBAoERAYlOipgwBAgQIECBAgAABAlUJSGyqqg7BECBAgAABAgQIECBQIiCxKVFThgABAgQIECBAgACBqgQkNlVVh2AIECBAgAABAgQIECgRkNiUqClDgAABAgQIECBAgEBVAhKbqqpDMAQIECBAgAABAgQIlAhIbErUlCFAgAABAgQIECBAoCoBiU1V1SEYAgQIECBAgAABAgRKBCQ2JWrKECBAgAABAgQIECBQlYDEpqrqEAwBAgQIECBAgAABAiUCEpsSNWUIECBAgAABAgQIEKhKQGJTVXUIhgABAgQIECBAgACBEgGJTYmaMgQIECBAgAABAgQIVCUgsamqOgRDgAABAgQIECBAgECJgMSmRE0ZAgQIECBAgAABAgSqEpDYVFUdgiFAgAABAgQIECBAoERAYlOipgwBAgQIECBAgAABAlUJSGyqqg7BECBAgAABAgQIECBQIiCxKVFThgABAgQIECBAgACBqgQkNlVVh2AIECBAgAABAgQIECgRkNiUqClDgAABAgQIECBAgEBVAhKbqqpDMAQIECBAgAABAgQIlAhIbErUlCFAgAABAgQIECBAoCoBiU1V1SEYAgQIECBAgAABAgRKBCQ2JWrKECBAgAABAgQIECBQlYDEpqrqEAwBAgQIECBAgAABAiUCEpsSNWUIECBAgAABAgQIEKhKQGJTVXUIhgABAgQIECBAgACBEgGJTYmaMgQIECBAgAABAgQIVCUgsamqOgRDgAABAgQIECBAgECJgMSmRE0ZAgQIECBAgAABAgSqEpDYVFUdgiFAgAABAgQIECBAoERAYlOipgwBAgQIECBAgAABAlUJrFq7du3GqiISDAECBAgMXOD/vfvMZp4fOu2Ugc/bDAkQIECAQA0CjtjUUAtiIECAAAECBAgQIECgL4HVUXp2dravmQyr8NTUVDPrWuObnp5u4puZmRkWQV/zrd1PfH1Vb6rdz++jzvodVHta+/ev9vj8Pur8ffQX1dOla//+1R6f38fT36WSV7XX7zDjc8Sm5BujDAECBAgQIECAAAECVQlIbKqqDsEQIECAAAECBAgQIFAiILEpUVOGAAECBAgQIECAAIGqBCQ2VVWHYAgQIECAAAECBAgQKBGQ2JSoKUOAAAECBAgQIECAQFUCEpuqqkMwBAgQIECAAAECBAiUCEhsStSUIUCAAAECBAgQIECgKgGJTVXVIRgCBAgQIECAAAECBEoEJDYlasoQIECAAAECBAgQIFCVgMSmquoQDAECBAgQIECAAAECJQISmxI1ZQgQIECAAAECBAgQqEpAYlNVdQiGAAECBAgQIECAAIESAYlNiZoyBAgQIECAAAECBAhUJSCxqao6BEOAAAECBAgQIECAQImAxKZETRkCBAgQIECAAAECBKoSkNhUVR2CIUCAAAECBAgQIECgREBiU6KmDAECBAgQIECAAAECVQlIbKqqDsEQIECAAAECBAgQIFAiILEpUVOGAAECBAgQIECAAIGqBCQ2VVWHYAgQIECAAAECBAgQKBGQ2JSoKUOAAAECBAgQIECAQFUCEpuqqkMwBAgQIECAAAECBAiUCEhsStSUIUCAAAECBAgQIECgKgGJTVXVIRgCBAgQIECAAAECBEoEJDYlasoQIECAAAECBAgQIFCVgMSmquoQDAECBAgQIECAAAECJQISmxI1ZQgQIECAAAECBAgQqEpAYlNVdQiGAAECBAgQIECAAIESAYlNiZoyBAgQIECAAAECBAhUJSCxqao6BEOAAAECBAgQIECAQImAxKZETRkCBAgQIECAAAECBKoSkNhUVR2CIUCAAAECBAgQIECgREBiU6KmDAECBAgQIECAAAECVQlIbKqqDsEQIECAAAECBAgQIFAisLqkkDIECBAgUL/AqWd+YpMg2+NOPeXNm3xuBAECBAgQWK4Cjtgs15oTNwECBDYj8N9XXTrvFAt9Nm8hHxAgQIAAgYoFJDYVV47QCBAg0I/A7Tf/77zFF/ps3kI+IECAAAECFQusWrt27caK4xMaAQIECBQKPOMZW6S3/8VfpXhuDz/+8ZPprNP/PMWzgQABAgQIrBQBR2xWSk1aDwIECHQJROKy7vZbusamZpykZhMWIwgQIEBgmQs0nQfMzs5WuRpTU1NNXLXGNz093cQ3MzPDr0Cg9voVX0Gltor4fbQwCl4O6vt3043Xpz33ftGcCGJcv+3qoOKbE9gA39Qen99Hf5Vde/2Kr7/69fvoz2+Sv3+O2PT33VGaAAECVQvcfsum19n0Glf1SgiOAAECBAgsQkBiswgkkxAgQGC5Cqy/7/tp/X33dsKP1zHOQIAAAQIEVpqAxGal1aj1IUCAQJfA7bfc2BnTft0Z6QUBAgQIEFgBAhKbFVCJVoEAAQILCbS7dm6/XqiMzwgQIECAwHITkNgstxoTLwECBJYocFvrOpv26yXOxuQECBAgQKBqAYlN1dUjOAIECPQv8OMnn75fTft1/3M2BwIECBAgUI9A091zPeGIhACBUQusXr06rVmzJm299dZpm222mfM837hnPvOZow7T8voWeKyZw0c+8pG+52QGoxX40Y9+lB577LHm8eijj855jvHd4+666670xBNPjDZISyNAgEAFAhKbCipBCARGKbDzzjs3iUwkM/HYbbfdRrl4yyJAYIkCsSMhHtttt92iS65bty7deeedzePuu+9ODzzwwKLLmpAAAQLLVUBis1xrTtwEFiEQR2P22GOP9PznP7/z2GmnnTYpuWHDhnTvvT/pBnj9+nT//fenBx98MD388MPNTRxjA8lAgEBdAvG7jpvwRbKzww47pB133DHFTQ132WWXtNVWWzW/+5jm8MMPbwKPG0lHohNJTjzuueceR3XqqlLRECAwAAGJzQAQzYJATQJbbLFFOvDAA9NBBx3UHJGJ08vaw+OPP95s4ETCkjdw7M1tC3lNoH6BhXY4RJITR2J33333JsHZc889m6QnEp+Xvexlzco98sgjKeZx7bXXpuuuuy492boOq/61FyEBAgR6C0hsersYS2DZCcSe2khmIql5znOeMyf+22+/Pd18883plltuaZKaOR96Q4DAihKIHRXxuP766zvrFcnNPvvsk/bdd9+01157pW233Ta9+MUvbh7HHHNMk9xEkhNHbg0ECBBYrgISm+Vac+Im8JTA/vvv3zlCs2rVqo7LjTfe2GzYfO1rX+uM84IAgckUyNfbXHjhhQ3A2rVr0wEHHJD222+/ZkfIsccemyLByUdwFjoiNJmC1poAgeUgILFZDrUkRgJdAttvv32KDZOXvOQlcy7+j42Ra665Jl1xxRVp48aNXaW8JUCAwE8FYodHPGJnyKGHHpoOPvjg5rS1eI5HXIPzzW9+s5nmoYcewkaAAIFlISCxWRbVJEgCPxWIHs3iYuA45ax97UzeSIm9sgYCBAgsViB2gHzlK19pHnG6WuwwiUdcoxOPww47rDmKc/nllzediyx2vqYjQIDAOAQkNuNQt0wCBQKHHHJIc6pI7vI1ejKLIzOXXXZZcx+LglkqQoAAgY5APl3tP/7jP9IRRxzRHMmJHSjR9sRpaxdddFG66qqrOtN7QYAAgdoEJDa11Yh4CHQJxL1mjj766OZc+PgoEppLL7005XPluyb3lgABAn0JxA0/zz///OYR194ceeSRTbfSr3vd69KLXvSidPHFF6e4CaiBAAECtQlIbGqrEfEQeEogum2OhCYe8TqGSy65JJ177rlPTeGJAAECwxWIHSjxeM1rXpOOOuqoZgfLz/3czzXJTSQ4uokerr+5EyCwNAGJzdK8TE1gJALRU1EkNHG0JoboEODss89uXvtDgACBUQvEDpV4nHTSSU3nAq961atSTnCiB0YDAQIEahCQ2NRQC2Ig8JRAXD8TXa7GOe0xfO9730vnnXdeuuGGG56awhMBAgTGJxA7WKJL6BNOOKHZ8XLyySc3193E9TcPP/zw+AKzZAIECPxEQGLja0CgEoHo6ez4449P0fNZDE47q6RihEGAwByB2NESj3x6WuyIiaM3F1xwQXN0ec7E3hAgQGCEAhKbEWJbFIH5BF75ylemE088sfn4+9//fvrc5z6Xbr311vkmN54AAQJjF4hT02666aam7dp1113Tb/3Wb6Uddtih2Skz9uAEQIDARAo8YyLX2koTqEjguOOO6yQ1X/3qV9P73/9+SU1F9SMUAgTmF4gdMGeccUaKtiuGOEXt137t1+Yv4BMCBAgMUUBiM0RcsyawOYHoPjW6U43hnHPOaR6bK+NzAgQI1CbQbr9e8YpXpDe/+c21hSgeAgQmQEBiMwGVbBXrE3jWs56V3vrWtzadBKxfvz6dddZZnT2e9UUrIgIECGxeII7aRFsWbVrc7+bP/uzP0rbbbrv5gqYgQIDAgAQkNgOCNBsCixXYaaed0qmnnppe+MIXpptvvjm9973vTXfcccdii5uOAAEC1QpEWxZtWrRtu+yyS3r3u9+ddtttt2rjFRgBAitLQGKzsurT2lQusMcee6R3vvOdaeutt07f+MY30t/93d9VHrHwCBAgsHSBaNuijdtyyy3TKaeckg444IClz0QJAgQILFFAYrNEMJMTKBWI887/+I//uCl++eWXp8985jOls1KOAAEC1QtEGxdtXQxvetOb0mGHHda89ocAAQLDEpDYDEvWfAm0BOIfeu4pKG5k94UvfKH1qZcECBBYmQLR1kWbF8NrX/vaFDt4DAQIEBiWgMRmWLLmS+ApgTgFI/6hx/ClL30pnX/++U994okAAQIrXyDavGj7YogdPHFKroEAAQLDEJDYDEPVPAk8JRAXzcYpGDHEXsu4M7eBAAECkyYQbV8+chOn5EYnKgYCBAgMWkBiM2hR8yPwlEB0c/q2t72teRfnmTtS46tBgMAkC0QbmK+5iQ4Fott7AwECBAYpILEZpKZ5EWgJRFITPQJFz0CuqWnBeEmAwMQKRFsYbWL0DHnyySdPrIMVJ0BgOAISm+G4muuEC8Rdt+MeDnEvB72fTfiXweoTIDBHINrEaBvjXl6ve93r5nzmDQECBPoRkNj0o6csgR4CcXFs3HU77r7tPjU9gIwiQGDiBaJtjDbykEMOSccdd9zEewAgQGAwAhKbwTiaC4FG4Kijjup0Z/ov//IvVAgQIEBgHoHcRh577LHpla985TxTGU2AAIHFC0hsFm9lSgILChx88MHphBNOaKY555xz0h133LHg9D4kQIDAJAtEGxltZQwnnnhiOuiggyaZw7oTIDAAAYnNABDNgsB2223XOZ3iq1/9aoqHgQABAgQWFmi3l8cff3yKttRAgACBUoFVa9eu3VhaWDkCBH4qEEdqXv7yl6fvfe976YwzzuphmHoAACuuSURBVMBCgAABAksQeMc73pF23XXX9N///d/pvPPOW0JJkxIgQOBpAUdsnrbwikCRwL777tskNVH485//fNE8FCJAgMAkC+S2M3YQRZtqIECAQInA6ig0OztbUnboZaampppl1Brf9PR0E9/MzMzQLUoWULvfSohviy22aHr1ifq55JJL0q233lpSVcoQIEBgogWi7Yw2NDpgiZ7SrrvuuvTkk0/Oa7IS/n/Mu3Ij+KB2P9tX/X0Jaq/fYcbniE1/3x2lJ1zg6KOPTmvWrGlOQTv33HMnXMPqEyBAoFwg2tA4nTfa1GhbDQQIEFiqgMRmqWKmJ/CUQPufr3PCfS0IECDQv0BuS/NOo/7naA4ECEySgMRmkmrbug5UIP7xxqlo11xzTbrhhhsGOm8zI0CAwCQKRFsabWq0rY7aTOI3wDoT6E9AYtOfn9ITKhDngO+3337N2p999tkTqmC1CRAgMHiB3KZGGxttrYEAAQKLFZDYLFbKdASeEth5553TMccc07yLi10NBAgQIDBYgdy2Rlsbba6BAAECixGQ2CxGyTQEWgKHH354cxO5DRs2JB0GtGC8JECAwIAEom2NNjZu2BltroEAAQKLEZDYLEbJNASeEth+++3TQQcd1Ly79NJLuRAgQIDAkARyGxttbrS9BgIECGxOQGKzOSGfE2gJHHjggWmbbbZp9iReeOGFrU+8JECAAIFBCkQbG0dtos2NttdAgACBzQlIbDYn5HMCLYH8z/WKK65ojfWSAAECBIYhkNva3PYOYxnmSYDAyhGQ2KycurQmQxbYf//90x577NEs5bLLLhvy0syeAAECBHJbG21vtMEGAgQILCQgsVlIx2cEWgJ5j+HXvva19Oijj7Y+8ZIAAQIEhiEQbW20uTHkNngYyzFPAgRWhoDEZmXUo7UYssAuu+zS6TQg/5Md8iLNngABAgR+IpDb3OhEINpiAwECBOYTkNjMJ2M8gZZA/ENdtWpVWrduXbrzzjtbn3hJgAABAsMUiDY32t5og3OvlMNcnnkTILB8BSQ2y7fuRD4igS222KJzCsTVV189oqVaDAECBAhkgWuuuaZ5GaejRZtsIECAQC8BiU0vFeMItATiH+lznvOcZsyVV17Z+sRLAgQIEBiFQO4dLdpi19qMQtwyCCxPAYnN8qw3UY9QIJ/6cOONN6aNGzeOcMkWRYAAAQIhEG1vtMEx5Da5eeMPAQIEWgISmxaGlwS6BeKUh9zF8/XXX9/9sfcECBAgMCKB3AavWbMmrV69ekRLtRgCBJaTgMRmOdWWWEcu8NznPjdtu+22zXJzzzwjD8ICCRAgQKDTO9o222yTdtttNyIECBDYREBiswmJEQSeFnje857XvLn99tufHukVAQIECIxFILfFuW0eSxAWSoBAtQISm2qrRmA1CMQRmxhuvvnmGsIRAwECBCZaILfFjthM9NfAyhOYV0BiMy+NDwik9PznP79huOWWW3AQIECAwJgFclvsiM2YK8LiCVQqILGptGKENX6BZz/72WmnnXZKjz/+uJtyjr86RECAAIGmLY42eYcddkg77rgjEQIECMwRkNjM4fCGwNMCeY9g3PXaQIAAAQJ1COQ2effdd68jIFEQIFCNgMSmmqoQSG0C+Z/munXragtNPAQIEJhYgdwm5zZ6YiGsOAECmwhIbDYhMYLATwXy/WvuvvtuJAQIECBQiUBuk/M1kJWEJQwCBCoQkNhUUAlCqE8gbv6We92555576gtQRAQIEJhQgdwm77rrrm7UOaHfAatNYD4Bt+6dT8b4iRaIO1vHsGHDhvTAAw80r/0hsBiBww47LG233XbNpJdeeml65JFHFiy2//77pz333LMzzXe+85107bXXdt57QYDAXIFok6Nt3mqrrVK01bfddtvcCbwjQGBiBSQ2E1v1Vnwhga233rr5+N57711oMp8R2ETg1a9+dXrWs57VjL/++usXTGzWrl2b3vCGN3Tm8eMf/zj97d/+bee9FwQI9BaItjlOF85tde+pjCVAYNIEnIo2aTVufRclsM022zTTzczMLGp6ExFYqkCvpOYjH/lIuuOOO5Y6K9MTmDiB9evXN+uc2+qJA7DCBAj0FHDEpieLkZMukPcCOg1t0r8Jw1n/7qTmySefTH/zN3+T8rUDw1mquRJYOQL3339/szK5rV45a2ZNCBDoR0Bi04+esitWIO8FfPDBB1fsOlqx8Qh0JzU/+tGP0plnnpm+//3vjycgSyWwDAVy25zb6mW4CkImQGAIAhKbIaCa5fIXyHsBH3744eW/MtagGoHupCbuoP7BD34wLeaUxy233LLpZCCuK/jBD36Qbr/99jSqa8Ce8YxnpBe96EXpuc99bnriiSfSt7/97ebo0g9/+MNqbAUyWQK5bc5t9WStvbUlQGA+AYnNfDLGT7RA/mc5Ozs70Q5WfnAC3UlNJAUf+MAHUj6lZr4lTU1Npbe85S1NUtE9zcaNG9P555+fLr744u6P0nvf+95O72x/8id/kuJ0t+7hV37lV9IRRxzRjP7iF7+YvvzlL3dPko499tjmscUWW2zy2Q033JDOPvvs1J3g/M7v/E76hV/4hWb6T33qU+l//ud/Nin7whe+MP3hH/5hM/7WW29NH/vYxzaZxggC8wnktjm31fNNZzwBApMloPOAyapva7tIgXx6Q77D9SKLmYxAT4HupOaxxx5Lf/VXf7XZpGafffZJp512Ws+kJha0atWqdMIJJ6RIXOKoSnt45jOf2Xnb/Vn+oD1N+3X+/A/+4A/S8ccfn3olNTFNdFV9+umnp5122ikXaZ7j6FIe4p5QvYb2+Pb0vaY1jkC3QG6bc1vd/bn3BAhMpkDv/ziTaWGtCXQE7AXsUHjRp8Av/uIvpl//9V/vzCWObkRSk0+l6XzQ9SKSkTe96U2dpCK6go774txyyy1p++23Ty9/+ctTHPWIIU5PiyMrF1xwQddcyt/+8i//corEKg9x6tuVV16ZYk/53nvvnY455pgmmYqEKI7QxHVCBgKjFtBWj1rc8gjULSCxqbt+RDcmAXsBxwS/whYbSU082kMkLL1OC2tPE69/6Zd+qXOPjuhgIK7Fue+++zqTff3rX2+O1hx99NHNuEg0LrvssubGhZ2JCl/EjQ+PPPLITuk41e28887rvI8bIv7Xf/1XczQp1mf33XdPL3nJS9I3v/nNzjReEBiFgLZ6FMqWQWD5CMw9d2H5xC1SAkMVsBdwqLwTM/PupCZWPI5w/P7v//5mDV75yld2pokjMe2kJn8QyUZ0JBBDJBhxFGcQw+GHH96c5hbzeuihh+YkNXn+ccTpoosuym/TgQce2HntBYFRCWirRyVtOQSWh0BzxCYuTq15qDW+DRs2NGy1xpfrVHxZYvHP9gIu3sqUmxeIDgLiVK6XvexlzcRxhOOoo45Kl1xyybyF8+82Ogi4/PLL553uiiuuaK6DiQmi17JeQ3yfozez7uFZz3pW96jmfZxqlodvfOMb+eUmz9EpwKte9apm/M/+7M9u8rkRBIYtEN/t/FsZ9rJK5l9zbLE+tcZn+6rk27ZpmVrrN0c6jPicipZ1PRMgQGAIAtEl81//9V83p5/FNSvPfvazm6XERf//+7//m7773e9ustTYWIsjMDE8+uijC5661r6p5y677LLJvGLEe97znp7j5xu5ww47dD467LDDOglZZ+RTL6LzgjzsuOOO+aVnAgQIECAwFoEmscndJo4lggUWmjO5WuObnp5uol/MPSgWWM2hfVS7X83xxcbkdtttN7S6MePJEPjOd76TPvShD3USk49//OPpz//8z5vTvCIpiF7H3vWud3U+zyo777xzfpmiB7WFhnwqWkzzMz/zMwtNuujP4hqbPMSpc716TMuf52c9m2UJz6MUiLa6xm2Emv+/Rf3UHp/tq/5+RbXX7zDjc8Smv++O0itUIDYmJTYrtHJHuFrnnHPOnKQlrpM599xzU/Q4FkMkIm984xvTP/3TP82Jav369Z33m7uGoJ3MtJOczgx+8uKqq66aE0f+bL/99kvtJCqPj+tqtt122+Zt7LhZzI1A51t2nqdnAsMQ2FziP4xlmicBAvUKSGzqrRuRjVEg9gIaCAxDIG6CedBBB6XddtutmX28vvbaa+f0KBbfv+jeOU5Hi9PS4j4y8/Wk9rznPa8T5nwJyL//+7+n6Fmte4j5HnLIId2jm0QmX68Tva+1OwnYZGIjCIxRQFs9RnyLJlChgF7RKqwUIY1fwF7A8dfBSo7g7//+7+ckKnEfmHxoPq93PgISp6wdeuihefQmz+3Pel2vs0mBRYxoz2eh3s4OPvjgdNZZZzWPt73tbYuYs0kIDFZAWz1YT3MjsNwFJDbLvQbFPxSBvBcwbnxoIDBogbgm4DOf+UxntnHk5C1veUvnfbyI+8Tk4fjjj0877bRTftt5jh7JckIUR3ji6MoghugJLXpjiyGO3MRRpe4hTpF7/etf3xl99dVXd157QWDYArltzm31sJdn/gQILA8Bp6Itj3oS5YgF8l7AvNE44sVb3AQIxOlnL33pS9OLX/ziZm0jgYhE5Utf+lLzPk7/iptkRpfMcWH+X/zFXzSnhN18881p++23b+5Zk8tGgTjFLX9vmxn08efBBx9MV155ZedI0W//9m+n/fffP0XCE0nZXnvtlV796lc3scVi4ujSfN1CH3HEESmu5eke2r2oRVfRJ510UpOY3XTTTd2Tek9gE4HcNg/qO7/JAowgQGBZCkhslmW1CXrYAvmfpQ4Ehi092fP/9Kc/nd73vvd1EoTjjjsu3XDDDSm6cI4jMPH57/7u7zbX2sRRnfg8Ht1DnDoWN/Ec5BDX5UQykxOQOO0sHt1DXPvz4Q9/uOc1PDFt3LMnHgsNkbzFvCNpktgsJOWzLJDb5txW5/GeCRCYbAGnok12/Vv7eQTy6Q3t+3nMM6nRBOYIREKSh3w6V37f/fz444+nf/iHf5gzOhKZPHzrW99Kp512Wore1HoNMf84svOBD3xgzjU7MW07jvbr9nzaHRK0X8c08T7uf3PZZZe1i8x5HUeP3v3ud6d2L2657JwJvSEwYIHcNue2esCzNzsCBJapgCM2y7TihD1cgbwXsNd1DcNdsrkvd4F3vOMdS1qF2267Lb397W+ft8z//d//pdNPP73pHe0FL3hBWrNmTXPq1x133NE5stOrcNwvZ3PDv/3bv6V4zDdE4hRHbs4///zmqEssO3ppW7duXbrzzjubOHqV/cd//Mdeo3uOi9PU2slcz4mMJNAlkI8k5ra662NvCRCYUAGJzYRWvNVeWCDvBex1j4+FS/qUwHAE4jsZp6nFY9RDdBUdiVQ8DARqEMg3cMxtdQ0xiYEAgfELOBVt/HUgggoF8l7AXXbZpcLohESAAIHJFshtc26rJ1vD2hMgkAUcsckSngm0BO66667m3VZbbdVcPP3AAw+0PvWSAIFBCcT1OXEtUQxxWp6BwOYE4jS0aJtjyG315sr4nACByRCQ2ExGPVvLJQo88cQTzfULcXf4eEhslghocgKLFLj33ntT3LDUQGCxAtEmx/C9730vRVttIECAQBZwKlqW8EygSyAukI5hc13VdhXzlgABAgSGKJDb5G9/+9tDXIpZEyCwHAUkNsux1sQ8EoG77767WU6+w/VIFmohBAgQILCgQG6Tcxu94MQ+JEBgogQkNhNV3VZ2KQLf+c53msn33HPPpRQzLQECBAgMUSC3yRKbISKbNYFlKiCxWaYVJ+zhC8T9Q+6///605ZZbpvyPdPhLtQQCBAgQmE8g2uJokx988EHXPs6HZDyBCRaQ2Exw5Vv1zQvkc7j32WefzU9sCgIECBAYqkBui/MR9aEuzMwJEFh2AhKbZVdlAh6lwHe/+91mcfvuu+8oF2tZBAgQINBDILfF99xzT49PjSJAYNIFJDaT/g2w/gsK5L2Ce+2114LT+ZAAAQIEhi+Q2+LcNg9/iZZAgMByEpDYLKfaEuvIBeKIzSOPPNIsd+3atSNfvgUSIECAwE8Fchv86KOPNvcZ40KAAIFuAYlNt4j3BFoCTz75ZMr3sznggANan3hJgAABAqMUyG3wXXfd5caco4S3LALLSEBis4wqS6jjEbj22mubBe+3335p1apV4wnCUgkQIDDBAtH2RhscQ26TJ5jDqhMgMI+AxGYeGKMJZIHrrrsu3Xfffc3bQw89NI/2TIAAAQIjEshtb7TF0SYbCBAg0EtAYtNLxTgCLYE4HS3/Iz344INbn3hJgAABAqMQyG1vtMXRJhsIECDQS0Bi00vFOAJdAnHqw8aNG9Mee+zhZp1dNt4SIEBgmAJxU85oe6MNdhraMKXNm8DyF5DYLP86tAYjELj33ns7/1BzzzwjWKxFECBAYOIFcpsbSU20xQYCBAjMJyCxmU/GeAJdAvl0tPgnu80223R96i0BAgQIDFog2tqc2OQ2eNDLMD8CBFaOgMRm5dSlNRmywA033NDp+vmII44Y8tLMngABAgRyWxvd7kcbbCBAgMBCAhKbhXR8RqBLIO8xzD30dH3sLQECBAgMUCC3tbntHeCszYoAgRUoILFZgZVqlYYnEP9c467XW221VTr22GOHtyBzJkCAwIQLRBsbbW20uRKbCf8yWH0CixSQ2CwSymQEQuChhx7qdCJw5JFHQiFAgACBIQnkNjY6DYi210CAAIHNCUhsNifkcwJdApdffnl6+OGHmz2Jr3nNa7o+9ZYAAQIE+hWItjWO1kRbG22ugQABAosRkNgsRsk0BFoC69evTxdddFEz5qijjmp94iUBAgQIDEIgt63R1kabayBAgMBiBCQ2i1EyDYEugauuuirdeOONzdiTTjqp61NvCRAgQKBUILep0cZGW2sgQIDAYgUkNouVMh2BLoGLL744Pfnkk+nggw9O+++/f9en3hIgQIDAUgWiLY02NdrWaGMNBAgQWIqAxGYpWqYl0BK46667Ov94TzjhhNYnXhIgQIBAiUBuSyOpiTbWQIAAgaUISGyWomVaAl0C+Z/vrrvumnQk0IXjLQECBJYgEG1otKXtnUZLKG5SAgQIpNXR60g8ah5qj296erpmPvXbZ+1s7vv39a9/Pa1ZsybFxa433XRTuvXWW/tcouIECBCYLIG99967aUNjraNN3XHHHRcFsLn2eVEzGeJE4usP1/ZVf36T+P1zxKa/74zSBJpE5uqrr24kTjzxRCIECBAgsESB3HZGW2rn0BLxTE6AQEdg9YYNG9Ls7GxnRE0vpqammnBqjS/vSZiZmamJrRNL7X4rKb4vfvGLzVGbOI3i9a9/fTrnnHM69eAFAQIECMwvEG1mtJ3RrXO0pXHvms0NK+n/x+bWdRif1+5n+6q/Wq+9focZnyM2/X13lCbQCMQ/4gsuuKB5/YpXvCLFw0CAAAECCwu028v//M//XFRSs/AcfUqAwCQLSGwmufat+0AFrrnmmnTeeec184w9kC94wQsGOn8zI0CAwEoSiDYy2soYPv/5z6drr712Ja2edSFAYAwCEpsxoFvkyhW45JJL0le/+tVmBX/zN39z5a6oNSNAgECfArmNvPDCC9OVV17Z59wUJ0CAQEoSG98CAgMW+OxnP5u+9a1vpZ133jm95S1vGfDczY4AAQLLXyDaxmgjr7rqqs5pvMt/rawBAQLjFpDYjLsGLH9FCnziE59I9957b9p3333TG9/4xhW5jlaKAAECJQLRJkbbeNttt6XPfe5zJbNQhgABAj0FJDY9WYwk0L/ARz/60fT444+nl770pem1r31t/zM0BwIECCxzgWgLo0187LHH0ic/+cllvjbCJ0CgNgGJTW01Ip4VI/DII4+kSG5iOPzww9OrX/3qFbNuVoQAAQJLFYg2MNrCGM4888z0wx/+sHntDwECBAYlILEZlKT5EOghcM8996RPfepTzSfHHHNMOu6443pMZRQBAgRWtkC0fdEGxvDhD3843X///St7ha0dAQJjEZDYjIXdQidJ4Prrr09f+MIXmlV+1ate5cjNJFW+dSVAoGnzou2LITpXWbduHRUCBAgMRUBiMxRWMyUwV+ArX/lK8w89xsZeS9fczPXxjgCBlSkQbV0+UhM7eHJ3+Ctzba0VAQLjFpDYjLsGLH9iBOIfepyCEUOcZ663tImpeitKYCIFoo3L19TEKbmxg8dAgACBYQpIbIapa94EugTiFIz3vve9TY9A0TOQ+9x0AXlLgMCKEIi2Ldq46BkyOgqIU3INBAgQGLaAxGbYwuZPoEsgLpo99dRTm3s4xL0c3vnOd6YXvOAFXVN5S4AAgeUnEG1ZtGnRtsW9vE477bQUnagYCBAgMAoBic0olC2DQJdAdHP6sY99rLnrdtx9++1vf3t6xSte0TWVtwQIEFg+AtGGRVsWbdq3vvWt9P73vz9Ft/cGAgQIjEpg9agWZDkECGwqEHfdjn/8xx57bHr961+fdt9993TOOedsOqExBAgQqFgg2q+8cyauJ4zezwwECBAYtYDEZtTilkegS+CCCy5IP/jBD9KJJ57YbBjsueee6fOf/3y69dZbu6b0lgABAnUJ7L333k3bteuuuzaBnXfeeemSSy6pK0jRECAwMQISm4mpaitas8CVV17ZHLk5/vjjU2wgvPWtb202Ds4999yawxYbAQITLPCa17wmHXXUUY3A+vXrU+ykueaaayZYxKoTIDBuAYnNuGvA8gk8JXDttdc2HQrEPR8OOeSQZoNhv/32S7EH9IYbbuBEgACBKgT233//dMIJJzQ7YSKgq666Kl100UXp4YcfriI+QRAgMLkCEpvJrXtrXqFAbBjEdTdx4e3RRx+d1qxZk04++eRmL+jZZ59dYcRCIkBgkgROOumkdPDBBzerfNddd6WLL7443XjjjZNEYF0JEKhYQGJTceUIbXIFYkPhpptuapKbSHBiQyIece6609Mm93thzQmMS6B92tmTTz7ZJDSR1MRrAwECBGoRkNjUUhPiINAlEBsMX/rSlzoJTpyWFuezx2lql156abrwwgu7SnhLgACBwQpEj41HHnlk2mqrrZoZx06XSGjiaI2BAAECtQlIbGqrEfEQ6BKIDYhPfvKTTUIT199st912KToZiI2NK664Il122WXp0Ucf7SrlLQECBMoEttlmm3TEEUekQw89tJPQxGmycR1NXE9jIECAQK0CEptaa0ZcBLoEYoMiTk87/PDD00EHHZRi4yMSnXh87Wtfax533nlnVylvCRAgsDiB6Gp+7dq1zSOXiJ0m0bHJ5ZdfnqLnMwMBAgRqFpDY1Fw7YiPQJRAbFtG5QJwKEhsgL3nJS9Juu+3W2RhZt25d09FAHMnZuHFjV2lvCRAgMFdg1apVzZGZuIZvjz326Hx4zz33pG9+85vNDpOHHnqoM94LAgQI1Cwgsam5dsRGYB6B2NCIu3vHIzZGDjzwwOYoTryOx6/+6q82PRVdf/31zYbJPLMxmgCBCRWIHSMHHHBAimv38hA7Q+LozHXXXZdiJ0kMs7Oz+WPPBAgQqF5AYlN9FQmQwMICcY+beMT573GKWiQ5z3nOc5oNlthoecMb3pBuv/32dPPNN6dbbrklOV1tYU+fEliJAnGa2T777JP23XfftNdee81Zxfvuu69JZiKpuffee5vPpqam5kzjDQECBJaDgMRmOdSSGAksQiA2SOLO35Hg5CM4cR+cuBYnNmTiEZ0OPP74401yE3tk77777hSnnDzwwAOLWIJJCBBYDgI77rhjc4rq7rvv3hzBjaRmyy23nBP6I4880hyVyUdodNs8h8cbAgSWqYDEZplWnLAJzCcQGyhXX31181i9enWzYfP85z8/5cdOO+3U7LWNPbd52LBhQ7OndmZmpklyHnzwweYu4nEaSj4lJU/rmQCB8QvEKadxVCV6Sdxhhx1S/K533nnntMsuu3R6MmtHGb/tOFobOzPyDo0nnniiPYnXBAgQWPYCEptlX4VWgMD8ArHhEqehxSMPsfETR3LyIzofiHtU5Otz8nSeCRBYvgKxQyISmZzMOCq7fOtS5AQILF5AYrN4K1MSWBEC0bNaPOKoTgxxVCeSnK233ro5ba393H4dp7TF+3g885nPXBEWVoLAchD40Y9+lB577LHmEd0vx+v83H6dx8W9rxyNWQ41K0YCBAYtILEZtKj5EVhmArEBdNtttw086unp6WaecQpMjUO+OLrWXp/E19+3pna/2n8f/ekrTYAAgfEIPGM8i7VUAgQIECBAgAABAgQIDE5AYjM4S3MiQIAAAQIECBAgQGBMAhKbMcFbLAECBAgQIECAAAECgxOQ2AzO0pwIECBAgAABAgQIEBiTgMRmTPAWS4AAAQIECBAgQIDA4AQkNoOzNCcCBAgQIECAAAECBMYksGrt2rUbx7RsiyVAgAABAgQIECBAgMBABByxGQijmRAgQIAAAQIECBAgME6B5gadblBXVgW132Ct9hvUia/se5dL1e7n95Frquy59voVX1m95lJ+H1mi7Nn3r8wtl6rdz+8j11TZc+31O8z4HLEp+84oRYAAAQIECBAgQIBARQISm4oqQygECBAgQIAAAQIECJQJSGzK3JQiQIAAAQIECBAgQKAiAYlNRZUhFAIECBAgQIAAAQIEygQkNmVuShEgQIAAAQIECBAgUJGAxKaiyhAKAQIECBAgQIAAAQJlAhKbMjelCBAgQIAAAQIECBCoSEBiU1FlCIUAAQIECBAgQIAAgTIBiU2Zm1IECBAgQIAAAQIECFQkILGpqDKEQoAAAQIECBAgQIBAmYDEpsxNKQIECBAgQIAAAQIEKhKQ2FRUGUIhQIAAAQIECBAgQKBMQGJT5qYUAQIECBAgQIAAAQIVCUhsKqoMoRAgQIAAAQIECBAgUCYgsSlzU4oAAQIECBAgQIAAgYoEJDYVVYZQCBAgQIAAAQIECBAoE5DYlLkpRYAAAQIECBAgQIBARQISm4oqQygECBAgQIAAAQIECJQJSGzK3JQiQIAAAQIECBAgQKAiAYlNRZUhFAIECBAgQIAAAQIEygQkNmVuShEgQIAAAQIECBAgUJGAxKaiyhAKAQIECBAgQIAAAQJlAhKbMjelCBAgQIAAAQIECBCoSEBiU1FlCIUAAQIECBAgQIAAgTIBiU2Zm1IECBAgQIAAAQIECFQkILGpqDKEQoAAAQIECBAgQIBAmYDEpsxNKQIECBAgQIAAAQIEKhKQ2FRUGUIhQIAAAQIECBAgQKBMQGJT5qYUAQIECBAgQIAAAQIVCUhsKqoMoRAgQIAAAQIECBAgUCYgsSlzU4oAAQIECBAgQIAAgYoEJDYVVYZQCBAgQIAAAQIECBAoE5DYlLkpRYAAAQIECBAgQIBARQISm4oqQygECBAgQIAAAQIECJQJSGzK3JQiQIAAAQIECBAgQKAiAYlNRZUhFAIECBAgQIAAAQIEygQkNmVuShEgQIAAAQIECBAgUJHAqrVr126sKB6hECBAgAABAgQIECBAYMkCjtgsmUwBAgQIECBAgAABAgRqE1gdAc3OztYWVxPP1NRU81xrfNPT0018MzMz/AoEaq9f8RVUaquI30cLo+Cl718BWqtI7X5+H63KKnhZe/2Kr6BSW0X8PloYBS8n+fvniE3BF0YRAgQIECBAgAABAgTqEpDY1FUfoiFAgAABAgQIECBAoEBAYlOApggBAgQIECBAgAABAnUJSGzqqg/RECBAgAABAgQIECBQICCxKUBThAABAgQIECBAgACBugQkNnXVh2gIECBAgAABAgQIECgQkNgUoClCgAABAgQIECBAgEBdAhKbuupDNAQIECBAgAABAgQIFAhIbArQFCFAgAABAgQIECBAoC4BiU1d9SEaAgQIECBAgAABAgQKBCQ2BWiKECBAgAABAgQIECBQl4DEpq76EA0BAgQIECBAgAABAgUCEpsCNEUIECBAgAABAgQIEKhLQGJTV32IhgABAgQIECBAgACBAgGJTQGaIgQIECBAgAABAgQI1CUgsamrPkRDgAABAgQIECBAgECBgMSmAE0RAgQIECBAgAABAgTqEpDY1FUfoiFAgAABAgQIECBAoEBAYlOApggBAgQIECBAgAABAnUJSGzqqg/RECBAgAABAgQIECBQICCxKUBThAABAgQIECBAgACBugQkNnXVh2gIECBAgAABAgQIECgQkNgUoClCgAABAgQIECBAgEBdAhKbuupDNAQIECBAgAABAgQIFAhIbArQFCFAgAABAgQIECBAoC4BiU1d9SEaAgQIECBAgAABAgQKBCQ2BWiKECBAgAABAgQIECBQl4DEpq76EA0BAgQIECBAgAABAgUCEpsCNEUIECBAgAABAgQIEKhLQGJTV32IhgABAgQIECBAgACBAgGJTQGaIgQIECBAgAABAgQI1CUgsamrPkRDgAABAgQIECBAgECBgMSmAE0RAgQIECBAgAABAgTqEpDY1FUfoiFAgAABAgQIECBAoEBAYlOApggBAgQIECBAgAABAnUJSGzqqg/RECBAgAABAgQIECBQICCxKUBThAABAgQIECBAgACBugRWrV27dmNdIYmGAAECBAgQIECAAAECSxNwxGZpXqYmQIAAAQIECBAgQKBCgdUR0+zsbIWhpTQ1NdXEVWt809PTTXwzMzP8CgRqr1/xFVRqq4jfRwuj4KXvXwFaq0jtfn4frcoqeFl7/YqvoFJbRfw+WhgFLyf5++eITcEXRhECBAgQIECAAAECBOoSkNjUVR+iIUCAAAECBAgQIECgQEBiU4CmCAECBAgQIECAAAECdQlIbOqqD9EQIECAAAECBAgQIFAgILEpQFOEAAECBAgQIECAAIG6BCQ2ddWHaAgQIECAAAECBAgQKBCQ2BSgKUKAAAECBAgQIECAQF0CEpu66kM0BAgQIECAAAECBAgUCEhsCtAUIUCAAAECBAgQIECgLgGJTV31IRoCBAgQIECAAAECBAoEJDYFaIoQIECAAAECBAgQIFCXgMSmrvoQDQECBAgQIECAAAECBQISmwI0RQgQIECAAAECBAgQqEtAYlNXfYiGAAECBAgQIECAAIECAYlNAZoiBAgQIECAAAECBAjUJSCxqas+REOAAAECBAgQIECAQIGAxKYATRECBAgQIECAAAECBOoSkNjUVR+iIUCAAAECBAgQIECgQEBiU4CmCAECBAgQIECAAAECdQlIbOqqD9EQIECAAAECBAgQIFAgILEpQFOEAAECBAgQIECAAIG6BCQ2ddWHaAgQIECAAAECBAgQKBCQ2BSgKUKAAAECBAgQIECAQF0CEpu66kM0BAgQIECAAAECBAgUCEhsCtAUIUCAAAECBAgQIECgLgGJTV31IRoCBAgQIECAAAECBAoEJDYFaIoQIECAAAECBAgQIFCXgMSmrvoQDQECBAgQIECAAAECBQISmwI0RQgQIECAAAECBAgQqEtAYlNXfYiGAAECBAgQIECAAIECAYlNAZoiBAgQIECAAAECBAjUJSCxqas+REOAAAECBAgQIECAQIGAxKYATRECBAgQIECAAAECBOoSkNjUVR+iIUCAAAECBAgQIECgQEBiU4CmCAECBAgQIECAAAECdQlIbOqqD9EQIECAAAECBAgQIFAgILEpQFOEAAECBAgQIECAAIG6BFatXbt2Y10hiYYAAQIECBAgQIAAAQJLE3DEZmlepiZAgAABAgQIECBAoEKB1RHT7OxshaGlNDU11cRVa3zT09NNfDMzM/wKBGqvX/EVVGqriN9HC6Pgpe9fAVqrSO1+fh+tyip4WXv9iq+gUltF/D5aGAUvJ/n754hNwRdGEQIECBAgQIAAAQIE6hKQ2NRVH6IhQIAAAQIECBAgQKBAQGJTgKYIAQIECBAgQIAAAQJ1CUhs6qoP0RAgQIAAAQIECBAgUCAgsSlAU4QAAQIECBAgQIAAgboEJDZ11YdoCBAgQIAAAQIECBAoEJDYFKApQoAAAQIECBAgQIBAXQISm7rqQzQECBAgQIAAAQIECBQISGwK0BQhQIAAAQIECBAgQKAuAYlNXfUhGgIECBAgQIAAAQIECgQkNgVoihAgQIAAAQIECBAgUJeAxKau+hANAQIECBAgQIAAAQIFAhKbAjRFCBAgQIAAAQIECBCoS0BiU1d9iIYAAQIECBAgQIAAgQIBiU0BmiIECBAgQIAAAQIECNQlILGpqz5EQ4AAAQIECBAgQIBAgYDEpgBNEQIECBAgQIAAAQIE6hKQ2NRVH6IhQIAAAQIECBAgQKBAQGJTgKYIAQIECBAgQIAAAQJ1CUhs6qoP0RAgQIAAAQIECBAgUCAgsSlAU4QAAQIECBAgQIAAgboEJDZ11YdoCBAgQIAAAQIECBAoEJDYFKApQoAAAQIECBAgQIBAXQISm7rqQzQECBAgQIAAAQIECBQISGwK0BQhQIAAAQIECBAgQKAuAYlNXfUhGgIECBAgQIAAAQIECgQkNgVoihAgQIAAAQIECBAgUJeAxKau+hANAQIECBAgQIAAAQIFAhKbAjRFCBAgQIAAAQIECBCoS0BiU1d9iIYAAQIECBAgQIAAgQIBiU0BmiIECBAgQIAAAQIECNQlILGpqz5EQ4AAAQIECBAgQIBAgYDEpgBNEQIECBAgQIAAAQIE6hKQ2NRVH6IhQIAAAQIECBAgQKBAQGJTgKYIAQIECBAgQIAAAQJ1CUhs6qoP0RAgQIAAAQIECBAgUCAgsSlAU4QAAQIECBAgQIAAgboEVh1++OEb6wpJNAQIECBAgAABAgQIEFiagCM2S/MyNQECBAgQIECAAAECFQqs3rBhQ5qdna0wtJSmpqaauGqNb3p6uolvZmaGX4FA7fUrvoJKbRXx+2hhFLz0/StAaxWp3c/vo1VZBS9rr1/xFVRqq4jfRwuj4OUkf//+f1ZBzmrZe1wSAAAAAElFTkSuQmCC"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.5.3\n",
+    "1) Напишите алгоритм для расчета простых процентов от некоторой суммы.\n",
+    "Начало \n",
+    "Ввести сумму a\n",
+    "Ввести процент b\n",
+    "Ввести время c\n",
+    "итого = (a * b * c) / 100\n",
+    "Вывод итог\n",
+    "Конец\n",
+    "\n",
+    "2) Напишите алгоритм для вычисления площади прямоугольника.\n",
+    "Начало\n",
+    "Ввести ширина w\n",
+    "Ввести длину l\n",
+    "s = w * h\n",
+    "Вывод s\n",
+    "Конец\n",
+    "\n",
+    "3) Напишите алгоритм вычисления периметра круга.\n",
+    "Начало\n",
+    "Ввести радиус или диаметр r/d\n",
+    "Если радиус p = 2 * pi * r\n",
+    "Если диаметр p = pi * d\n",
+    "Вывод p\n",
+    "Конец\n",
+    "\n",
+    "4) Напишите алгоритм,который находит все простые числа меньше 100\n",
+    "Начало \n",
+    "Пока i находится в диапозоне от 0 до 100:\n",
+    "    Вывод каждого числа через 1\n",
+    "Конец\n",
+    "\n",
+    "5) Напишите алгоритм превращения предложения, \n",
+    "написанного в верхнем регист­ре, в обычный для предложений регистр.\n",
+    "Начало \n",
+    "Ввеcти s\n",
+    "Преобразовать s в строку\n",
+    "Первая буква s в верхний регистр + остальные буквы s \n",
+    "Вывод s\n",
+    "Конец\n",
+    "\n",
+    "6) Составьте блок-схему приготовления льда из кипяченой воды с помощью холо­дильника.\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "7) Составьте блок-схему для нахождения суммы всех четных чисел меньше ста.\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "\n",
+    "8) Составьте блок-схему для вычисления квадрата \n",
+    "всех нечетных чисел от 1 до 15 включительно.\n",
+    "![image-3.png](attachment:image-3.png)\n",
+    "\n",
+    "9) Составьте блок-схему вывода таблицы умножения на 3.\n",
+    "![image-4.png](attachment:image-4.png)\n",
+    "\n",
+    "10) Составьте блок-схему для расчета сложных процентов (с капитализацией).\n",
+    "![image-5.png](attachment:image-5.png)\n",
+    "\n",
+    "## 1.5.4\n",
+    "1)  Что думают ученые о будущем Data Science? \n",
+    "Изучите материалы на эту тему и поделитесь ими с друзьями.\n",
+    "Ученные считают, что DS связан с интеграцией во многие прикалдыне сферы\n",
+    "человека(автоматизация на заводах, помощь в медецине, работа в опсаных местах)\n",
+    "\n",
+    "2) Составьте список разных IDE для языка Python. Узнайте, чем они похожи и чем отличаются.\n",
+    "PyCharm, VScode, Jupyter Notebook\n",
+    "PyCharm - требует много ресурсов ПК, подходит для больших проектов,\n",
+    "Имеет отладку, тестирование, работа с Git\n",
+    "\n",
+    "VScode - легкий/расширяемый, поддержка Python, бесплатный, \n",
+    "быстро работает, подходит и для маленьких проектов, \n",
+    "меньше встроенных инструментов\n",
+    "\n",
+    "Jupyter Notebook - удобен для анализа данных, визуализации, обучения,\n",
+    "может работать даже в браузере, не подходит для больших проектов\n",
+    "\n",
+    "3) Составьте список всех компилируемых и интерпретируемых языков. \n",
+    "Найдите ситуации, в которых каждый из них будет полезнее в использовании, чем остальные.\n",
+    "Компилируемые: C, C++, C#, Rust, Go, Swift, Fortran \n",
+    "Интерпретируемые: Python, JS, PHP, Ruby\n",
+    "Компилируемые - когда нужна скорость и производительность(игры, драйвера)\n",
+    "Интерпретируемые - когда нужна простота и гибкость(скрипты, веб-разработка,\n",
+    "анализ данных)\n",
+    "\n",
+    "4) Создайте алгоритмы для решения некоторых распространенных задач,\n",
+    "с кото­рыми вы сталкиваетесь в повседневной жизни. \n",
+    "Составьте для них блок-схемы.\n",
+    "![image-6.png](attachment:image-6.png)\n",
+    "\n",
+    "5) Как вы думаете, может ли изучение компьютерного программирования \n",
+    "помочь автоматизировать какие-нибудь повторяющиеся рутинные задачи? \n",
+    "Подготовьте список таких задач и попробуйте \n",
+    "автоматизировать их по мере изучения этой книги.\n",
+    "Может помочь, и помогает в таких задачах как:\n",
+    "Работа с файлами, работа с текстом, обработка данных,\n",
+    "учеба и анализ\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/made-easy/chapter_1.py b/python/made-easy/chapter_1.py
new file mode 100644
index 00000000..1a8bee1b
--- /dev/null
+++ b/python/made-easy/chapter_1.py
@@ -0,0 +1,158 @@
+"""Упражения."""
+
+# ## 1.5.1
+# 1) Какие предметные области входят в Data Science?
+# Что между ними общего и в чем различие?
+# В DS входят - математика, статистика, алгоритмы, сбор данных,
+# программная инженерия. Общее то, что сбором данных и
+# математикой/статистикой/алгоритмами занимается аналитик данных,
+# а программной инженерией, математик и т.д. занимается дата инженер
+#
+# 2) Как вы понимаете термин «алгоритм»?Как алгоритмы связаны с блок-схемами?
+# Алгоритм - это последовательность выполнение каких-либо действий/команд.Блок-схема
+# это ранний способо представить визуально алгоритм
+#
+# 3) Какую программу можно назвать хорошей? Запишите все характеристики, какие удастся придумать.
+# Я думаю, что программу можно назвать хорошей, если она организована для пользователей,
+# устройств, разработчиков. Характеристики: реализован весь функционал, безопасность,
+# оптимизация под разные платформы/версии устройств, совместимость.
+#
+# 4)  Какой язык понимает компьютер?
+# Он понимает машинный код(двоичный)
+#
+# 5) Чем языки программирования отличаются от языков, на которых мы говорим?
+# Языки на которых мы говорим, не понимает ПК, также ЯП имеют строгую типизацию,
+# синтаксис и предназначен для написания "команд для ПК".
+#
+# ## 1.5.2
+# 1) Машинное обучение-это инструмент для извлечения знаний из данных.
+# Правда
+#
+# 2) Глубокое обучение-это то же самое,что машинное обучение.
+# Ложь
+#
+# 3) Всеи нженеры-программисты также могут считаться специалистами по данным.
+# Ложь
+#
+# 4) Статистика-важный инструмент для специалистов по данным.
+# Правда
+#
+# 5) Компьютер может принимать решения, выходящие за рамки данных ему инструкций,
+# подстраиваясь под изменения среды.
+# Ложь
+#
+# 6) Компьютеры понимают языки программирования «как есть».
+# Ложь
+#
+# 7) Некоторые языки программирования компилируются,
+# некоторые интерпрети­руются, а некоторые используют и то и другое.
+# Правда
+#
+# 8) Все программы выполняются последовательно
+# Ложь
+#
+# 9) В IDE есть встроенный текстовый редактор.
+# Правда
+#
+# 10) Компиляторы и интерпретаторы - это такие механизмы,
+# наподобие привода для компакт-дисков.
+# Ложь
+#
+#
+
+# ## 1.5.3
+# 1) Напишите алгоритм для расчета простых процентов от некоторой суммы.
+# Начало
+# Ввести сумму a
+# Ввести процент b
+# Ввести время c
+# итого = (a * b * c) / 100
+# Вывод итог
+# Конец
+#
+# 2) Напишите алгоритм для вычисления площади прямоугольника.
+# Начало
+# Ввести ширина w
+# Ввести длину l
+# s = w * h
+# Вывод s
+# Конец
+#
+# 3) Напишите алгоритм вычисления периметра круга.
+# Начало
+# Ввести радиус или диаметр r/d
+# Если радиус p = 2 * pi * r
+# Если диаметр p = pi * d
+# Вывод p
+# Конец
+#
+# 4) Напишите алгоритм,который находит все простые числа меньше 100
+# Начало
+# Пока i находится в диапозоне от 0 до 100:
+#     Вывод каждого числа через 1
+# Конец
+#
+# 5) Напишите алгоритм превращения предложения,
+# написанного в верхнем регист­ре, в обычный для предложений регистр.
+# Начало
+# Ввеcти s
+# Преобразовать s в строку
+# Первая буква s в верхний регистр + остальные буквы s
+# Вывод s
+# Конец
+#
+# 6) Составьте блок-схему приготовления льда из кипяченой воды с помощью холо­дильника.
+# ![image.png](attachment:image.png)
+#
+# 7) Составьте блок-схему для нахождения суммы всех четных чисел меньше ста.
+# ![image-2.png](attachment:image-2.png)
+#
+# 8) Составьте блок-схему для вычисления квадрата
+# всех нечетных чисел от 1 до 15 включительно.
+# ![image-3.png](attachment:image-3.png)
+#
+# 9) Составьте блок-схему вывода таблицы умножения на 3.
+# ![image-4.png](attachment:image-4.png)
+#
+# 10) Составьте блок-схему для расчета сложных процентов (с капитализацией).
+# ![image-5.png](attachment:image-5.png)
+#
+# ## 1.5.4
+# 1)  Что думают ученые о будущем Data Science?
+# Изучите материалы на эту тему и поделитесь ими с друзьями.
+# Ученные считают, что DS связан с интеграцией во многие прикалдыне сферы
+# человека(автоматизация на заводах, помощь в медецине, работа в опсаных местах)
+#
+# 2) Составьте список разных IDE для языка Python. Узнайте, чем они похожи и чем отличаются.
+# PyCharm, VScode, Jupyter Notebook
+# PyCharm - требует много ресурсов ПК, подходит для больших проектов,
+# Имеет отладку, тестирование, работа с Git
+#
+# VScode - легкий/расширяемый, поддержка Python, бесплатный,
+# быстро работает, подходит и для маленьких проектов,
+# меньше встроенных инструментов
+#
+# Jupyter Notebook - удобен для анализа данных, визуализации, обучения,
+# может работать даже в браузере, не подходит для больших проектов
+#
+# 3) Составьте список всех компилируемых и интерпретируемых языков.
+# Найдите ситуации, в которых каждый из них будет полезнее в использовании, чем остальные.
+# Компилируемые: C, C++, C#, Rust, Go, Swift, Fortran
+# Интерпретируемые: Python, JS, PHP, Ruby
+# Компилируемые - когда нужна скорость и производительность(игры, драйвера)
+# Интерпретируемые - когда нужна простота и гибкость(скрипты, веб-разработка,
+# анализ данных)
+#
+# 4) Создайте алгоритмы для решения некоторых распространенных задач,
+# с кото­рыми вы сталкиваетесь в повседневной жизни.
+# Составьте для них блок-схемы.
+# ![image-6.png](attachment:image-6.png)
+#
+# 5) Как вы думаете, может ли изучение компьютерного программирования
+# помочь автоматизировать какие-нибудь повторяющиеся рутинные задачи?
+# Подготовьте список таких задач и попробуйте
+# автоматизировать их по мере изучения этой книги.
+# Может помочь, и помогает в таких задачах как:
+# Работа с файлами, работа с текстом, обработка данных,
+# учеба и анализ
+#
diff --git a/python/made-easy/chapter_2.ipynb b/python/made-easy/chapter_2.ipynb
new file mode 100644
index 00000000..ad216cf7
--- /dev/null
+++ b/python/made-easy/chapter_2.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Упражнения.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) Python - это программное обеспечение с открытым исходным кодом. \n",
+    "Это то же самое, что и бесплатное ПО?\n",
+    "Нет, так как исходный код, это когда любой может\n",
+    "посмотреть что в программе написано, изменить под себя, разработчики\n",
+    "с разных стран могут учавствовать в его улучшении. А бесплатное\n",
+    "ПО - это беспланая программа на ПК\n",
+    "\n",
+    "2) У всех ли бесплатных программ открытый исходный код? А если нет, то в чем\n",
+    "разница.\n",
+    "Нет не у всех, так как и можно использовать бесплатно, но\n",
+    "распростронять по своему усмотрению нельзя или изменять\n",
+    "Пример: WinRar\n",
+    "\n",
+    "3) Python поддерживает динамическую типизацию. Что это такое?\n",
+    "Динамическая типизация - это когда тип переменной определяется сам\n",
+    "в ходе исполнения программы, а не на этапе компиляции\n",
+    "\n",
+    "4) Назовите 5 самых популярных языков программирования для специалистов по\n",
+    "анализу данных\n",
+    "Python, R, Java, SQL, C++\n",
+    "\n",
+    "5) В чем заключается преимущество Python по сравнению с языком С?\n",
+    "Он простой, более востребован, и на нем написано много\n",
+    "удобных библиотек\n",
+    "\n",
+    "6) Python портативен. Что в этом контексте означает «портативность»?\n",
+    "Это значит, что один и тот же код можно запускать на разных ОС\n",
+    "\n",
+    "7) В чем разница между «расширяемым» и «встраиваемым» языком?\n",
+    "\n",
+    "Расширяемый — можно расширять язык внешним кодом.\n",
+    "Встраиваемый — можно встроить сам язык в другую программу.\n",
+    "\n",
+    "8) В чем смысл IDE? Чем она отличается от командной строки?\n",
+    "IDE - среда разработки, с удобными инструментами.\n",
+    "Командная строка - минимальный интерфейс для запуска команд\n",
+    "\n",
+    "9) Как открыть существующий документ Jupyter Notebook? \n",
+    "Чем эта процедура от­ личается от открытия РDF-файла \n",
+    "или текстового файла?\n",
+    "\n",
+    "Для Jupyter Notebook - нужен запущенный Jupyter-сервер\n",
+    "\n",
+    "10) В чем разница между «ячейками разметки Markdown» и «ячейками кода» \n",
+    "в Jupyter Notebook? Для чего они нужны?\n",
+    "\n",
+    "Markdown - обычный текс\n",
+    "Ячейки кода - место для написания кода\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2.12.2. Правда или ложь\n",
+    "1) Ложь \n",
+    "2) Правда\n",
+    "3) Ложь\n",
+    "4) Ложь\n",
+    "5) Правда\n",
+    "6) Правда\n",
+    "7) Ложь\n",
+    "8) Правда\n",
+    "9) Правда\n",
+    "10) Ложь"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2.13.2 - Сделал"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/made-easy/chapter_2.py b/python/made-easy/chapter_2.py
new file mode 100644
index 00000000..c9b87f00
--- /dev/null
+++ b/python/made-easy/chapter_2.py
@@ -0,0 +1,65 @@
+"""Упражнения."""
+
+# 1) Python - это программное обеспечение с открытым исходным кодом.
+# Это то же самое, что и бесплатное ПО?
+# Нет, так как исходный код, это когда любой может
+# посмотреть что в программе написано, изменить под себя, разработчики
+# с разных стран могут учавствовать в его улучшении. А бесплатное
+# ПО - это беспланая программа на ПК
+#
+# 2) У всех ли бесплатных программ открытый исходный код? А если нет, то в чем
+# разница.
+# Нет не у всех, так как и можно использовать бесплатно, но
+# распростронять по своему усмотрению нельзя или изменять
+# Пример: WinRar
+#
+# 3) Python поддерживает динамическую типизацию. Что это такое?
+# Динамическая типизация - это когда тип переменной определяется сам
+# в ходе исполнения программы, а не на этапе компиляции
+#
+# 4) Назовите 5 самых популярных языков программирования для специалистов по
+# анализу данных
+# Python, R, Java, SQL, C++
+#
+# 5) В чем заключается преимущество Python по сравнению с языком С?
+# Он простой, более востребован, и на нем написано много
+# удобных библиотек
+#
+# 6) Python портативен. Что в этом контексте означает «портативность»?
+# Это значит, что один и тот же код можно запускать на разных ОС
+#
+# 7) В чем разница между «расширяемым» и «встраиваемым» языком?
+#
+# Расширяемый — можно расширять язык внешним кодом.
+# Встраиваемый — можно встроить сам язык в другую программу.
+#
+# 8) В чем смысл IDE? Чем она отличается от командной строки?
+# IDE - среда разработки, с удобными инструментами.
+# Командная строка - минимальный интерфейс для запуска команд
+#
+# 9) Как открыть существующий документ Jupyter Notebook?
+# Чем эта процедура от­ личается от открытия РDF-файла
+# или текстового файла?
+#
+# Для Jupyter Notebook - нужен запущенный Jupyter-сервер
+#
+# 10) В чем разница между «ячейками разметки Markdown» и «ячейками кода»
+# в Jupyter Notebook? Для чего они нужны?
+#
+# Markdown - обычный текс
+# Ячейки кода - место для написания кода
+#
+
+# 2.12.2. Правда или ложь
+# 1) Ложь
+# 2) Правда
+# 3) Ложь
+# 4) Ложь
+# 5) Правда
+# 6) Правда
+# 7) Ложь
+# 8) Правда
+# 9) Правда
+# 10) Ложь
+
+# 2.13.2 - Сделал
diff --git a/python/makarov/chapter_1_back_the_roots.py b/python/makarov/chapter_1_back_the_roots.py
new file mode 100644
index 00000000..5f06c704
--- /dev/null
+++ b/python/makarov/chapter_1_back_the_roots.py
@@ -0,0 +1,109 @@
+"""Раздел 1."""
+
+# # Как обьявить переменную в Питоне
+
+# +
+# fist_num: int
+# fist_num = 15
+# print(fist_num)
+
+# second_num: str
+# second_num = "Я программирую на Питоне!"
+# print(second_num)
+
+# number_a: str
+# number_b: str
+# number_c: str
+# number_a, number_b, number_c = "Питон", "C++", "PHP"
+# print(number_a, number_b, number_c)
+
+# number_x: str
+# number_y: str
+# number_a: str
+# number_x = number_y = number_z = "То же самое значение"
+# print(number_x, number_y, number_z)
+
+# my_list: list[str]
+# my_list = ["помидоры", "огурцы", "картофель"]
+# number_a, number_b, number_c = my_list
+# print(my_list)
+# -
+
+# # Автоматическое определение типа данных
+# При создании и записи данных в переменную
+# Питон попытается автоматически определить тип этих данных.
+
+# +
+# value1 = (
+#     256  # в этом случае переменной x присваивается тип int (целочисленное значение)
+# )
+# float1 = 0.25  # y становится float (десятичной дробью)
+# words1 = "Просто текст"  # z становится str (строкой)
+# -
+
+# # Присвоение и преобразование типа данных
+# Иногда может быть полезно принудительно присвоить или
+# преобразовать тип данных уже созданной переменной.
+
+# +
+# numx = str(25)  # число 25 превратится в строку
+# numy = int(25)  # число 25 останется целочисленным значением
+# numz = float(25)  # число 25 превратится в десятичную дробь
+
+# print(type(numx), type(numy), type(numz))
+
+# # Либо
+# numx: str
+# numy: int
+# numz: float
+# print(type(numx), type(numy), type(numz))
+
+# +
+# # преобразуем строку, похожую на целое число, в целое число
+# print(type(int("25")))
+
+# # или строку, похожую на дробь, в настоящую десятичную дробь
+# print(type(float("2.5")))
+
+# # преобразуем дробь в целочисленное значение
+# # обратите внимание, что округления в большую сторону не происходит
+# print(int(36.6))
+# print(type(int(36.6)))
+
+# # конечно, и целое число, и дробь можно преобразовать в строку
+# print(type(str(25)))
+# print(type(str(36.6)))
+# -
+
+# # Именование переменных
+# Имя переменной может включать только латинские буквы и цифры,
+# а также символ подчеркивания. Одновременно оно не должно начинаться с цифры.
+# Питон отличает заглавную от строчной буквы. Пробелы и кириллицу использовать нельзя.
+
+# +
+# # допустимые имена переменных
+# variable = 'Просто переменная'
+# _variable = 'Просто переменная'
+# variable_ = 'Просто переменная'
+# my_variable = 'Просто переменная'
+# My_variable_123 = 'Просто переменная'
+
+
+# # можно применить так называемый верблюжий регистр, camelCase
+# # все слова кроме первого начинаются с заглавной буквы и пишутся слитно
+# camelCaseVariable = 'Верблюжий регистр'
+
+# # нотацию Паскаль, PascalCase (то же самое, только тепер все слова пишутся с заглавной)
+# PascalCaseVariable = 'Нотация Паскаль'
+
+# # змеиный стиль, snake_case (с нижними подчеркиваниями)
+# snake_case_variable = 'Змеиная нотация'
+
+# # Так делать нельзя
+# my-variable = 'Так делать нельзя'
+# 123variable = 'Так делать нельзя'
+# my variable = 'Так делать нельзя'
+# -
+
+
+#
diff --git a/python/makarov/chapter_1_back_to_the_roots.ipynb b/python/makarov/chapter_1_back_to_the_roots.ipynb
new file mode 100644
index 00000000..9594d420
--- /dev/null
+++ b/python/makarov/chapter_1_back_to_the_roots.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Раздел 1.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Как обьявить переменную в Питоне"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n",
+      "Я программирую на Питоне!\n",
+      "Питон C++ PHP\n",
+      "То же самое значение То же самое значение То же самое значение\n",
+      "['помидоры', 'огурцы', 'картофель']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# fist_num: int\n",
+    "# fist_num = 15\n",
+    "# print(fist_num)\n",
+    "\n",
+    "# second_num: str\n",
+    "# second_num = \"Я программирую на Питоне!\"\n",
+    "# print(second_num)\n",
+    "\n",
+    "# number_a: str\n",
+    "# number_b: str\n",
+    "# number_c: str\n",
+    "# number_a, number_b, number_c = \"Питон\", \"C++\", \"PHP\"\n",
+    "# print(number_a, number_b, number_c)\n",
+    "\n",
+    "# number_x: str\n",
+    "# number_y: str\n",
+    "# number_a: str\n",
+    "# number_x = number_y = number_z = \"То же самое значение\"\n",
+    "# print(number_x, number_y, number_z)\n",
+    "\n",
+    "# my_list: list[str]\n",
+    "# my_list = [\"помидоры\", \"огурцы\", \"картофель\"]\n",
+    "# number_a, number_b, number_c = my_list\n",
+    "# print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Автоматическое определение типа данных\n",
+    "При создании и записи данных в переменную \n",
+    "Питон попытается автоматически определить тип этих данных."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value1 = (\n",
+    "#     256  # в этом случае переменной x присваивается тип int (целочисленное значение)\n",
+    "# )\n",
+    "# float1 = 0.25  # y становится float (десятичной дробью)\n",
+    "# words1 = \"Просто текст\"  # z становится str (строкой)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Присвоение и преобразование типа данных\n",
+    "Иногда может быть полезно принудительно присвоить или \n",
+    "преобразовать тип данных уже созданной переменной. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'str'> <class 'int'> <class 'float'>\n",
+      "<class 'str'> <class 'int'> <class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# numx = str(25)  # число 25 превратится в строку\n",
+    "# numy = int(25)  # число 25 останется целочисленным значением\n",
+    "# numz = float(25)  # число 25 превратится в десятичную дробь\n",
+    "\n",
+    "# print(type(numx), type(numy), type(numz))\n",
+    "\n",
+    "# # Либо\n",
+    "# numx: str\n",
+    "# numy: int\n",
+    "# numz: float\n",
+    "# print(type(numx), type(numy), type(numz))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'int'>\n",
+      "<class 'float'>\n",
+      "36\n",
+      "<class 'int'>\n",
+      "<class 'str'>\n",
+      "<class 'str'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # преобразуем строку, похожую на целое число, в целое число\n",
+    "# print(type(int(\"25\")))\n",
+    "\n",
+    "# # или строку, похожую на дробь, в настоящую десятичную дробь\n",
+    "# print(type(float(\"2.5\")))\n",
+    "\n",
+    "# # преобразуем дробь в целочисленное значение\n",
+    "# # обратите внимание, что округления в большую сторону не происходит\n",
+    "# print(int(36.6))\n",
+    "# print(type(int(36.6)))\n",
+    "\n",
+    "# # конечно, и целое число, и дробь можно преобразовать в строку\n",
+    "# print(type(str(25)))\n",
+    "# print(type(str(36.6)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Именование переменных\n",
+    "Имя переменной может включать только латинские буквы и цифры,\n",
+    "а также символ подчеркивания. Одновременно оно не должно начинаться с цифры. \n",
+    "Питон отличает заглавную от строчной буквы. Пробелы и кириллицу использовать нельзя."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # допустимые имена переменных\n",
+    "# variable = 'Просто переменная'\n",
+    "# _variable = 'Просто переменная'\n",
+    "# variable_ = 'Просто переменная'\n",
+    "# my_variable = 'Просто переменная'\n",
+    "# My_variable_123 = 'Просто переменная'\n",
+    "\n",
+    "\n",
+    "# # можно применить так называемый верблюжий регистр, camelCase\n",
+    "# # все слова кроме первого начинаются с заглавной буквы и пишутся слитно\n",
+    "# camelCaseVariable = 'Верблюжий регистр'\n",
+    "\n",
+    "# # нотацию Паскаль, PascalCase (то же самое, только тепер все слова пишутся с заглавной)\n",
+    "# PascalCaseVariable = 'Нотация Паскаль'\n",
+    "\n",
+    "# # змеиный стиль, snake_case (с нижними подчеркиваниями)\n",
+    "# snake_case_variable = 'Змеиная нотация'\n",
+    "\n",
+    "# # Так делать нельзя\n",
+    "# my-variable = 'Так делать нельзя'\n",
+    "# 123variable = 'Так делать нельзя'\n",
+    "# my variable = 'Так делать нельзя'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/chapter_1_back_to_the_roots.py b/python/makarov/chapter_1_back_to_the_roots.py
new file mode 100644
index 00000000..5f06c704
--- /dev/null
+++ b/python/makarov/chapter_1_back_to_the_roots.py
@@ -0,0 +1,109 @@
+"""Раздел 1."""
+
+# # Как обьявить переменную в Питоне
+
+# +
+# fist_num: int
+# fist_num = 15
+# print(fist_num)
+
+# second_num: str
+# second_num = "Я программирую на Питоне!"
+# print(second_num)
+
+# number_a: str
+# number_b: str
+# number_c: str
+# number_a, number_b, number_c = "Питон", "C++", "PHP"
+# print(number_a, number_b, number_c)
+
+# number_x: str
+# number_y: str
+# number_a: str
+# number_x = number_y = number_z = "То же самое значение"
+# print(number_x, number_y, number_z)
+
+# my_list: list[str]
+# my_list = ["помидоры", "огурцы", "картофель"]
+# number_a, number_b, number_c = my_list
+# print(my_list)
+# -
+
+# # Автоматическое определение типа данных
+# При создании и записи данных в переменную
+# Питон попытается автоматически определить тип этих данных.
+
+# +
+# value1 = (
+#     256  # в этом случае переменной x присваивается тип int (целочисленное значение)
+# )
+# float1 = 0.25  # y становится float (десятичной дробью)
+# words1 = "Просто текст"  # z становится str (строкой)
+# -
+
+# # Присвоение и преобразование типа данных
+# Иногда может быть полезно принудительно присвоить или
+# преобразовать тип данных уже созданной переменной.
+
+# +
+# numx = str(25)  # число 25 превратится в строку
+# numy = int(25)  # число 25 останется целочисленным значением
+# numz = float(25)  # число 25 превратится в десятичную дробь
+
+# print(type(numx), type(numy), type(numz))
+
+# # Либо
+# numx: str
+# numy: int
+# numz: float
+# print(type(numx), type(numy), type(numz))
+
+# +
+# # преобразуем строку, похожую на целое число, в целое число
+# print(type(int("25")))
+
+# # или строку, похожую на дробь, в настоящую десятичную дробь
+# print(type(float("2.5")))
+
+# # преобразуем дробь в целочисленное значение
+# # обратите внимание, что округления в большую сторону не происходит
+# print(int(36.6))
+# print(type(int(36.6)))
+
+# # конечно, и целое число, и дробь можно преобразовать в строку
+# print(type(str(25)))
+# print(type(str(36.6)))
+# -
+
+# # Именование переменных
+# Имя переменной может включать только латинские буквы и цифры,
+# а также символ подчеркивания. Одновременно оно не должно начинаться с цифры.
+# Питон отличает заглавную от строчной буквы. Пробелы и кириллицу использовать нельзя.
+
+# +
+# # допустимые имена переменных
+# variable = 'Просто переменная'
+# _variable = 'Просто переменная'
+# variable_ = 'Просто переменная'
+# my_variable = 'Просто переменная'
+# My_variable_123 = 'Просто переменная'
+
+
+# # можно применить так называемый верблюжий регистр, camelCase
+# # все слова кроме первого начинаются с заглавной буквы и пишутся слитно
+# camelCaseVariable = 'Верблюжий регистр'
+
+# # нотацию Паскаль, PascalCase (то же самое, только тепер все слова пишутся с заглавной)
+# PascalCaseVariable = 'Нотация Паскаль'
+
+# # змеиный стиль, snake_case (с нижними подчеркиваниями)
+# snake_case_variable = 'Змеиная нотация'
+
+# # Так делать нельзя
+# my-variable = 'Так делать нельзя'
+# 123variable = 'Так делать нельзя'
+# my variable = 'Так делать нельзя'
+# -
+
+
+#
diff --git a/python/makarov/chapter_1_conditions.ipynb b/python/makarov/chapter_1_conditions.ipynb
new file mode 100644
index 00000000..81892cdd
--- /dev/null
+++ b/python/makarov/chapter_1_conditions.ipynb
@@ -0,0 +1,901 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Условия, циклы.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Medium\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Множественные условия (multi-way decisions)\n",
+    "# напишем программу, которая разобьет\n",
+    "# все числа на малые, средние и большие\n",
+    "\n",
+    "# num_x: int\n",
+    "# num_x = 42  # зададим число\n",
+    "\n",
+    "# # и пропишем условия (не забывайте про двоеточие и отступ)\n",
+    "# if num_x < 10:\n",
+    "#     print(\"Small\")\n",
+    "# elif num_x < 100:\n",
+    "#     print(\"Medium\")\n",
+    "# else:\n",
+    "#     print(\"Large\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Medium\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # запросим число у пользователя\n",
+    "# number_x: str | int\n",
+    "# number_x = input(\"Введите число: \")\n",
+    "\n",
+    "# # преобразуем в тип int\n",
+    "# number_x = int(number_x)\n",
+    "\n",
+    "# # и наконец классифицируем\n",
+    "# if number_x < 10:\n",
+    "#     print(\"Small\")\n",
+    "# elif number_x < 100:\n",
+    "#     print(\"Medium\")\n",
+    "# else:\n",
+    "#     print(\"Large\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Medium\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Вложенные условия (nested decisions)\n",
+    "# # запрашиваем число\n",
+    "# number_y: str | int\n",
+    "# number_y = input(\"Введите число: \")\n",
+    "\n",
+    "# # проверяем первое условие (не пустая ли строка), если оно выполняется\n",
+    "# if len(number_y) != 0:\n",
+    "\n",
+    "#     # преобразуем в тип int\n",
+    "#     number_y = int(number_y)\n",
+    "\n",
+    "#     # и классифицируем\n",
+    "#     if number_x < 10:\n",
+    "#         print(\"Small\")\n",
+    "#     elif number_x < 100:\n",
+    "#         print(\"Medium\")\n",
+    "#     else:\n",
+    "#         print(\"Large\")\n",
+    "\n",
+    "# # в противном, говорим, что ввод пустой\n",
+    "# else:\n",
+    "#     print(\"Ввод пустой\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Medium\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Несколько условий в одном выражении с операторами and или or\n",
+    "# # пример с and (логическим И)\n",
+    "# number_z: int\n",
+    "# number_z = 42\n",
+    "\n",
+    "# # если z больше 10 и одновременно меньше 100\n",
+    "# if number_z > 10 and number_z < 100:\n",
+    "\n",
+    "#     # у нас среднее число\n",
+    "#     print(\"Medium\")\n",
+    "\n",
+    "# # в противном случае оно либо маленькое либо большое\n",
+    "# else:\n",
+    "#     print(\"Small or Large\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Small or Large\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # пример с or (логическим ИЛИ)\n",
+    "# numz: int\n",
+    "# numz = 2\n",
+    "\n",
+    "# # если z меньше 10 или больше 100\n",
+    "# if numz < 10 or numz > 100:\n",
+    "\n",
+    "#     # оно либо маленькое либо большое\n",
+    "#     print(\"Small or Large\")\n",
+    "\n",
+    "# # в противном случае оно среднее\n",
+    "# else:\n",
+    "#     print(\"Medium\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # можно проверить вхождение слова в строку\n",
+    "# sentence: str\n",
+    "# word: str\n",
+    "# sentence = \"To be, or not to be, that is the question\"\n",
+    "# word = \"question\"\n",
+    "\n",
+    "# if word in sentence:\n",
+    "#     print(\"Слово найдено\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # или отсутствие элемента в списке\n",
+    "# number_list: list[int]\n",
+    "# number: int\n",
+    "# number_list = [2, 3, 4, 6, 7]\n",
+    "# number = 5\n",
+    "\n",
+    "# if number not in number_list:\n",
+    "#     print(\"Такого числа в списке нет\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # кроме того, можно проверить вхождение ключа и значения в словарь\n",
+    "\n",
+    "# # возьмем очень простой словарь\n",
+    "# tasty_list: dict[str, int]\n",
+    "# tasty_list = {\"apple\": 3, \"tomato\": 6, \"carrot\": 2}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Нашлись\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # вначале поищем яблоки среди ключей словаря\n",
+    "# if \"apple\" in tasty_list:\n",
+    "#     print(\"Нашлись\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Есть\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # а затем посмотрим, нет ли числа 6 среди его значений\n",
+    "# # с помощью метода .values()\n",
+    "# if 6 in tasty_list.values():\n",
+    "#     print(\"Есть\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Циклы в Питоне"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # поочередно выведем элементы списка\n",
+    "# second_number_list = [1, 2, 3]\n",
+    "\n",
+    "# # не забывая про двоеточие и отступ\n",
+    "# for num in second_number_list:\n",
+    "#     print(num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # создадим словарь, значениями которого будут списки из двух элементов\n",
+    "# scd_f = {\"apple\": [3, \"kg\"], \"tomato\": [6, \"pcs\"], \"carrot\": [2, \"kg\"]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apple [3, 'kg']\n",
+      "tomato [6, 'pcs']\n",
+      "carrot [2, 'kg']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # затем создадим две переменные-контейнера и применим метод .items()\n",
+    "# for key, value in scd_f.items():\n",
+    "#     print(key, value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "6\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # возьмем только одну переменную и применим метод .values()\n",
+    "# for food in scd_f.values():\n",
+    "#     # значение представляет собой список, выведем его первый элемент с индексом [0]\n",
+    "#     print(food[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clients = dict[int, dict[str, str | int]]\n",
+    "# clients = {\n",
+    "#     1: {\"name\": \"Анна\", \"age\": 24, \"sex\": \"male\", \"revenue\": 12000},\n",
+    "#     2: {\"name\": \"Илья\", \"age\": 18, \"sex\": \"female\", \"revenue\": 8000},\n",
+    "# }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "client ID: 1\n",
+      "name: Анна\n",
+      "age: 24\n",
+      "sex: male\n",
+      "revenue: 12000\n",
+      "\n",
+      "client ID: 2\n",
+      "name: Илья\n",
+      "age: 18\n",
+      "sex: female\n",
+      "revenue: 8000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # в первом цикле for поместим id и информацию о клиентах в переменные id и info\n",
+    "# for id, info in clients.items():\n",
+    "\n",
+    "#     # выведем id клиента\n",
+    "#     print(\"client ID: \" + str(id))\n",
+    "\n",
+    "#     # во втором цикле возьмем информацию об очередном клиенте (тоже словарь)\n",
+    "#     for k, v in info.items():\n",
+    "\n",
+    "#         # и выведем каждый ключ (название поля) и значение (саму информацию)\n",
+    "#         print(k + \": \" + str(v))\n",
+    "\n",
+    "#     # добавим пустую строку после того, как выведем информацию об одном клиенте\n",
+    "#     print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Функции range() и enumerate()\n",
+    "# # создадим последовательность от 0 до 4\n",
+    "# for nums1 in range(5):\n",
+    "#     print(nums1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # от 1 до 5\n",
+    "# for nums2 in range(1, 6):\n",
+    "#     print(nums2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "2\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # и от 0 до 5 с шагом 2 (то есть будем выводить числа через одно)\n",
+    "# for nums3 in range(0, 6, 2):\n",
+    "#     print(nums3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Функция range() принимает от одного до трех параметров.\n",
+    "\n",
+    "Если передать только один параметр, то мы начнем последовательность с нуля и закончим на элементе, предшествующем нашему параметру. В примере выше мы передали параметр «пять» (range(5)) и получили последовательность 0, 1, 2, 3, 4.\n",
+    "Если указать два параметра, то мы начнем последовательность с первого параметра и законим на элементе, предшествующем второму параметру. В частности, если написать range(1, 6), то получится 1, 2, 3, 4, 5.\n",
+    "Третий параметр устанавливает шаг. По умолчанию он равен единице, однако если, например, написать, range(0, 6, 2), то мы получим 0, 2, 4.\n",
+    "Что интересно, если совместить range() с функцией len(), то такую конструкцию можно использовать для того, чтобы в одном цикле вывести все элементы, например, двух списков по их индексу."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Январь 47\n",
+      "Февраль 75\n",
+      "Март 79\n",
+      "Апрель 94\n",
+      "Май 123\n",
+      "Июнь 209\n",
+      "Июль 233\n",
+      "Август 214\n",
+      "Сентябрь 197\n",
+      "Октябрь 130\n",
+      "Ноябрь 87\n",
+      "Декабрь 55\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # возьмем месяцы года\n",
+    "# months: list[str]\n",
+    "# sales: list[int]\n",
+    "# months = [\n",
+    "#     \"Январь\",\n",
+    "#     \"Февраль\",\n",
+    "#     \"Март\",\n",
+    "#     \"Апрель\",\n",
+    "#     \"Май\",\n",
+    "#     \"Июнь\",\n",
+    "#     \"Июль\",\n",
+    "#     \"Август\",\n",
+    "#     \"Сентябрь\",\n",
+    "#     \"Октябрь\",\n",
+    "#     \"Ноябрь\",\n",
+    "#     \"Декабрь\",\n",
+    "# ]\n",
+    "\n",
+    "# # и продажи мороженого в тыс. рублей в каждый из месяцев\n",
+    "# sales = [47, 75, 79, 94, 123, 209, 233, 214, 197, 130, 87, 55]\n",
+    "\n",
+    "# # задав последовательность через range(len()),\n",
+    "# for mnts in range(len(months)):\n",
+    "\n",
+    "#     # мы можем вывести каждый из элементов обоих списков в одном цикле\n",
+    "#     print(months[mnts], sales[mnts])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Способ 1. Использовать функцию reversed(). Эта функция меняет порядок элементов списка на обратный.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "3\n",
+      "2\n",
+      "1\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # создадим список\n",
+    "# my_list2 = [0, 1, 2, 3, 4]\n",
+    "\n",
+    "# # передадим его функции reversed() и\n",
+    "# # выведем каждый из элементов списка с помощью цикла for\n",
+    "# for nms in reversed(my_list2):\n",
+    "#     print(nms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "3\n",
+      "2\n",
+      "1\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for numbrr in reversed(range(5)):\n",
+    "#     print(numbrr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Способ 2. Указать в качестве параметра шага. При этом важно, чтобы первым параметром указывался конечный элемент списка, а вторым — начальный."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "3\n",
+      "2\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for num_i in range(4, 0, -1):\n",
+    "#     print(num_i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "3\n",
+      "2\n",
+      "1\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for nms_i in range(4, -1, -1):\n",
+    "#     print(nms_i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Способ 3. Функция sorted(). Наконец, хотя в данном случае это явно не оптимальный вариант, можно использовать функцию sorted(), которая сортирует элементы списка по убыванию, если передать ей параметр reverse = True."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "3\n",
+      "2\n",
+      "1\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # создадим последовательность от 0 до 4\n",
+    "# revrs = range(5)\n",
+    "\n",
+    "# # отсортируем ее по убыванию\n",
+    "# sorted_values = sorted(revrs, reverse=True)\n",
+    "\n",
+    "# # выведем элементы отсортированной последовательности\n",
+    "# for srt_num in sorted_values:\n",
+    "#     print(srt_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 Понедельник\n",
+      "1 Вторник\n",
+      "2 Среда\n",
+      "3 Четверг\n",
+      "4 Пятница\n",
+      "5 Суббота\n",
+      "6 Воскресенье\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Функция enumerate()\n",
+    "\n",
+    "# # пусть дан список с днями недели\n",
+    "# days: list[str]\n",
+    "# days = [\n",
+    "#     \"Понедельник\",\n",
+    "#     \"Вторник\",\n",
+    "#     \"Среда\",\n",
+    "#     \"Четверг\",\n",
+    "#     \"Пятница\",\n",
+    "#     \"Суббота\",\n",
+    "#     \"Воскресенье\",\n",
+    "# ]\n",
+    "\n",
+    "# # выведем индекс (i) и сами элементы списка (day)\n",
+    "# for indx, day in enumerate(days):\n",
+    "#     print(indx, day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 Понедельник\n",
+      "2 Вторник\n",
+      "3 Среда\n",
+      "4 Четверг\n",
+      "5 Пятница\n",
+      "6 Суббота\n",
+      "7 Воскресенье\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # так же выведем индекс и элементы списка, но начнем с 1\n",
+    "# for indx2, day in enumerate(days, 1):\n",
+    "#     print(indx2, day)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Цикл while"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Текущее значение счетчика:  11\n",
+      "Новое значение счетчика:    1\n",
+      "\n",
+      "Текущее значение счетчика:  11\n",
+      "Новое значение счетчика:    2\n",
+      "\n",
+      "Текущее значение счетчика:  11\n",
+      "Новое значение счетчика:    3\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # зададим начальное значение счетчика\n",
+    "# my_num: int\n",
+    "# my_num = 0\n",
+    "\n",
+    "# # пока счетчик меньше трех\n",
+    "# while my_num < 3:\n",
+    "\n",
+    "#     # в каждом цикле будем выводить его текущее значение\n",
+    "#     print(\"Текущее значение счетчика:  \" + str(i))\n",
+    "\n",
+    "#     # внутри цикла не забудем \"нарастить\" счетчик\n",
+    "#     my_num = my_num + 1\n",
+    "\n",
+    "#     # и выведем новое значение\n",
+    "#     print(\"Новое значение счетчика:    \" + str(my_num))\n",
+    "\n",
+    "#     # добавим пустую строку\n",
+    "#     print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 {'name': 'Анна', 'age': 24, 'sex': 'male', 'revenue': 12000}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Операторы break и continue\n",
+    "\n",
+    "# # вновь возьмем словарь clients\n",
+    "# sec_clients: dict[int, dict[str, str | int]]\n",
+    "# sec_clients = {\n",
+    "#     1: {\"name\": \"Анна\", \"age\": 24, \"sex\": \"male\", \"revenue\": 12000},\n",
+    "#     2: {\"name\": \"Илья\", \"age\": 18, \"sex\": \"female\", \"revenue\": 8000},\n",
+    "# }\n",
+    "\n",
+    "# # в цикле пройдемся по ключам и значениям словаря\n",
+    "# for id2, info2 in sec_clients.items():\n",
+    "\n",
+    "#     # и выведем их\n",
+    "#     print(id2, info2)\n",
+    "\n",
+    "#     # однако уже после первого исполнения цикла, прервем его\n",
+    "#     break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "4\n",
+      "6\n",
+      "8\n",
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # с помощью функции range создадим последовательность от 1 до 10\n",
+    "# for cnt in range(1, 11):\n",
+    "\n",
+    "#     # если остаток от деления на два не равен нулю (то есть число нечетное)\n",
+    "#     if cnt % 2 != 0:\n",
+    "\n",
+    "#         # идем к следующему числу последовательности\n",
+    "#         continue\n",
+    "\n",
+    "#     # в противном случае выводим число\n",
+    "#     else:\n",
+    "#         print(cnt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Понедельник - день тяжелый\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Форматирование строк в функции print()\n",
+    "# # снова возьмем список с днями недели\n",
+    "# days2: list[str]\n",
+    "# days2 = [\n",
+    "#     \"Понедельник\",\n",
+    "#     \"Вторник\",\n",
+    "#     \"Среда\",\n",
+    "#     \"Четверг\",\n",
+    "#     \"Пятница\",\n",
+    "#     \"Суббота\",\n",
+    "#     \"Воскресенье\",\n",
+    "# ]\n",
+    "\n",
+    "# # и для простоты поместим слово \"Понедельник\" в переменную Monday\n",
+    "# Monday = days[0]\n",
+    "# # Monday\n",
+    "# print(f\"{Monday} - день тяжелый\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/chapter_1_conditions.py b/python/makarov/chapter_1_conditions.py
new file mode 100644
index 00000000..fb94a733
--- /dev/null
+++ b/python/makarov/chapter_1_conditions.py
@@ -0,0 +1,369 @@
+"""Условия, циклы."""
+
+# +
+# Множественные условия (multi-way decisions)
+# напишем программу, которая разобьет
+# все числа на малые, средние и большие
+
+# num_x: int
+# num_x = 42  # зададим число
+
+# # и пропишем условия (не забывайте про двоеточие и отступ)
+# if num_x < 10:
+#     print("Small")
+# elif num_x < 100:
+#     print("Medium")
+# else:
+#     print("Large")
+
+# +
+# # запросим число у пользователя
+# number_x: str | int
+# number_x = input("Введите число: ")
+
+# # преобразуем в тип int
+# number_x = int(number_x)
+
+# # и наконец классифицируем
+# if number_x < 10:
+#     print("Small")
+# elif number_x < 100:
+#     print("Medium")
+# else:
+#     print("Large")
+
+# +
+# # Вложенные условия (nested decisions)
+# # запрашиваем число
+# number_y: str | int
+# number_y = input("Введите число: ")
+
+# # проверяем первое условие (не пустая ли строка), если оно выполняется
+# if len(number_y) != 0:
+
+#     # преобразуем в тип int
+#     number_y = int(number_y)
+
+#     # и классифицируем
+#     if number_x < 10:
+#         print("Small")
+#     elif number_x < 100:
+#         print("Medium")
+#     else:
+#         print("Large")
+
+# # в противном, говорим, что ввод пустой
+# else:
+#     print("Ввод пустой")
+
+# +
+# # Несколько условий в одном выражении с операторами and или or
+# # пример с and (логическим И)
+# number_z: int
+# number_z = 42
+
+# # если z больше 10 и одновременно меньше 100
+# if number_z > 10 and number_z < 100:
+
+#     # у нас среднее число
+#     print("Medium")
+
+# # в противном случае оно либо маленькое либо большое
+# else:
+#     print("Small or Large")
+
+# +
+# # пример с or (логическим ИЛИ)
+# numz: int
+# numz = 2
+
+# # если z меньше 10 или больше 100
+# if numz < 10 or numz > 100:
+
+#     # оно либо маленькое либо большое
+#     print("Small or Large")
+
+# # в противном случае оно среднее
+# else:
+#     print("Medium")
+
+# +
+# # можно проверить вхождение слова в строку
+# sentence: str
+# word: str
+# sentence = "To be, or not to be, that is the question"
+# word = "question"
+
+# if word in sentence:
+#     print("Слово найдено")
+
+# +
+# # или отсутствие элемента в списке
+# number_list: list[int]
+# number: int
+# number_list = [2, 3, 4, 6, 7]
+# number = 5
+
+# if number not in number_list:
+#     print("Такого числа в списке нет")
+
+# +
+# # кроме того, можно проверить вхождение ключа и значения в словарь
+
+# # возьмем очень простой словарь
+# tasty_list: dict[str, int]
+# tasty_list = {"apple": 3, "tomato": 6, "carrot": 2}
+
+# +
+# # вначале поищем яблоки среди ключей словаря
+# if "apple" in tasty_list:
+#     print("Нашлись")
+
+# +
+# # а затем посмотрим, нет ли числа 6 среди его значений
+# # с помощью метода .values()
+# if 6 in tasty_list.values():
+#     print("Есть")
+# -
+
+# ## Циклы в Питоне
+
+# +
+# # поочередно выведем элементы списка
+# second_number_list = [1, 2, 3]
+
+# # не забывая про двоеточие и отступ
+# for num in second_number_list:
+#     print(num)
+
+# +
+# # создадим словарь, значениями которого будут списки из двух элементов
+# scd_f = {"apple": [3, "kg"], "tomato": [6, "pcs"], "carrot": [2, "kg"]}
+
+# +
+# # затем создадим две переменные-контейнера и применим метод .items()
+# for key, value in scd_f.items():
+#     print(key, value)
+
+# +
+# # возьмем только одну переменную и применим метод .values()
+# for food in scd_f.values():
+#     # значение представляет собой список, выведем его первый элемент с индексом [0]
+#     print(food[0])
+
+# +
+# clients = dict[int, dict[str, str | int]]
+# clients = {
+#     1: {"name": "Анна", "age": 24, "sex": "male", "revenue": 12000},
+#     2: {"name": "Илья", "age": 18, "sex": "female", "revenue": 8000},
+# }
+
+# +
+# # в первом цикле for поместим id и информацию о клиентах в переменные id и info
+# for id, info in clients.items():
+
+#     # выведем id клиента
+#     print("client ID: " + str(id))
+
+#     # во втором цикле возьмем информацию об очередном клиенте (тоже словарь)
+#     for k, v in info.items():
+
+#         # и выведем каждый ключ (название поля) и значение (саму информацию)
+#         print(k + ": " + str(v))
+
+#     # добавим пустую строку после того, как выведем информацию об одном клиенте
+#     print()
+
+# +
+# # Функции range() и enumerate()
+# # создадим последовательность от 0 до 4
+# for nums1 in range(5):
+#     print(nums1)
+
+# +
+# # от 1 до 5
+# for nums2 in range(1, 6):
+#     print(nums2)
+
+# +
+# # и от 0 до 5 с шагом 2 (то есть будем выводить числа через одно)
+# for nums3 in range(0, 6, 2):
+#     print(nums3)
+# -
+
+# Функция range() принимает от одного до трех параметров.
+#
+# Если передать только один параметр, то мы начнем последовательность с нуля и закончим на элементе, предшествующем нашему параметру. В примере выше мы передали параметр «пять» (range(5)) и получили последовательность 0, 1, 2, 3, 4.
+# Если указать два параметра, то мы начнем последовательность с первого параметра и законим на элементе, предшествующем второму параметру. В частности, если написать range(1, 6), то получится 1, 2, 3, 4, 5.
+# Третий параметр устанавливает шаг. По умолчанию он равен единице, однако если, например, написать, range(0, 6, 2), то мы получим 0, 2, 4.
+# Что интересно, если совместить range() с функцией len(), то такую конструкцию можно использовать для того, чтобы в одном цикле вывести все элементы, например, двух списков по их индексу.
+
+# +
+# # возьмем месяцы года
+# months: list[str]
+# sales: list[int]
+# months = [
+#     "Январь",
+#     "Февраль",
+#     "Март",
+#     "Апрель",
+#     "Май",
+#     "Июнь",
+#     "Июль",
+#     "Август",
+#     "Сентябрь",
+#     "Октябрь",
+#     "Ноябрь",
+#     "Декабрь",
+# ]
+
+# # и продажи мороженого в тыс. рублей в каждый из месяцев
+# sales = [47, 75, 79, 94, 123, 209, 233, 214, 197, 130, 87, 55]
+
+# # задав последовательность через range(len()),
+# for mnts in range(len(months)):
+
+#     # мы можем вывести каждый из элементов обоих списков в одном цикле
+#     print(months[mnts], sales[mnts])
+# -
+
+# ## Способ 1. Использовать функцию reversed(). Эта функция меняет порядок элементов списка на обратный.
+#
+#
+
+# +
+# # создадим список
+# my_list2 = [0, 1, 2, 3, 4]
+
+# # передадим его функции reversed() и
+# # выведем каждый из элементов списка с помощью цикла for
+# for nms in reversed(my_list2):
+#     print(nms)
+
+# +
+# for numbrr in reversed(range(5)):
+#     print(numbrr)
+# -
+
+# ## Способ 2. Указать в качестве параметра шага. При этом важно, чтобы первым параметром указывался конечный элемент списка, а вторым — начальный.
+
+# +
+# for num_i in range(4, 0, -1):
+#     print(num_i)
+
+# +
+# for nms_i in range(4, -1, -1):
+#     print(nms_i)
+# -
+
+# ## Способ 3. Функция sorted(). Наконец, хотя в данном случае это явно не оптимальный вариант, можно использовать функцию sorted(), которая сортирует элементы списка по убыванию, если передать ей параметр reverse = True.
+
+# +
+# # создадим последовательность от 0 до 4
+# revrs = range(5)
+
+# # отсортируем ее по убыванию
+# sorted_values = sorted(revrs, reverse=True)
+
+# # выведем элементы отсортированной последовательности
+# for srt_num in sorted_values:
+#     print(srt_num)
+
+# +
+# # Функция enumerate()
+
+# # пусть дан список с днями недели
+# days: list[str]
+# days = [
+#     "Понедельник",
+#     "Вторник",
+#     "Среда",
+#     "Четверг",
+#     "Пятница",
+#     "Суббота",
+#     "Воскресенье",
+# ]
+
+# # выведем индекс (i) и сами элементы списка (day)
+# for indx, day in enumerate(days):
+#     print(indx, day)
+
+# +
+# # так же выведем индекс и элементы списка, но начнем с 1
+# for indx2, day in enumerate(days, 1):
+#     print(indx2, day)
+# -
+
+# ## Цикл while
+
+# +
+# # зададим начальное значение счетчика
+# my_num: int
+# my_num = 0
+
+# # пока счетчик меньше трех
+# while my_num < 3:
+
+#     # в каждом цикле будем выводить его текущее значение
+#     print("Текущее значение счетчика:  " + str(i))
+
+#     # внутри цикла не забудем "нарастить" счетчик
+#     my_num = my_num + 1
+
+#     # и выведем новое значение
+#     print("Новое значение счетчика:    " + str(my_num))
+
+#     # добавим пустую строку
+#     print()
+
+# +
+# # Операторы break и continue
+
+# # вновь возьмем словарь clients
+# sec_clients: dict[int, dict[str, str | int]]
+# sec_clients = {
+#     1: {"name": "Анна", "age": 24, "sex": "male", "revenue": 12000},
+#     2: {"name": "Илья", "age": 18, "sex": "female", "revenue": 8000},
+# }
+
+# # в цикле пройдемся по ключам и значениям словаря
+# for id2, info2 in sec_clients.items():
+
+#     # и выведем их
+#     print(id2, info2)
+
+#     # однако уже после первого исполнения цикла, прервем его
+#     break
+
+# +
+# # с помощью функции range создадим последовательность от 1 до 10
+# for cnt in range(1, 11):
+
+#     # если остаток от деления на два не равен нулю (то есть число нечетное)
+#     if cnt % 2 != 0:
+
+#         # идем к следующему числу последовательности
+#         continue
+
+#     # в противном случае выводим число
+#     else:
+#         print(cnt)
+
+# +
+# # Форматирование строк в функции print()
+# # снова возьмем список с днями недели
+# days2: list[str]
+# days2 = [
+#     "Понедельник",
+#     "Вторник",
+#     "Среда",
+#     "Четверг",
+#     "Пятница",
+#     "Суббота",
+#     "Воскресенье",
+# ]
+
+# # и для простоты поместим слово "Понедельник" в переменную Monday
+# Monday = days[0]
+# # Monday
+# print(f"{Monday} - день тяжелый")
diff --git a/python/makarov/chapter_1_conditions_and_cycles_continued.py b/python/makarov/chapter_1_conditions_and_cycles_continued.py
new file mode 100644
index 00000000..93d1343c
--- /dev/null
+++ b/python/makarov/chapter_1_conditions_and_cycles_continued.py
@@ -0,0 +1,369 @@
+"""conditions_and_cycles_continued."""
+
+# +
+# Множественные условия (multi-way decisions)
+# напишем программу, которая разобьет
+# все числа на малые, средние и большие
+
+# num_x: int
+# num_x = 42  # зададим число
+
+# # и пропишем условия (не забывайте про двоеточие и отступ)
+# if num_x < 10:
+#     print("Small")
+# elif num_x < 100:
+#     print("Medium")
+# else:
+#     print("Large")
+
+# +
+# # запросим число у пользователя
+# number_x: str | int
+# number_x = input("Введите число: ")
+
+# # преобразуем в тип int
+# number_x = int(number_x)
+
+# # и наконец классифицируем
+# if number_x < 10:
+#     print("Small")
+# elif number_x < 100:
+#     print("Medium")
+# else:
+#     print("Large")
+
+# +
+# # Вложенные условия (nested decisions)
+# # запрашиваем число
+# number_y: str | int
+# number_y = input("Введите число: ")
+
+# # проверяем первое условие (не пустая ли строка), если оно выполняется
+# if len(number_y) != 0:
+
+#     # преобразуем в тип int
+#     number_y = int(number_y)
+
+#     # и классифицируем
+#     if number_x < 10:
+#         print("Small")
+#     elif number_x < 100:
+#         print("Medium")
+#     else:
+#         print("Large")
+
+# # в противном, говорим, что ввод пустой
+# else:
+#     print("Ввод пустой")
+
+# +
+# # Несколько условий в одном выражении с операторами and или or
+# # пример с and (логическим И)
+# number_z: int
+# number_z = 42
+
+# # если z больше 10 и одновременно меньше 100
+# if number_z > 10 and number_z < 100:
+
+#     # у нас среднее число
+#     print("Medium")
+
+# # в противном случае оно либо маленькое либо большое
+# else:
+#     print("Small or Large")
+
+# +
+# # пример с or (логическим ИЛИ)
+# numz: int
+# numz = 2
+
+# # если z меньше 10 или больше 100
+# if numz < 10 or numz > 100:
+
+#     # оно либо маленькое либо большое
+#     print("Small or Large")
+
+# # в противном случае оно среднее
+# else:
+#     print("Medium")
+
+# +
+# # можно проверить вхождение слова в строку
+# sentence: str
+# word: str
+# sentence = "To be, or not to be, that is the question"
+# word = "question"
+
+# if word in sentence:
+#     print("Слово найдено")
+
+# +
+# # или отсутствие элемента в списке
+# number_list: list[int]
+# number: int
+# number_list = [2, 3, 4, 6, 7]
+# number = 5
+
+# if number not in number_list:
+#     print("Такого числа в списке нет")
+
+# +
+# # кроме того, можно проверить вхождение ключа и значения в словарь
+
+# # возьмем очень простой словарь
+# tasty_list: dict[str, int]
+# tasty_list = {"apple": 3, "tomato": 6, "carrot": 2}
+
+# +
+# # вначале поищем яблоки среди ключей словаря
+# if "apple" in tasty_list:
+#     print("Нашлись")
+
+# +
+# # а затем посмотрим, нет ли числа 6 среди его значений
+# # с помощью метода .values()
+# if 6 in tasty_list.values():
+#     print("Есть")
+# -
+
+# ## Циклы в Питоне
+
+# +
+# # поочередно выведем элементы списка
+# second_number_list = [1, 2, 3]
+
+# # не забывая про двоеточие и отступ
+# for num in second_number_list:
+#     print(num)
+
+# +
+# # создадим словарь, значениями которого будут списки из двух элементов
+# scd_f = {"apple": [3, "kg"], "tomato": [6, "pcs"], "carrot": [2, "kg"]}
+
+# +
+# # затем создадим две переменные-контейнера и применим метод .items()
+# for key, value in scd_f.items():
+#     print(key, value)
+
+# +
+# # возьмем только одну переменную и применим метод .values()
+# for food in scd_f.values():
+#     # значение представляет собой список, выведем его первый элемент с индексом [0]
+#     print(food[0])
+
+# +
+# clients = dict[int, dict[str, str | int]]
+# clients = {
+#     1: {"name": "Анна", "age": 24, "sex": "male", "revenue": 12000},
+#     2: {"name": "Илья", "age": 18, "sex": "female", "revenue": 8000},
+# }
+
+# +
+# # в первом цикле for поместим id и информацию о клиентах в переменные id и info
+# for id, info in clients.items():
+
+#     # выведем id клиента
+#     print("client ID: " + str(id))
+
+#     # во втором цикле возьмем информацию об очередном клиенте (тоже словарь)
+#     for k, v in info.items():
+
+#         # и выведем каждый ключ (название поля) и значение (саму информацию)
+#         print(k + ": " + str(v))
+
+#     # добавим пустую строку после того, как выведем информацию об одном клиенте
+#     print()
+
+# +
+# # Функции range() и enumerate()
+# # создадим последовательность от 0 до 4
+# for nums1 in range(5):
+#     print(nums1)
+
+# +
+# # от 1 до 5
+# for nums2 in range(1, 6):
+#     print(nums2)
+
+# +
+# # и от 0 до 5 с шагом 2 (то есть будем выводить числа через одно)
+# for nums3 in range(0, 6, 2):
+#     print(nums3)
+# -
+
+# Функция range() принимает от одного до трех параметров.
+#
+# Если передать только один параметр, то мы начнем последовательность с нуля и закончим на элементе, предшествующем нашему параметру. В примере выше мы передали параметр «пять» (range(5)) и получили последовательность 0, 1, 2, 3, 4.
+# Если указать два параметра, то мы начнем последовательность с первого параметра и законим на элементе, предшествующем второму параметру. В частности, если написать range(1, 6), то получится 1, 2, 3, 4, 5.
+# Третий параметр устанавливает шаг. По умолчанию он равен единице, однако если, например, написать, range(0, 6, 2), то мы получим 0, 2, 4.
+# Что интересно, если совместить range() с функцией len(), то такую конструкцию можно использовать для того, чтобы в одном цикле вывести все элементы, например, двух списков по их индексу.
+
+# +
+# # возьмем месяцы года
+# months: list[str]
+# sales: list[int]
+# months = [
+#     "Январь",
+#     "Февраль",
+#     "Март",
+#     "Апрель",
+#     "Май",
+#     "Июнь",
+#     "Июль",
+#     "Август",
+#     "Сентябрь",
+#     "Октябрь",
+#     "Ноябрь",
+#     "Декабрь",
+# ]
+
+# # и продажи мороженого в тыс. рублей в каждый из месяцев
+# sales = [47, 75, 79, 94, 123, 209, 233, 214, 197, 130, 87, 55]
+
+# # задав последовательность через range(len()),
+# for mnts in range(len(months)):
+
+#     # мы можем вывести каждый из элементов обоих списков в одном цикле
+#     print(months[mnts], sales[mnts])
+# -
+
+# ## Способ 1. Использовать функцию reversed(). Эта функция меняет порядок элементов списка на обратный.
+#
+#
+
+# +
+# # создадим список
+# my_list2 = [0, 1, 2, 3, 4]
+
+# # передадим его функции reversed() и
+# # выведем каждый из элементов списка с помощью цикла for
+# for nms in reversed(my_list2):
+#     print(nms)
+
+# +
+# for numbrr in reversed(range(5)):
+#     print(numbrr)
+# -
+
+# ## Способ 2. Указать в качестве параметра шага. При этом важно, чтобы первым параметром указывался конечный элемент списка, а вторым — начальный.
+
+# +
+# for num_i in range(4, 0, -1):
+#     print(num_i)
+
+# +
+# for nms_i in range(4, -1, -1):
+#     print(nms_i)
+# -
+
+# ## Способ 3. Функция sorted(). Наконец, хотя в данном случае это явно не оптимальный вариант, можно использовать функцию sorted(), которая сортирует элементы списка по убыванию, если передать ей параметр reverse = True.
+
+# +
+# # создадим последовательность от 0 до 4
+# revrs = range(5)
+
+# # отсортируем ее по убыванию
+# sorted_values = sorted(revrs, reverse=True)
+
+# # выведем элементы отсортированной последовательности
+# for srt_num in sorted_values:
+#     print(srt_num)
+
+# +
+# # Функция enumerate()
+
+# # пусть дан список с днями недели
+# days: list[str]
+# days = [
+#     "Понедельник",
+#     "Вторник",
+#     "Среда",
+#     "Четверг",
+#     "Пятница",
+#     "Суббота",
+#     "Воскресенье",
+# ]
+
+# # выведем индекс (i) и сами элементы списка (day)
+# for indx, day in enumerate(days):
+#     print(indx, day)
+
+# +
+# # так же выведем индекс и элементы списка, но начнем с 1
+# for indx2, day in enumerate(days, 1):
+#     print(indx2, day)
+# -
+
+# ## Цикл while
+
+# +
+# # зададим начальное значение счетчика
+# my_num: int
+# my_num = 0
+
+# # пока счетчик меньше трех
+# while my_num < 3:
+
+#     # в каждом цикле будем выводить его текущее значение
+#     print("Текущее значение счетчика:  " + str(i))
+
+#     # внутри цикла не забудем "нарастить" счетчик
+#     my_num = my_num + 1
+
+#     # и выведем новое значение
+#     print("Новое значение счетчика:    " + str(my_num))
+
+#     # добавим пустую строку
+#     print()
+
+# +
+# # Операторы break и continue
+
+# # вновь возьмем словарь clients
+# sec_clients: dict[int, dict[str, str | int]]
+# sec_clients = {
+#     1: {"name": "Анна", "age": 24, "sex": "male", "revenue": 12000},
+#     2: {"name": "Илья", "age": 18, "sex": "female", "revenue": 8000},
+# }
+
+# # в цикле пройдемся по ключам и значениям словаря
+# for id2, info2 in sec_clients.items():
+
+#     # и выведем их
+#     print(id2, info2)
+
+#     # однако уже после первого исполнения цикла, прервем его
+#     break
+
+# +
+# # с помощью функции range создадим последовательность от 1 до 10
+# for cnt in range(1, 11):
+
+#     # если остаток от деления на два не равен нулю (то есть число нечетное)
+#     if cnt % 2 != 0:
+
+#         # идем к следующему числу последовательности
+#         continue
+
+#     # в противном случае выводим число
+#     else:
+#         print(cnt)
+
+# +
+# # Форматирование строк в функции print()
+# # снова возьмем список с днями недели
+# days2: list[str]
+# days2 = [
+#     "Понедельник",
+#     "Вторник",
+#     "Среда",
+#     "Четверг",
+#     "Пятница",
+#     "Суббота",
+#     "Воскресенье",
+# ]
+
+# # и для простоты поместим слово "Понедельник" в переменную Monday
+# Monday = days[0]
+# # Monday
+# print(f"{Monday} - день тяжелый")
diff --git a/python/makarov/chapter_1_data_types.ipynb b/python/makarov/chapter_1_data_types.ipynb
new file mode 100644
index 00000000..749d8b14
--- /dev/null
+++ b/python/makarov/chapter_1_data_types.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Типы данных.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Какие типы данных есть в Питоне\n",
+    "1) Целые числа - int\n",
+    "2) С плавающей точки - float\n",
+    "3) Комплексные - complex\n",
+    "4) Сторка - str\n",
+    "5) Логические значения - boolean\n",
+    "\n",
+    "# Работа с числами\n",
+    "Могут быть как int, float, complex "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2000.0 <class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# int_num: int\n",
+    "# float_num: float\n",
+    "# compl_num: complex\n",
+    "# int_num = 25\n",
+    "# float_num = 2.5\n",
+    "# compl_num = 3 + 25j\n",
+    "\n",
+    "# floatd: float\n",
+    "# floatd = 2e3\n",
+    "# print(floatd, type(floatd))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Операции с числами\n",
+    "# сложение, вычитание, умножение, деление, возведение в степень\n",
+    "# print(2 + 2, 4 - 2, 2 * 2, 4 / 2, 2**3)\n",
+    "\n",
+    "# разделим 7 на 2, и найдем целую часть и остаток\n",
+    "\n",
+    "# целая часть\n",
+    "# print(7 // 2)\n",
+    "\n",
+    "# остаток от деления\n",
+    "# print(7 % 2)\n",
+    "\n",
+    "# Операторы сравнения\n",
+    "# print(4 > 2, 4 < 2, 4 >= 2, 4 <= 2)\n",
+    "\n",
+    "# равенство\n",
+    "# print(2 == 4)\n",
+    "\n",
+    "# и новый для нас оператор неравенства\n",
+    "# print(2 != 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Кроме этого, существуют и чисто логические операции И, ИЛИ и НЕ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# логическое И, обе операции должны быть истинны\n",
+    "# print(4 > 2 and 2 != 3)\n",
+    "\n",
+    "# логическое ИЛИ, хотя бы одна из операций должна быть истинна\n",
+    "# print(4 < 2 or 2 == 2)\n",
+    "\n",
+    "# логическое НЕ, перевод истинного значения в ложное и наоборот\n",
+    "# print(not (4 == 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Перевод чисел в другую систему счисления\n",
+    "Перевод в двоичную систему, то есть в систему нулей и единиц, \n",
+    "можно сделать с помощью функции bin(). \n",
+    "Для обратного преобразования в десятичную систему достаточно функции int() \n",
+    "с указанием системы счисления, из которой происходит преобразование"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # создадим число в десятичной системе\n",
+    "# fltd: int\n",
+    "# fltd = 25\n",
+    "\n",
+    "# # переведем в двоичную (binary)\n",
+    "# bin_d = bin(fltd)\n",
+    "# print(bin_d)\n",
+    "\n",
+    "# # переведем обратно в десятичную\n",
+    "# print(int(bin_d, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Строковые данные \n",
+    "Такой тип данных можно разбить на несколько строк (multiline string). \n",
+    "Для этого нужно использовать тройные одинарные или двойные кавычки."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# string_1 = \"это строка\"\n",
+    "# string_2 = \"это тоже строка\"\n",
+    "\n",
+    "# multi_string = \"\"\"Мы все учились понемногу\n",
+    "# Чему-нибудь и как-нибудь,\n",
+    "# Так воспитаньем, слава богу,\n",
+    "# У нас немудрено блеснуть.\"\"\"\n",
+    "\n",
+    "# # Функция len() позволяет посчитать количество символов в строке.\n",
+    "\n",
+    "# len(multi_string)\n",
+    "\n",
+    "# # Две и более строки можно объединить друг с другом.\n",
+    "# # Пробелы, если они нужны, добавляются отдельно.\n",
+    "\n",
+    "# # создадим три строки\n",
+    "# prgr, on, pyth = \"Программирование\", \"на\", \"Питоне\"\n",
+    "\n",
+    "# # соединим с помощью + и добавим пробелы ' '\n",
+    "# prgr + \" \" + on + \" \" + pyth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Индекс символа в строке\n",
+    "При создании строки, Питон автоматически создает два индекса, \n",
+    "нумерует символы от начала и до конца (начиная с нуля, положительный индекс) \n",
+    "и с конца и до начала (начиная с −1, отрицательный индекс)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # выведем первый элемент строки multi_string\n",
+    "# print(multi_string[0])\n",
+    "\n",
+    "# # теперь выведем последний элемент\n",
+    "# print(multi_string[-1])\n",
+    "\n",
+    "# # Можно делать срезы и в строках\n",
+    "\n",
+    "# # выберем элементы с четвертого по шестой\n",
+    "# print(multi_string[3:6])\n",
+    "\n",
+    "# # выведем все элементы вплоть до второго\n",
+    "# print(multi_string[:2])\n",
+    "\n",
+    "# # а также все элементы, начиная с четвертого\n",
+    "# print(multi_string[3:])\n",
+    "\n",
+    "# # еще можно пройтись по строке с помощью for\n",
+    "# # выведем буквы в слове Питон\n",
+    "# for i in \"Питон\":\n",
+    "#     print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Методы .strip() и .split()\n",
+    "Метод .strip() удаляет символы в начале и в конце строки. \n",
+    "Это бывает полезно, если в базе данных значения записаны, \n",
+    "например, вместе со служебными символами и от них нужно избавиться."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # применим метод .strip(), чтобы удалить *\n",
+    "# print(\"***15 849 302*****\".strip(\"*\"))\n",
+    "\n",
+    "# # если ничего не указать в качестве аргумента, то удаляются пробелы по краям строки\n",
+    "# print(\" 15 849 302 \".strip())\n",
+    "\n",
+    "# # применим метод .split(), чтобы разделить строку на части\n",
+    "# print(multi_string.split())\n",
+    "\n",
+    "# # Сущестует метод для строк replace\n",
+    "# # предположим данные содержатся вот в таком формате\n",
+    "# data: str | float\n",
+    "# data = \"20,25\"\n",
+    "\n",
+    "# # теперь заменим ',' на '.'\n",
+    "# data = data.replace(\",\", \".\")\n",
+    "\n",
+    "# # и преобразуем в число\n",
+    "# data = float(data)\n",
+    "# print(data)\n",
+    "# print(type(data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Логические значения"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # создадим переменную и запишем в нее логическое значение True\n",
+    "# # (обязательно с большой буквы)\n",
+    "# varib = True\n",
+    "# type(varib)\n",
+    "\n",
+    "# # напишем небольшую программу, которая будет показывать,\n",
+    "# # какое значение содержится в переменной var\n",
+    "\n",
+    "# if varib is True:\n",
+    "#     print(\"Значение переменной истинно\")\n",
+    "# else:\n",
+    "#     print(\"Значение переменной ложно\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/chapter_1_data_types.py b/python/makarov/chapter_1_data_types.py
new file mode 100644
index 00000000..bed4ddc4
--- /dev/null
+++ b/python/makarov/chapter_1_data_types.py
@@ -0,0 +1,179 @@
+"""Типы данных."""
+
+# # Какие типы данных есть в Питоне
+# 1) Целые числа - int
+# 2) С плавающей точки - float
+# 3) Комплексные - complex
+# 4) Сторка - str
+# 5) Логические значения - boolean
+#
+# # Работа с числами
+# Могут быть как int, float, complex
+
+# +
+# int_num: int
+# float_num: float
+# compl_num: complex
+# int_num = 25
+# float_num = 2.5
+# compl_num = 3 + 25j
+
+# floatd: float
+# floatd = 2e3
+# print(floatd, type(floatd))
+
+# +
+# Операции с числами
+# сложение, вычитание, умножение, деление, возведение в степень
+# print(2 + 2, 4 - 2, 2 * 2, 4 / 2, 2**3)
+
+# разделим 7 на 2, и найдем целую часть и остаток
+
+# целая часть
+# print(7 // 2)
+
+# остаток от деления
+# print(7 % 2)
+
+# Операторы сравнения
+# print(4 > 2, 4 < 2, 4 >= 2, 4 <= 2)
+
+# равенство
+# print(2 == 4)
+
+# и новый для нас оператор неравенства
+# print(2 != 4)
+# -
+
+# ## Кроме этого, существуют и чисто логические операции И, ИЛИ и НЕ.
+
+# +
+# логическое И, обе операции должны быть истинны
+# print(4 > 2 and 2 != 3)
+
+# логическое ИЛИ, хотя бы одна из операций должна быть истинна
+# print(4 < 2 or 2 == 2)
+
+# логическое НЕ, перевод истинного значения в ложное и наоборот
+# print(not (4 == 4))
+# -
+
+# ## Перевод чисел в другую систему счисления
+# Перевод в двоичную систему, то есть в систему нулей и единиц,
+# можно сделать с помощью функции bin().
+# Для обратного преобразования в десятичную систему достаточно функции int()
+# с указанием системы счисления, из которой происходит преобразование
+
+# +
+# # создадим число в десятичной системе
+# fltd: int
+# fltd = 25
+
+# # переведем в двоичную (binary)
+# bin_d = bin(fltd)
+# print(bin_d)
+
+# # переведем обратно в десятичную
+# print(int(bin_d, 2))
+# -
+
+# ## Строковые данные
+# Такой тип данных можно разбить на несколько строк (multiline string).
+# Для этого нужно использовать тройные одинарные или двойные кавычки.
+
+# +
+# string_1 = "это строка"
+# string_2 = "это тоже строка"
+
+# multi_string = """Мы все учились понемногу
+# Чему-нибудь и как-нибудь,
+# Так воспитаньем, слава богу,
+# У нас немудрено блеснуть."""
+
+# # Функция len() позволяет посчитать количество символов в строке.
+
+# len(multi_string)
+
+# # Две и более строки можно объединить друг с другом.
+# # Пробелы, если они нужны, добавляются отдельно.
+
+# # создадим три строки
+# prgr, on, pyth = "Программирование", "на", "Питоне"
+
+# # соединим с помощью + и добавим пробелы ' '
+# prgr + " " + on + " " + pyth
+# -
+
+# ## Индекс символа в строке
+# При создании строки, Питон автоматически создает два индекса,
+# нумерует символы от начала и до конца (начиная с нуля, положительный индекс)
+# и с конца и до начала (начиная с −1, отрицательный индекс).
+
+# +
+# # выведем первый элемент строки multi_string
+# print(multi_string[0])
+
+# # теперь выведем последний элемент
+# print(multi_string[-1])
+
+# # Можно делать срезы и в строках
+
+# # выберем элементы с четвертого по шестой
+# print(multi_string[3:6])
+
+# # выведем все элементы вплоть до второго
+# print(multi_string[:2])
+
+# # а также все элементы, начиная с четвертого
+# print(multi_string[3:])
+
+# # еще можно пройтись по строке с помощью for
+# # выведем буквы в слове Питон
+# for i in "Питон":
+#     print(i)
+# -
+
+# ## Методы .strip() и .split()
+# Метод .strip() удаляет символы в начале и в конце строки.
+# Это бывает полезно, если в базе данных значения записаны,
+# например, вместе со служебными символами и от них нужно избавиться.
+
+# +
+# # применим метод .strip(), чтобы удалить *
+# print("***15 849 302*****".strip("*"))
+
+# # если ничего не указать в качестве аргумента, то удаляются пробелы по краям строки
+# print(" 15 849 302 ".strip())
+
+# # применим метод .split(), чтобы разделить строку на части
+# print(multi_string.split())
+
+# # Сущестует метод для строк replace
+# # предположим данные содержатся вот в таком формате
+# data: str | float
+# data = "20,25"
+
+# # теперь заменим ',' на '.'
+# data = data.replace(",", ".")
+
+# # и преобразуем в число
+# data = float(data)
+# print(data)
+# print(type(data))
+# -
+
+# ## Логические значения
+
+# +
+# # создадим переменную и запишем в нее логическое значение True
+# # (обязательно с большой буквы)
+# varib = True
+# type(varib)
+
+# # напишем небольшую программу, которая будет показывать,
+# # какое значение содержится в переменной var
+
+# if varib is True:
+#     print("Значение переменной истинно")
+# else:
+#     print("Значение переменной ложно")
diff --git a/python/makarov/chapter_1_datetime.ipynb b/python/makarov/chapter_1_datetime.ipynb
new file mode 100644
index 00000000..2e7fffff
--- /dev/null
+++ b/python/makarov/chapter_1_datetime.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "231736c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Date and time.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f9011e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from datetime import datetime, timedelta\n",
+    "\n",
+    "# import pytz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(datetime.now())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cur_dt: datetime = datetime.now()\n",
+    "# print(cur_dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(\n",
+    "#     cur_dt.year,\n",
+    "#     cur_dt.month,\n",
+    "#     cur_dt.day,\n",
+    "#     cur_dt.hour,\n",
+    "#     cur_dt.minute,\n",
+    "#     cur_dt.second,\n",
+    "#     cur_dt.microsecond,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(cur_dt.weekday(), cur_dt.isoweekday())\n",
+    "# print(cur_dt.tzinfo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dt_moscow: datetime = datetime.now(pytz.timezone(\"Europe/Moscow\"))\n",
+    "# print(dt_moscow)\n",
+    "# print(dt_moscow.tzinfo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# timestamp: float = datetime.now().timestamp()\n",
+    "# print(timestamp)\n",
+    "# print(datetime.fromtimestamp(timestamp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hb: datetime = datetime(1991, 2, 20)\n",
+    "# print(hb)\n",
+    "# print(hb.year)\n",
+    "# print(datetime.timestamp(hb))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# str_to_dt: str = \"2007-12-02 12:30:45\"\n",
+    "# res_dt: datetime = datetime.strptime(str_to_dt, \"%Y-%m-%d %H:%M:%S\")\n",
+    "# print(res_dt)\n",
+    "# print(type(res_dt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dt_to_str: datetime = datetime(2002, 11, 19)\n",
+    "# print(dt_to_str.strftime(\"%A, %B %d, %Y\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# date1: datetime = datetime(1905, 6, 30)\n",
+    "# date2: datetime = datetime(1916, 5, 11)\n",
+    "\n",
+    "# diff = date2 - date1\n",
+    "# print(diff)\n",
+    "# print(diff.days)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# future: datetime = datetime(2070, 1, 1)\n",
+    "# time_travel: timedelta = timedelta(days=62092)\n",
+    "# past = future - time_travel\n",
+    "# past"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cur_date: datetime = datetime(2021, 1, 1)\n",
+    "# end_date: datetime = datetime(2021, 1, 10)\n",
+    "\n",
+    "# while cur_date <= end_date:\n",
+    "#     print(cur_date.strftime(\"%b %d, %Y\"))\n",
+    "#     cur_date += timedelta(days=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# numbers: list[str] = [\"5\", \"10\", \"a\", \"15\", \"10\"]\n",
+    "# total: int = 0\n",
+    "\n",
+    "# for number in numbers:\n",
+    "#     try:\n",
+    "#         total += int(number)\n",
+    "#     except ValueError:\n",
+    "#         pass\n",
+    "\n",
+    "# total"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/python/makarov/chapter_1_datetime.py b/python/makarov/chapter_1_datetime.py
new file mode 100644
index 00000000..81f8421a
--- /dev/null
+++ b/python/makarov/chapter_1_datetime.py
@@ -0,0 +1,88 @@
+"""Date and time."""
+
+# +
+# from datetime import datetime, timedelta
+
+# import pytz
+
+# +
+# print(datetime.now())
+
+# +
+# cur_dt: datetime = datetime.now()
+# print(cur_dt)
+
+# +
+# print(
+#     cur_dt.year,
+#     cur_dt.month,
+#     cur_dt.day,
+#     cur_dt.hour,
+#     cur_dt.minute,
+#     cur_dt.second,
+#     cur_dt.microsecond,
+# )
+
+# +
+# print(cur_dt.weekday(), cur_dt.isoweekday())
+# print(cur_dt.tzinfo)
+
+# +
+# dt_moscow: datetime = datetime.now(pytz.timezone("Europe/Moscow"))
+# print(dt_moscow)
+# print(dt_moscow.tzinfo)
+
+# +
+# timestamp: float = datetime.now().timestamp()
+# print(timestamp)
+# print(datetime.fromtimestamp(timestamp))
+
+# +
+# hb: datetime = datetime(1991, 2, 20)
+# print(hb)
+# print(hb.year)
+# print(datetime.timestamp(hb))
+
+# +
+# str_to_dt: str = "2007-12-02 12:30:45"
+# res_dt: datetime = datetime.strptime(str_to_dt, "%Y-%m-%d %H:%M:%S")
+# print(res_dt)
+# print(type(res_dt))
+
+# +
+# dt_to_str: datetime = datetime(2002, 11, 19)
+# print(dt_to_str.strftime("%A, %B %d, %Y"))
+
+# +
+# date1: datetime = datetime(1905, 6, 30)
+# date2: datetime = datetime(1916, 5, 11)
+
+# diff = date2 - date1
+# print(diff)
+# print(diff.days)
+
+# +
+# future: datetime = datetime(2070, 1, 1)
+# time_travel: timedelta = timedelta(days=62092)
+# past = future - time_travel
+# past
+
+# +
+# cur_date: datetime = datetime(2021, 1, 1)
+# end_date: datetime = datetime(2021, 1, 10)
+
+# while cur_date <= end_date:
+#     print(cur_date.strftime("%b %d, %Y"))
+#     cur_date += timedelta(days=1)
+
+# +
+# numbers: list[str] = ["5", "10", "a", "15", "10"]
+# total: int = 0
+
+# for number in numbers:
+#     try:
+#         total += int(number)
+#     except ValueError:
+#         pass
+
+# total
diff --git a/python/makarov/chapter_1_dict.ipynb b/python/makarov/chapter_1_dict.ipynb
new file mode 100644
index 00000000..cff3983b
--- /dev/null
+++ b/python/makarov/chapter_1_dict.ipynb
@@ -0,0 +1,1146 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Словарь.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{} {}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # импортируем класс Counter\n",
+    "# from collections import Counter\n",
+    "# from pprint import pprint\n",
+    "\n",
+    "# # ключи мы поместим в кортеж\n",
+    "# # Словарь — неупорядоченный набор элементов с доступом по ключу.\n",
+    "# # Пустой словарь можно инициализировать через\n",
+    "# фигурные скобки {} или функцию dict().\n",
+    "# from typing import Any, Dict, List, Tuple\n",
+    "\n",
+    "# import numpy as np\n",
+    "\n",
+    "# dict_1: Dict\n",
+    "# dict_2: Dict\n",
+    "# dict_1, dict_2 = {}, dict()\n",
+    "# print(dict_1, dict_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Toyota', 'founded': 1937, 'founder': 'Kiichiro Toyoda'}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# company: Dict[str, str | int]\n",
+    "# company = {\"name\": \"Toyota\", \"founded\": 1937, \"founder\": \"Kiichiro Toyoda\"}\n",
+    "# company"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'TYO': 'Toyota', 'TSLA': 'Tesla', 'F': 'Ford'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# tickers: Dict[str, str]\n",
+    "# tickers = dict([[\"TYO\", \"Toyota\"], [\"TSLA\", \"Tesla\"], [\"F\", \"Ford\"]])\n",
+    "# tickers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'k1': 0, 'k2': 0, 'k3': 0}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# keys: Tuple[str]\n",
+    "# keys = (\"k1\", \"k2\", \"k3\")\n",
+    "\n",
+    "# # значением каждого ключа будет 0,\n",
+    "# # если ничего не указывать, ключи получат значение None\n",
+    "# value = 0\n",
+    "\n",
+    "# empty_k = dict.fromkeys(keys, value)\n",
+    "# empty_k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'k1': 123,\n",
+       " 'k2': 'string',\n",
+       " 'k3': nan,\n",
+       " 'k4': True,\n",
+       " 'k5': None,\n",
+       " 'k6': [1, 2, 3],\n",
+       " 'k7': array([1, 2, 3]),\n",
+       " 'k8': {1: 'v1', 2: 'v2', 3: 'v3'}}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# value_types: Dict[str, Any]\n",
+    "\n",
+    "# value_types = {\n",
+    "#     \"k1\": 123,\n",
+    "#     \"k2\": \"string\",\n",
+    "#     \"k3\": np.NaN,  # тип \"Пропущенное значение\"\n",
+    "#     \"k4\": True,  # логическое значение\n",
+    "#     \"k5\": None,\n",
+    "#     \"k6\": [1, 2, 3],\n",
+    "#     \"k7\": np.array([1, 2, 3]),\n",
+    "#     \"k8\": {1: \"v1\", 2: \"v2\", 3: \"v3\"},\n",
+    "# }\n",
+    "\n",
+    "# value_types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# person: Dict[str, Any]\n",
+    "# person = {\"first name\": \"Иван\", \"last name\":\n",
+    "# \"Иванов\", \"born\": 1980, \"dept\": \"IT\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['first name', 'last name', 'born', 'dept'])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_values(['Иван', 'Иванов', 1980, 'IT'])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person.values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_items([('first name', 'Иван'), ('last name', 'Иванов'), ('born', 1980), ('dept', 'IT')])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person.items()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first name Иван\n",
+      "last name Иванов\n",
+      "born 1980\n",
+      "dept IT\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for k, v in person.items():\n",
+    "#     print(k, v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Иванов'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person[\"last name\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# \"born\" in person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1980 in person.values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'first name': 'Иван',\n",
+       " 'last name': 'Иванов',\n",
+       " 'born': 1980,\n",
+       " 'dept': 'IT',\n",
+       " 'languages': ['Python', 'C++']}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # обратите внимание, в данном случае новое значение -\n",
+    "# это список\n",
+    "# person[\"languages\"] = [\"Python\", \"C++\"]\n",
+    "# person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'first name': 'Иван',\n",
+       " 'last name': 'Иванов',\n",
+       " 'born': 1980,\n",
+       " 'dept': 'IT',\n",
+       " 'languages': ['Python', 'C++'],\n",
+       " 'job': 'программист',\n",
+       " 'experience': 7}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # возьмем еще один словарь\n",
+    "# new_elements: Dict[str, str | int]\n",
+    "# new_elements = {\"job\": \"программист\", \"experience\": 7}\n",
+    "\n",
+    "# # и присоединим его к существующему словарю с\n",
+    "# помощью метода .update()\n",
+    "# person.update(new_elements)\n",
+    "# person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'first name': 'Иван',\n",
+       " 'last name': 'Иванов',\n",
+       " 'born': 1980,\n",
+       " 'dept': 'IT',\n",
+       " 'languages': ['Python', 'C++'],\n",
+       " 'job': 'программист',\n",
+       " 'experience': 7}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person.setdefault(\"last name\", \"Петров\")\n",
+    "# person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'first name': 'Иван',\n",
+       " 'last name': 'Иванов',\n",
+       " 'born': 1980,\n",
+       " 'dept': 'IT',\n",
+       " 'languages': ['Python', 'C++'],\n",
+       " 'job': 'программист',\n",
+       " 'experience': 7,\n",
+       " 'f_languages': ['русский', 'английский']}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person.setdefault(\"f_languages\", [\"русский\", \"английский\"])\n",
+    "# person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'IT'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person.pop(\"dept\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # удаляемое значение не выводится\n",
+    "# del person[\"born\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('f_languages', ['русский', 'английский'])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person.popitem()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# person.clear()\n",
+    "# person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # возьмем несложный словарь\n",
+    "# dict_to_sort: Dict[str, int]\n",
+    "# dict_to_sort = {\"k2\": 30, \"k1\": 20, \"k3\": 10}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['k1', 'k2', 'k3']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sorted(dict_to_sort)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # создадим исходный словарь с количеством студентов\n",
+    "# на первом и втором курсах университета\n",
+    "# original: Dict[str, int]\n",
+    "# original = {\"Первый курс\": 174, \"Второй курс\": 131}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Первый курс': 174, 'Второй курс': 131}\n",
+      "{'Первый курс': 174, 'Второй курс': 131, 'Третий курс': 117}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # создадим копию исходного словаря\n",
+    "# с помощью метода .copy()\n",
+    "# new_1 = original.copy()\n",
+    "\n",
+    "# # добавим информацию о третьем\n",
+    "# курсе в новый словарь\n",
+    "# new_1[\"Третий курс\"] = 117\n",
+    "\n",
+    "# # выведем исходный и новый словари\n",
+    "# print(original)\n",
+    "# print(new_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # передадим исходный словарь\n",
+    "# в новую переменную\n",
+    "# new_2 = original\n",
+    "\n",
+    "# # удалим элементы нового словаря\n",
+    "# new_2.clear()\n",
+    "\n",
+    "# # выведем исходный и новый словари\n",
+    "# print(original)\n",
+    "# print(new_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__class_getitem__',\n",
+       " '__contains__',\n",
+       " '__delattr__',\n",
+       " '__delitem__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__ior__',\n",
+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__or__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__reversed__',\n",
+       " '__ror__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'clear',\n",
+       " 'copy',\n",
+       " 'fromkeys',\n",
+       " 'get',\n",
+       " 'items',\n",
+       " 'keys',\n",
+       " 'pop',\n",
+       " 'popitem',\n",
+       " 'setdefault',\n",
+       " 'update',\n",
+       " 'values']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим словарь,\n",
+    "# some_dict: Dict[str, int]\n",
+    "# some_dict = {\"k\": 1}\n",
+    "\n",
+    "# # передадим его в функцию dir() и\n",
+    "# # выведем первые 11 элементов\n",
+    "# dir(some_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Dict comprehension\n",
+    "# # создадим еще один словарь\n",
+    "# source_dict: Dict[str, int]\n",
+    "# source_dict = {\"k1\": 2, \"k2\": 4, \"k3\": 6}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'k1': 4, 'k2': 8, 'k3': 12}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# {k: v * 2 for (k, v) in source_dict.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'K1': 2, 'K2': 4, 'K3': 6}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# {k.upper(): v for (k, v) in source_dict.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'k2': 4}"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# new_dict: Dict\n",
+    "# new_dict = {}\n",
+    "\n",
+    "# for k, v in source_dict.items():\n",
+    "#     if v > 2 and v < 6:\n",
+    "\n",
+    "# # если условия верны, записываем\n",
+    "# ключ и значение в новый словарь\n",
+    "#         new_dict[k] = v\n",
+    "\n",
+    "# new_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# words: List[str]\n",
+    "# words = [\"apple\", \"banana\", \"fig\", \"blackberry\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 6, 3, 10]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# length = list(map(lambda word: len(word), words))\n",
+    "# length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'apple': 5, 'banana': 6, 'fig': 3, 'blackberry': 10}"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# dict(zip(words, length))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# height_feet: Dict[str, float]\n",
+    "# height_feet = {\"Alex\": 6.1, \"Jerry\": 5.4, \"Ben\": 5.8}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.85928, 1.6459200000000003, 1.76784]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # один фут равен 0,3048 метра\n",
+    "# metres: List[float]\n",
+    "\n",
+    "# metres = list(map(lambda m: m * 0.3048, height_feet.values()))\n",
+    "# metres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# employees = {\n",
+    "#     \"id1\": {\n",
+    "#         \"first name\": \"Александр\",\n",
+    "#         \"last name\": \"Иванов\",\n",
+    "#         \"age\": 30,\n",
+    "#         \"job\": \"программист\",\n",
+    "#     },\n",
+    "#     \"id2\": {\n",
+    "#         \"first name\": \"Ольга\",\n",
+    "#         \"last name\": \"Петрова\",\n",
+    "#         \"age\": 35,\n",
+    "#         \"job\": \"ML-engineer\",\n",
+    "#     },\n",
+    "# }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'first name': 'Александр', 'last name': 'Иванов', 'age': 30, 'job': 'программист'}\n",
+      "{'first name': 'Ольга', 'last name': 'Петрова', 'age': 35, 'job': 'ML-engineer'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for v in employees.values():\n",
+    "#     print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # первый ключ - нужный нам сотрудник,\n",
+    "# второй - элемент с информацией о нем\n",
+    "# employees[\"id1\"][\"age\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id1': {'age': 30,\n",
+      "         'first name': 'Александр',\n",
+      "         'job': 'программист',\n",
+      "         'last name': 'Иванов'},\n",
+      " 'id2': {'age': 35,\n",
+      "         'first name': 'Ольга',\n",
+      "         'job': 'ML-engineer',\n",
+      "         'last name': 'Петрова'},\n",
+      " 'id3': {'age': 27,\n",
+      "         'first name': 'Дарья',\n",
+      "         'job': 'веб-дизайнер',\n",
+      "         'last name': 'Некрасова'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # добавим информацию о новом сотруднике\n",
+    "# employees[\"id3\"] = {\n",
+    "#     \"first name\": \"Дарья\",\n",
+    "#     \"last name\": \"Некрасова\",\n",
+    "#     \"age\": 27,\n",
+    "#     \"job\": \"веб-дизайнер\",\n",
+    "# }\n",
+    "\n",
+    "# # и выведем обновленный словарь с\n",
+    "# помощью функции pprint()\n",
+    "# pprint(employees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id1': {'age': 30.0,\n",
+      "         'first name': 'Александр',\n",
+      "         'job': 'программист',\n",
+      "         'last name': 'Иванов'},\n",
+      " 'id2': {'age': 35.0,\n",
+      "         'first name': 'Ольга',\n",
+      "         'job': 'ML-engineer',\n",
+      "         'last name': 'Петрова'},\n",
+      " 'id3': {'age': 27.0,\n",
+      "         'first name': 'Дарья',\n",
+      "         'job': 'веб-дизайнер',\n",
+      "         'last name': 'Некрасова'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# для этого вначале пройдемся по вложенным словарям,\n",
+    "# т.е. по значениям info внешнего словаря employees\n",
+    "# for info in employees.values():\n",
+    "\n",
+    "# затем по ключам и значениям вложенного словаря info\n",
+    "#     for k, v in info.items():\n",
+    "\n",
+    "# если ключ совпадет со словом 'age'\n",
+    "#         if k == \"age\":\n",
+    "\n",
+    "# преобразуем значение в тип float\n",
+    "#           info[k] = float(v)\n",
+    "\n",
+    "# pprint(employees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# corpus: str\n",
+    "# corpus = \"When we were in Paris we\n",
+    "# visited a lot of museums. We first went\n",
+    "# to the Louvre, the largest art museum in the world.\n",
+    "# I have always been\n",
+    "# interested in art so I spent many hours there.\n",
+    "# The museum is enormous, so a week there would not be enough.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['When', 'we', 'were', 'in', 'Paris', 'we', 'visited', 'a', 'lot', 'of', 'museums.', 'We', 'first', 'went', 'to', 'the', 'Louvre,', 'the', 'largest', 'art', 'museum', 'in', 'the', 'world.', 'I', 'have', 'always', 'been', 'interested', 'in', 'art', 'so', 'I', 'spent', 'many', 'hours', 'there.', 'The', 'museum', 'is', 'enourmous,', 'so', 'a', 'week', 'there', 'would', 'not', 'be', 'enough.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# words = corpus.split()\n",
+    "# print(words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['when', 'we', 'were', 'in', 'paris', 'we', 'visited', 'a', 'lot', 'of', 'museums', 'we', 'first', 'went', 'to', 'the', 'louvre', 'the', 'largest', 'art', 'museum', 'in', 'the', 'world', 'i', 'have', 'always', 'been', 'interested', 'in', 'art', 'so', 'i', 'spent', 'many', 'hours', 'there', 'the', 'museum', 'is', 'enourmous', 'so', 'a', 'week', 'there', 'would', 'not', 'be', 'enough']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# words: List[str]\n",
+    "# words = [word.strip(\".\").strip(\",\").lower() for word in words]\n",
+    "# print(words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('the', 4), ('we', 3), ('in', 3), ('a', 2), ('art', 2), ('museum', 2)]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# создадим пустой словарь для мешка слов bow\n",
+    "# bow_1: Dict\n",
+    "# bow_1 = {}\n",
+    "\n",
+    "# пройдемся по словам текста\n",
+    "# for word in words:\n",
+    "\n",
+    "# если нам встретилось слово,\n",
+    "# которое уже есть в словаре\n",
+    "#     if word in bow_1:\n",
+    "\n",
+    "#\n",
+    "# увеличим его значение (частоту) на 1\n",
+    "#         bow_1[word] = bow_1[word] + 1\n",
+    "\n",
+    "#\n",
+    "# в противном случае,\n",
+    "# если слово встречается впервые\n",
+    "#     else:\n",
+    "\n",
+    "#         # зададим ему значение 1\n",
+    "#         bow_1[word] = 1\n",
+    "\n",
+    "# отсортируем словарь по\n",
+    "# значению в убываюем порядке (reverse = True)\n",
+    "# и выведем шесть\n",
+    "# наиболее частотных слов\n",
+    "# sorted(bow_1.items(), key=lambda x: x[1], reverse=True)[:6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('the', 4), ('we', 3), ('in', 3), ('a', 2), ('art', 2), ('museum', 2)]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# bow_2: Dict\n",
+    "# bow_2 = {}\n",
+    "\n",
+    "# for word in words:\n",
+    "#     bow_2[word] = bow_2.get(word, 0) + 1\n",
+    "\n",
+    "# sorted(bow_2.items(), key=lambda x: x[1], reverse=True)[:6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('the', 4), ('we', 3), ('in', 3), ('a', 2), ('art', 2), ('museum', 2)]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим объект этого класса,\n",
+    "# передав ему список слов\n",
+    "# bow_3 = Counter(words)\n",
+    "\n",
+    "# # выведем шесть наиболее часто\n",
+    "# встречающихся слов с помощью метода .most_common()\n",
+    "# bow_3.most_common(6)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/chapter_1_dict.py b/python/makarov/chapter_1_dict.py
new file mode 100644
index 00000000..169c585e
--- /dev/null
+++ b/python/makarov/chapter_1_dict.py
@@ -0,0 +1,336 @@
+"""Словарь."""
+
+# +
+# # импортируем класс Counter
+# from collections import Counter
+# from pprint import pprint
+
+# # ключи мы поместим в кортеж
+# # Словарь — неупорядоченный набор элементов с доступом по ключу.
+# # Пустой словарь можно инициализировать через
+# фигурные скобки {} или функцию dict().
+# from typing import Any, Dict, List, Tuple
+
+# import numpy as np
+
+# dict_1: Dict
+# dict_2: Dict
+# dict_1, dict_2 = {}, dict()
+# print(dict_1, dict_2)
+
+# +
+# company: Dict[str, str | int]
+# company = {"name": "Toyota", "founded": 1937, "founder": "Kiichiro Toyoda"}
+# company
+
+# +
+# tickers: Dict[str, str]
+# tickers = dict([["TYO", "Toyota"], ["TSLA", "Tesla"], ["F", "Ford"]])
+# tickers
+
+# +
+# keys: Tuple[str]
+# keys = ("k1", "k2", "k3")
+
+# # значением каждого ключа будет 0,
+# # если ничего не указывать, ключи получат значение None
+# value = 0
+
+# empty_k = dict.fromkeys(keys, value)
+# empty_k
+
+# +
+# value_types: Dict[str, Any]
+
+# value_types = {
+#     "k1": 123,
+#     "k2": "string",
+#     "k3": np.NaN,  # тип "Пропущенное значение"
+#     "k4": True,  # логическое значение
+#     "k5": None,
+#     "k6": [1, 2, 3],
+#     "k7": np.array([1, 2, 3]),
+#     "k8": {1: "v1", 2: "v2", 3: "v3"},
+# }
+
+# value_types
+
+# +
+# person: Dict[str, Any]
+# person = {"first name": "Иван", "last name":
+# "Иванов", "born": 1980, "dept": "IT"}
+
+# +
+# person.keys()
+
+# +
+# person.values()
+
+# +
+# person.items()
+
+# +
+# for k, v in person.items():
+#     print(k, v)
+
+# +
+# person["last name"]
+
+# +
+# "born" in person
+
+# +
+# 1980 in person.values()
+
+# +
+# # обратите внимание, в данном случае новое значение -
+# это список
+# person["languages"] = ["Python", "C++"]
+# person
+
+# +
+# # возьмем еще один словарь
+# new_elements: Dict[str, str | int]
+# new_elements = {"job": "программист", "experience": 7}
+
+# # и присоединим его к существующему словарю с
+# помощью метода .update()
+# person.update(new_elements)
+# person
+
+# +
+# person.setdefault("last name", "Петров")
+# person
+
+# +
+# person.setdefault("f_languages", ["русский", "английский"])
+# person
+
+# +
+# person.pop("dept")
+
+# +
+# # удаляемое значение не выводится
+# del person["born"]
+
+# +
+# person.popitem()
+
+# +
+# person.clear()
+# person
+
+# +
+# # возьмем несложный словарь
+# dict_to_sort: Dict[str, int]
+# dict_to_sort = {"k2": 30, "k1": 20, "k3": 10}
+
+# +
+# sorted(dict_to_sort)
+
+# +
+# # создадим исходный словарь с количеством студентов
+# на первом и втором курсах университета
+# original: Dict[str, int]
+# original = {"Первый курс": 174, "Второй курс": 131}
+
+# +
+# # создадим копию исходного словаря
+# с помощью метода .copy()
+# new_1 = original.copy()
+
+# # добавим информацию о третьем
+# курсе в новый словарь
+# new_1["Третий курс"] = 117
+
+# # выведем исходный и новый словари
+# print(original)
+# print(new_1)
+
+# +
+# # передадим исходный словарь
+# в новую переменную
+# new_2 = original
+
+# # удалим элементы нового словаря
+# new_2.clear()
+
+# # выведем исходный и новый словари
+# print(original)
+# print(new_2)
+
+# +
+# # создадим словарь,
+# some_dict: Dict[str, int]
+# some_dict = {"k": 1}
+
+# # передадим его в функцию dir() и
+# # выведем первые 11 элементов
+# dir(some_dict)
+
+# +
+# # Dict comprehension
+# # создадим еще один словарь
+# source_dict: Dict[str, int]
+# source_dict = {"k1": 2, "k2": 4, "k3": 6}
+
+# +
+# {k: v * 2 for (k, v) in source_dict.items()}
+
+# +
+# {k.upper(): v for (k, v) in source_dict.items()}
+
+# +
+# new_dict: Dict
+# new_dict = {}
+
+# for k, v in source_dict.items():
+#     if v > 2 and v < 6:
+
+# # если условия верны, записываем
+# ключ и значение в новый словарь
+#         new_dict[k] = v
+
+# new_dict
+
+# +
+# words: List[str]
+# words = ["apple", "banana", "fig", "blackberry"]
+
+# +
+# length = list(map(lambda word: len(word), words))
+# length
+
+# +
+# dict(zip(words, length))
+
+# +
+# height_feet: Dict[str, float]
+# height_feet = {"Alex": 6.1, "Jerry": 5.4, "Ben": 5.8}
+
+# +
+# # один фут равен 0,3048 метра
+# metres: List[float]
+
+# metres = list(map(lambda m: m * 0.3048, height_feet.values()))
+# metres
+
+# +
+# employees = {
+#     "id1": {
+#         "first name": "Александр",
+#         "last name": "Иванов",
+#         "age": 30,
+#         "job": "программист",
+#     },
+#     "id2": {
+#         "first name": "Ольга",
+#         "last name": "Петрова",
+#         "age": 35,
+#         "job": "ML-engineer",
+#     },
+# }
+
+# +
+# for v in employees.values():
+#     print(v)
+
+# +
+# # первый ключ - нужный нам сотрудник,
+# второй - элемент с информацией о нем
+# employees["id1"]["age"]
+
+# +
+# # добавим информацию о новом сотруднике
+# employees["id3"] = {
+#     "first name": "Дарья",
+#     "last name": "Некрасова",
+#     "age": 27,
+#     "job": "веб-дизайнер",
+# }
+
+# # и выведем обновленный словарь с
+# помощью функции pprint()
+# pprint(employees)
+
+# +
+# для этого вначале пройдемся по вложенным словарям,
+# т.е. по значениям info внешнего словаря employees
+# for info in employees.values():
+
+# затем по ключам и значениям вложенного словаря info
+#     for k, v in info.items():
+
+# если ключ совпадет со словом 'age'
+#         if k == "age":
+
+# преобразуем значение в тип float
+#           info[k] = float(v)
+
+# pprint(employees)
+
+# +
+# corpus: str
+# corpus = "When we were in Paris we
+# visited a lot of museums. We first went
+# to the Louvre, the largest art museum in the world.
+# I have always been
+# interested in art so I spent many hours there.
+# The museum is enormous, so a week there would not be enough."
+
+# +
+# words = corpus.split()
+# print(words)
+
+# +
+# words: List[str]
+# words = [word.strip(".").strip(",").lower() for word in words]
+# print(words)
+
+# +
+# создадим пустой словарь для мешка слов bow
+# bow_1: Dict
+# bow_1 = {}
+
+# пройдемся по словам текста
+# for word in words:
+
+# если нам встретилось слово,
+# которое уже есть в словаре
+#     if word in bow_1:
+
+#
+# увеличим его значение (частоту) на 1
+#         bow_1[word] = bow_1[word] + 1
+
+#
+# в противном случае,
+# если слово встречается впервые
+#     else:
+
+#         # зададим ему значение 1
+#         bow_1[word] = 1
+
+# отсортируем словарь по
+# значению в убываюем порядке (reverse = True)
+# и выведем шесть
+# наиболее частотных слов
+# sorted(bow_1.items(), key=lambda x: x[1], reverse=True)[:6]
+
+# +
+# bow_2: Dict
+# bow_2 = {}
+
+# for word in words:
+#     bow_2[word] = bow_2.get(word, 0) + 1
+
+# sorted(bow_2.items(), key=lambda x: x[1], reverse=True)[:6]
+
+# +
+# # создадим объект этого класса,
+# передав ему список слов
+# bow_3 = Counter(words)
+
+# # выведем шесть наиболее часто
+# встречающихся слов с помощью метода .most_common()
+# bow_3.most_common(6)
diff --git a/python/makarov/chapter_1_func_in_py.ipynb b/python/makarov/chapter_1_func_in_py.ipynb
new file mode 100644
index 00000000..21152878
--- /dev/null
+++ b/python/makarov/chapter_1_func_in_py.ipynb
@@ -0,0 +1,2007 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0AoPSXyUTTLc"
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Функции в Питоне.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "W3QeADnwrI_g"
+   },
+   "source": [
+    "### Встроенные функции"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "mmuSaN9wrMpL"
+   },
+   "outputs": [],
+   "source": [
+    "# напомню, что мы создали список из кортежей\n",
+    "# и в каждом кортеже был\n",
+    "# индекс фильма и расстояние до него\n",
+    "# # функция может возвращать сразу два значения\n",
+    "# from typing import Callable, List, Tuple, Union\n",
+    "\n",
+    "# # импортируем библиотеки\n",
+    "# import matplotlib.pyplot as plt\n",
+    "\n",
+    "# # перед вызовом функции нужно не\n",
+    "# забыть импортировать соответствующую библиотеку\n",
+    "# import numpy as np\n",
+    "\n",
+    "# # установим точку отсчета\n",
+    "# np.random.seed(42)\n",
+    "# # и снова сгенерируем данные о росте\n",
+    "# (как мы делали на восьмом занятии вводного курса)\n",
+    "# height = list(np.round(np.random.normal(180, 10, 1000)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
+    },
+    "id": "GZzCE-GXsD8H",
+    "outputId": "cf0f65c1-33d8-4ba2-d3ac-7a131a645d16"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# # теперь построим гистограмму передав\n",
+    "# ей два параметра, данные о росте и количество интервалов\n",
+    "# # первый параметр у нас позиционный, второй - именованный\n",
+    "# plt.hist(height, bins=10)\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
+    },
+    "id": "If9utZiWZYeq",
+    "outputId": "83670d8d-9d21-435e-8be2-1d8e6d9d965b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# # первый параметр можно также сделать именованным (данные обозначаются через x)\n",
+    "# # и тогда порядок параметров можно менять\n",
+    "# plt.hist(bins=10, x=height)\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
+    },
+    "id": "5rM2p7inZ4gh",
+    "outputId": "bf17b7b5-681f-4f33-f60d-e998c1e52d45"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOtGh3gB6r3uWlIZ6CwAAS3EHBQAAWKfLgXLo0CFNmzZNXq9XLpdLO3fuDHreGKNly5bJ6/UqPj5eY8eO1alTp4LWBAIBFRYWKjk5Wf369VN+fr7Onj17R4MAAIDI0eVAuXjxooYOHao1a9Z0+PzKlSu1atUqrVmzRkePHpXH49GkSZPU1NTkrCkqKtKOHTu0bds2HT58WM3NzZo6daquXLly+5MAAICI0eX3oOTl5SkvL6/D54wxev311/XCCy9o5syZkqRNmzYpNTVVW7du1VNPPSWfz6f169dr8+bNmjhxoiRpy5YtysjI0P79+zV58uQ7GAcAAESCbn0PSk1Njerr65Wbm+uci4uL05gxY1RZWSlJOnbsmNra2oLWeL1eZWdnO2uuFQgE5Pf7gw4AABC5ujVQ6uvrJUmpqalB51NTU53n6uvrFRsbq/79+99wzbVKSkrkdrudIyMjozu3DQAALNMjn+JxuVxBj40x15271s3WLF26VD6fzzlqa2u7ba8AAMA+3RooHo9Hkq67E9LQ0ODcVfF4PGptbVVjY+MN11wrLi5OiYmJQQcAAIhc3RooWVlZ8ng8Kisrc861traqoqJCo0aNkiTl5OQoJiYmaE1dXZ1OnjzprAEAAL1blz/F09zcrE8//dR5XFNTo+PHjyspKUl33323ioqKtHz5cg0aNEiDBg3S8uXL1bdvX82ZM0eS5Ha7NX/+fC1atEgDBgxQUlKSFi9erCFDhjif6gGAcBOuPxn5zIqHQ70FoENdDpQPPvhA48aNcx4XFxdLkubOnauNGzfq2WefVUtLixYsWKDGxkaNGDFC+/btU0JCgvOa1atXKzo6WrNmzVJLS4smTJigjRs3KioqqhtGAgAA4c5ljDGh3kRX+f1+ud1u+Xw+3o8SxsL1b5xAJOEOCr5PXfn6ze/iAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgnW4PlGXLlsnlcgUdHo/Hed4Yo2XLlsnr9So+Pl5jx47VqVOnunsbAAAgjPXIHZQHHnhAdXV1znHixAnnuZUrV2rVqlVas2aNjh49Ko/Ho0mTJqmpqakntgIAAMJQjwRKdHS0PB6PcwwcOFDS/+6evP7663rhhRc0c+ZMZWdna9OmTbp06ZK2bt3aE1sBAABhqEcCpbq6Wl6vV1lZWXrsscf0+eefS5JqampUX1+v3NxcZ21cXJzGjBmjysrKntgKAAAIQ9HdfcERI0bonXfe0eDBg/X111/r97//vUaNGqVTp06pvr5ekpSamhr0mtTUVH3xxRc3vGYgEFAgEHAe+/3+7t42AACwSLcHSl5envPPQ4YM0YMPPqgf/ehH2rRpk0aOHClJcrlcQa8xxlx37rtKSkr0yiuvdPdWAQCApXr8Y8b9+vXTkCFDVF1d7Xya5+qdlKsaGhquu6vyXUuXLpXP53OO2traHt0zAAAIrR4PlEAgoI8//lhpaWnKysqSx+NRWVmZ83xra6sqKio0atSoG14jLi5OiYmJQQcAAIhc3f4tnsWLF2vatGm6++671dDQoN///vfy+/2aO3euXC6XioqKtHz5cg0aNEiDBg3S8uXL1bdvX82ZM6e7twIAAMJUtwfK2bNn9fjjj+ubb77RwIEDNXLkSB05ckSZmZmSpGeffVYtLS1asGCBGhsbNWLECO3bt08JCQndvRUAABCmXMYYE+pNdJXf75fb7ZbP5+PbPWHsniWlod4C0OudWfFwqLeAXqQrX7/5XTwAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArNPtP6gNABA+wvHnEfGzW3oH7qAAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOtEh3oD6B73LCkN9RYAAOg23EEBAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWCc61BsAAKAr7llSGuotdNmZFQ+HegthhzsoAADAOgQKAACwDoECAACsw3tQOhCO398EACCScAcFAABYJ6SBsnbtWmVlZalPnz7KycnR+++/H8rtAAAAS4QsUN577z0VFRXphRdeUFVVlX75y18qLy9PX375Zai2BAAALBGyQFm1apXmz5+vJ554Qvfdd59ef/11ZWRkaN26daHaEgAAsERI3iTb2tqqY8eOacmSJUHnc3NzVVlZed36QCCgQCDgPPb5fJIkv9/fI/trD1zqkesCAHqnu5/5v1BvoctOvjK526959eu2MabTtSEJlG+++UZXrlxRampq0PnU1FTV19dft76kpESvvPLKdeczMjJ6bI8AAPRm7td77tpNTU1yu903XRPSjxm7XK6gx8aY685J0tKlS1VcXOw8bm9v13//+18NGDCgw/XfN7/fr4yMDNXW1ioxMTHU2/le9ebZpd49f2+eXerd8/fm2aXePf+dzm6MUVNTk7xeb6drQxIoycnJioqKuu5uSUNDw3V3VSQpLi5OcXFxQed+8IMf9OQWb0tiYmKv+x/rVb15dql3z9+bZ5d69/y9eXapd89/J7N3dufkqpC8STY2NlY5OTkqKysLOl9WVqZRo0aFYksAAMAiIfsWT3FxsX7zm99o2LBhevDBB/XWW2/pyy+/1NNPPx2qLQEAAEuELFBmz56t8+fP69VXX1VdXZ2ys7O1Z88eZWZmhmpLty0uLk4vv/zydd+G6g168+xS756/N88u9e75e/PsUu+e//uc3WVu5bM+AAAA3yN+Fw8AALAOgQIAAKxDoAAAAOsQKAAAwDoEyg0cOnRI06ZNk9frlcvl0s6dO4OenzdvnlwuV9AxcuTIoDWBQECFhYVKTk5Wv379lJ+fr7Nnz36PU9yezmaXpI8//lj5+flyu91KSEjQyJEjg34TdbjOLnU+/7X/3a8ef/zjH5014Tp/Z7M3NzeroKBA6enpio+P13333XfdL/gM19mlzuf/+uuvNW/ePHm9XvXt21dTpkxRdXV10Jpwnb+kpETDhw9XQkKCUlJSNGPGDJ0+fTpojTFGy5Ytk9frVXx8vMaOHatTp04FrQnH+W9l9u3bt2vy5MlKTk6Wy+XS8ePHr7tOOM4udT5/W1ubnnvuOQ0ZMkT9+vWT1+vVb3/7W507dy7oOt09P4FyAxcvXtTQoUO1Zs2aG66ZMmWK6urqnGPPnj1BzxcVFWnHjh3atm2bDh8+rObmZk2dOlVXrlzp6e3fkc5m/+yzzzR69Gjde++9OnjwoP7973/rpZdeUp8+fZw14Tq71Pn83/1vXldXp7ffflsul0uPPvqosyZc5+9s9meeeUZ79+7Vli1b9PHHH+uZZ55RYWGhdu3a5awJ19mlm89vjNGMGTP0+eefa9euXaqqqlJmZqYmTpyoixcvOuvCdf6KigotXLhQR44cUVlZmS5fvqzc3Nyg2VauXKlVq1ZpzZo1Onr0qDwejyZNmqSmpiZnTTjOfyuzX7x4Ub/4xS+0YsWKG14nHGeXOp//0qVL+vDDD/XSSy/pww8/1Pbt2/XJJ58oPz8/6DrdPr9BpySZHTt2BJ2bO3eumT59+g1fc+HCBRMTE2O2bdvmnPvqq6/MXXfdZfbu3dtDO+1+Hc0+e/Zs8+tf//qGr4mU2Y3peP5rTZ8+3YwfP955HCnzdzT7Aw88YF599dWgcz//+c/Niy++aIyJnNmNuX7+06dPG0nm5MmTzrnLly+bpKQk87e//c0YE1nzNzQ0GEmmoqLCGGNMe3u78Xg8ZsWKFc6ab7/91rjdbvPmm28aYyJn/mtn/66amhojyVRVVQWdj5TZjbn5/Ff961//MpLMF198YYzpmfm5g3IHDh48qJSUFA0ePFhPPvmkGhoanOeOHTumtrY25ebmOue8Xq+ys7NVWVkZiu12i/b2dpWWlmrw4MGaPHmyUlJSNGLEiKBb4ZE6e0e+/vprlZaWav78+c65SJ5/9OjR2r17t7766isZY1ReXq5PPvlEkyf/79eyR/LsgUBAkoLuFEZFRSk2NlaHDx+WFFnz+3w+SVJSUpIkqaamRvX19UGzxcXFacyYMc5skTL/tbPfikiZXbq1+X0+n1wul/N78XpifgLlNuXl5endd9/VgQMH9Nprr+no0aMaP36884dYfX29YmNj1b9//6DXpaamXvdLEsNJQ0ODmpubtWLFCk2ZMkX79u3TI488opkzZ6qiokJS5M7ekU2bNikhIUEzZ850zkXy/G+88Ybuv/9+paenKzY2VlOmTNHatWs1evRoSZE9+7333qvMzEwtXbpUjY2Nam1t1YoVK1RfX6+6ujpJkTO/MUbFxcUaPXq0srOzJcnZ/7W/0PW7s0XC/B3NfisiYXbp1ub/9ttvtWTJEs2ZM8f5hYE9MX/IftR9uJs9e7bzz9nZ2Ro2bJgyMzNVWloa9MXqWsYYuVyu72OLPaK9vV2SNH36dD3zzDOSpJ/97GeqrKzUm2++qTFjxtzwteE+e0fefvtt/epXvwr6W/WNRML8b7zxho4cOaLdu3crMzNThw4d0oIFC5SWlqaJEyfe8HWRMHtMTIz++c9/av78+UpKSlJUVJQmTpyovLy8Tl8bbvMXFBToo48+cu4Mfde1c9zKbOE0/81mvx3hNLvU+fxtbW167LHH1N7errVr13Z6vTuZnzso3SQtLU2ZmZnOO/o9Ho9aW1vV2NgYtK6hoeG6v4GEk+TkZEVHR+v+++8POn/fffc5n+KJ1Nmv9f777+v06dN64okngs5H6vwtLS16/vnntWrVKk2bNk0//elPVVBQoNmzZ+tPf/qTpMid/aqcnBwdP35cFy5cUF1dnfbu3avz588rKytLUmTMX1hYqN27d6u8vFzp6enOeY/HI0nX/W34u7OF+/w3mv1WhPvsUufzt7W1adasWaqpqVFZWZlz90TqmfkJlG5y/vx51dbWKi0tTdL//iCLiYlRWVmZs6aurk4nT57UqFGjQrXNOxYbG6vhw4df9xG8Tz75xPlFj5E6+7XWr1+vnJwcDR06NOh8pM7f1tamtrY23XVX8B8bUVFRzp21SJ39Wm63WwMHDlR1dbU++OADTZ8+XVJ4z2+MUUFBgbZv364DBw440XVVVlaWPB5P0Gytra2qqKhwZgvX+Tub/VaE6+zSrc1/NU6qq6u1f/9+DRgwIOj5Hpn/tt5a2ws0NTWZqqoqU1VVZSSZVatWmaqqKvPFF1+YpqYms2jRIlNZWWlqampMeXm5efDBB80Pf/hD4/f7nWs8/fTTJj093ezfv998+OGHZvz48Wbo0KHm8uXLIZysczeb3Rhjtm/fbmJiYsxbb71lqqurzV/+8hcTFRVl3n//feca4Tq7MZ3Pb4wxPp/P9O3b16xbt67Da4Tr/J3NPmbMGPPAAw+Y8vJy8/nnn5sNGzaYPn36mLVr1zrXCNfZjel8/n/84x+mvLzcfPbZZ2bnzp0mMzPTzJw5M+ga4Tr/7373O+N2u83BgwdNXV2dc1y6dMlZs2LFCuN2u8327dvNiRMnzOOPP27S0tLC/s+9W5n9/PnzpqqqypSWlhpJZtu2baaqqsrU1dU5a8JxdmM6n7+trc3k5+eb9PR0c/z48aA1gUDAuU53z0+g3EB5ebmRdN0xd+5cc+nSJZObm2sGDhxoYmJizN13323mzp1rvvzyy6BrtLS0mIKCApOUlGTi4+PN1KlTr1tjo5vNftX69evNj3/8Y9OnTx8zdOhQs3PnzqBrhOvsxtza/H/9619NfHy8uXDhQofXCNf5O5u9rq7OzJs3z3i9XtOnTx/zk5/8xLz22mumvb3duUa4zm5M5/P/+c9/Nunp6c7/71988cWgP6CNCd/5O5pbktmwYYOzpr293bz88svG4/GYuLg489BDD5kTJ04EXScc57+V2Tds2NDhmpdfftlZE46zG9P5/Fc/Wt3RUV5e7lynu+d3/f/NAQAAWIP3oAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKzz/wCzEDh9T6K/6gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# # у параметра bins есть аргумент по умолчанию (как раз 10 интервалов),\n",
+    "# # а значит, этот параметр можно не указывать\n",
+    "# plt.hist(height)\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "t6UAoUmwag2c",
+    "outputId": "be491226-dbf0-437e-92ca-2626b106d815"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первая строка\n",
+      "\n",
+      "Третья строка\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # функция может не принимать параметров\n",
+    "# print(\"Первая строка\")\n",
+    "# print()\n",
+    "# print(\"Третья строка\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "o_fZPD7XJjFy",
+    "outputId": "6d528175-5a56-44d9-ea6e-f2f6e3f27dc6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Machine Learning'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # дана строка\n",
+    "# some_string: str\n",
+    "# some_string = \"machine learning\"\n",
+    "\n",
+    "# # применим метод .title()\n",
+    "# some_string.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 180
+    },
+    "id": "uVPQU3PoKwpR",
+    "outputId": "d0298065-b2bf-46ed-e917-4b63a77f7a3e"
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'title'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[15], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m some_list \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mmachine\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mlearning\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m      5\u001b[0m \u001b[39m# этот метод не применить\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m some_list\u001b[39m.\u001b[39mtitle()\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'title'"
+     ]
+    }
+   ],
+   "source": [
+    "# # к списку\n",
+    "# some_list: list[str]\n",
+    "# some_list = [\"machine\", \"learning\"]\n",
+    "\n",
+    "# # этот метод не применить\n",
+    "# some_list.title()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lIM5gFo4TbUg"
+   },
+   "source": [
+    "### Собственные функции в Питоне"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gcAxKrBnKkyO"
+   },
+   "source": [
+    "## Объявление и вызов функции\n",
+    "Функции не обязательно должны быть встроены в базовый функционал или библиотеки. Мы вполне можем объявлять (т.е. создавать) собственные функции (user-defined functions). Рассмотрим пример."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "IjjLVSbWqDpE"
+   },
+   "outputs": [],
+   "source": [
+    "# # создадим функцию, которая удваивает любое передаваемое ей значение\n",
+    "\n",
+    "\n",
+    "# def double(numb1: int) -> int:\n",
+    "#     \"\"\"Возвращает число умноженное на 2.\"\"\"\n",
+    "#     result1 = numb1 * 2\n",
+    "#     return result1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "EW6-h1F8ufBG",
+    "outputId": "a0d5d7fd-d9be-4cb9-caed-19fe60e0dd50"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # и вызовем ее, передав число 2\n",
+    "# double(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "id": "40pnqSnoTR0Z"
+   },
+   "outputs": [],
+   "source": [
+    "# # тело функции не может быть пустым\n",
+    "\n",
+    "\n",
+    "# def only_return():\n",
+    "#     # нужно либо указать ключевое слово return\n",
+    "#     return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "HuYLHkX7TZnt"
+   },
+   "outputs": [],
+   "source": [
+    "# only_return()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "tihYzRyawYD8"
+   },
+   "outputs": [],
+   "source": [
+    "# # либо оператор pass\n",
+    "\n",
+    "\n",
+    "# def only_pass():\n",
+    "#     \"\"\"Ничего не делает.\"\"\"\n",
+    "#     pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "dP_fIG04TLad"
+   },
+   "outputs": [],
+   "source": [
+    "# only_pass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u06oHGCNK6Rm",
+    "outputId": "9b258421-71f1-4f21-a34a-163090d90a61"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # такая функция вернет тип данных None (отсутствие значения)\n",
+    "# print(only_return())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "08Pbq-5VREZk"
+   },
+   "source": [
+    "#### Функция print() вместо return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "Jaj1wdaAYFF-"
+   },
+   "outputs": [],
+   "source": [
+    "# # можно использовать print(), но есть нюансы (см. на странице урока)\n",
+    "\n",
+    "\n",
+    "# def double_print(numb2: int) -> int:\n",
+    "#     \"\"\"Возвращает число умноженное на 2.\"\"\"\n",
+    "#     result2 = numb2 * 2\n",
+    "#     print(result2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XgtVgq_HYKyh",
+    "outputId": "b4ab9976-8f25-4588-eb4a-3c004cf2a79a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# double_print(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zmJv5y8dLUJK"
+   },
+   "source": [
+    "#### Параметры собственных функций"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "id": "uhOnVbWhuis_"
+   },
+   "outputs": [],
+   "source": [
+    "# def calc_sum(number1: int, number2: int) -> int:\n",
+    "#     \"\"\"Возвращает сумму двух целых чисел.\"\"\"\n",
+    "#     return number1 + number2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KTEhuHaYwWLQ",
+    "outputId": "30e9f2e3-bc34-4fb4-c4ee-039fd4b9d14d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # вызовем эту функцию с одним позиционным и одним именованным параметром\n",
+    "# calc_sum(1, number2=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "baCYb7BbT46W",
+    "outputId": "cc713d28-a136-46b4-f289-738972f55326"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # параметрам функции можно задать аргументы по умолчанию\n",
+    "\n",
+    "\n",
+    "# def calc_sum_default(number3: int = 1, number4: int = 2) -> int:\n",
+    "#     \"\"\"Возвращает сумму двух целых чисел.\"\"\"\n",
+    "#     return number3 + number4\n",
+    "\n",
+    "\n",
+    "# # и при вызове тогда их указывать не обязательно\n",
+    "# calc_sum_default()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "2DQF4G9w_JUW",
+    "outputId": "410687ae-5c34-45c7-899b-4fc02ab1e7ba"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Some string\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # функция может не иметь параметров\n",
+    "\n",
+    "\n",
+    "# def print_string() -> str:\n",
+    "#     \"\"\"Возвращает строку.\"\"\"\n",
+    "#     print(\"Some string\")\n",
+    "\n",
+    "\n",
+    "# print_string()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jZABsfhPx9wd"
+   },
+   "source": [
+    "#### Аннотация функции"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "id": "TAoI_kYPyB44"
+   },
+   "outputs": [],
+   "source": [
+    "# # укажем, что на входе функция принимает тип float, а возвращает int\n",
+    "# # значение 3,5 - это значение параметра x по умолчанию\n",
+    "\n",
+    "\n",
+    "# def f(number5: float = 3.5) -> int:\n",
+    "#     return int(number5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QaNrg6kZyW8Y",
+    "outputId": "690cf434-e9fe-48e9-8421-48f4ef96f0b1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'number5': float, 'return': int}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # желаемый тип данных можно посмотреть через атрибут __annotations__\n",
+    "# f.__annotations__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-iKjBO501oYJ",
+    "outputId": "81393c0e-d58e-4c90-ee7d-8ffb6df650fc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # вызовем функцию без параметров\n",
+    "# f()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "id": "ziFM30EY2Auj"
+   },
+   "outputs": [],
+   "source": [
+    "# # сохраним аннотации, но изменим суть функции\n",
+    "\n",
+    "\n",
+    "# def f(number6: float) -> int:\n",
+    "#     \"\"\"вместо int функция будет возвращать float\"\"\"\n",
+    "#     return float(number6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AdpR-rb92Qmn",
+    "outputId": "e5f3fb8c-5172-46e2-af70-ccfedce19921"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # вновь вызовем функцию, передав ей на входе int, и ожидая на выходе получить float\n",
+    "# f(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vlOHQZGZ_ESX"
+   },
+   "source": [
+    "#### Дополнительные возможности"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QittQ3qiK8S4",
+    "outputId": "8cfe36d8-0ce1-48de-e149-e3e56b436f27"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # вызов функции можно совмещать с арифметическими\n",
+    "# calc_sum(1, 2) * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "83RysL9MMQCw",
+    "outputId": "5d8d544e-2d52-48ba-bb46-816678e998bf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # и логическими операциями\n",
+    "# calc_sum(1, 2) > 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "YL1UrXNFb84A",
+    "outputId": "c7ba8f5f-6c9e-43cd-f9f5-e90f9197b38b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'P'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # можно и так\n",
+    "\n",
+    "\n",
+    "# def first_letter() -> str:\n",
+    "#     \"\"\"Возвращает строку.\"\"\"\n",
+    "#     return \"Python\"\n",
+    "\n",
+    "\n",
+    "# first_letter()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WC59uMaoWy8u",
+    "outputId": "36758b95-4a78-4327-a1a0-e91d65b4cb7a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # функция может не принимать параметров, но использовать input()\n",
+    "\n",
+    "\n",
+    "# def use_input() -> int:\n",
+    "#     \"\"\"Возвращает квадрат введеного числа/цифры.\"\"\"\n",
+    "#     # запросим у пользователя число и переведем его в тип данных int\n",
+    "#     user_inp = int(input(\"Введите число: \"))\n",
+    "\n",
+    "#     # возведем число в квадрат\n",
+    "#     result = user_inp**2\n",
+    "\n",
+    "#     # вернем результат\n",
+    "#     return result\n",
+    "\n",
+    "\n",
+    "# # вызовем функцию\n",
+    "# use_input()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7L2W-41oM6vK"
+   },
+   "source": [
+    "#### Результат вызова функции"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zVLZODr-PazE",
+    "outputId": "22dfbfe1-91d4-4b9d-fb67-2da06f5ae516"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # функция может возвращать также список, кортеж, словарь и др.\n",
+    "\n",
+    "\n",
+    "# # объявим функцию, которая на входе получает число,\n",
+    "# # а на выходе формирует список чисел от 0 и до числа, предшествующего заданному\n",
+    "# def create_list(pl_in: int) -> list[int]:\n",
+    "#     \"\"\"Возваращает список чисел от 0 до введеного.\"\"\"\n",
+    "#     # создадим пустой список\n",
+    "#     list_qt = []\n",
+    "\n",
+    "#     # в цикле for создадим последовательность\n",
+    "#     for nums in range(pl_in):\n",
+    "\n",
+    "#         # и поместим ее в список\n",
+    "#         list_qt.append(nums)\n",
+    "\n",
+    "#     return list_qt\n",
+    "\n",
+    "\n",
+    "# # результатом вызова этой функции будет список\n",
+    "# create_list(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "id": "RI4Xl_CHNm3B"
+   },
+   "outputs": [],
+   "source": [
+    "# \"\"\"Возвращает строку и целое число.\"\"\"\n",
+    "\n",
+    "\n",
+    "# def tuple_f() -> Tuple[str, int]:\n",
+    "#     string = \"Python\"\n",
+    "#     number7 = 42\n",
+    "#     return string, number7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "e4AXnLEdNzMJ",
+    "outputId": "f995daf9-ec64-45b9-cb00-c2efa88d4df5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 42\n",
+      "<class 'str'> <class 'int'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # если использовать две переменные\n",
+    "# number8, number9 = tuple_f()\n",
+    "\n",
+    "# # на выходе мы получим строку и число\n",
+    "# print(number8, number9)\n",
+    "# print(type(number8), type(number9))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "n89-CN1pSlgP",
+    "outputId": "55af7c8c-51e2-49f4-a438-32cf5f626d9b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Python', 42)\n",
+      "<class 'tuple'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # если одну\n",
+    "# number10 = tuple_f()\n",
+    "\n",
+    "# # получится кортеж\n",
+    "# print(number10)\n",
+    "# print(type(number10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bQzEDji5VFCY",
+    "outputId": "9d55cd09-ca42-48b7-c997-4da0009fe823"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # выводом может быть логическое значение (True или False)\n",
+    "\n",
+    "\n",
+    "# def if_divisible(div_n: int) -> bool:\n",
+    "#     \"\"\"Проверяет четное число или нет.\"\"\"\n",
+    "#     # если остаток от деления на два равен нулю\n",
+    "#     if div_n % 2 == 0:\n",
+    "\n",
+    "#         # вернем True\n",
+    "#         return True\n",
+    "\n",
+    "#     else:\n",
+    "\n",
+    "#         # в противном случае False\n",
+    "#         return False\n",
+    "\n",
+    "\n",
+    "# if_divisible(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uRNLyptXUoyj"
+   },
+   "source": [
+    "#### Использование библиотек"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "id": "gFTdRnLlUt4Y"
+   },
+   "outputs": [],
+   "source": [
+    "# # применим функцию mean() библиотеки Numpy для расчета среднего арифметического\n",
+    "\n",
+    "\n",
+    "# # на входе наша функция примет список или массив x,\n",
+    "# def mean_f(ls_mean: list) -> list:\n",
+    "#     \"\"\"Возварщает среднее арифметическое.\"\"\"\n",
+    "#     # рассчитает среднее арифметическое и прибавит единицу\n",
+    "#     return np.mean(ls_mean) + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OFeR_QpWU4i1",
+    "outputId": "897d5eb8-4091-4c75-c418-cfc67d9ee8ec"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # и подготовить данные\n",
+    "# my_list: List[int]\n",
+    "# my_list = [1, 2, 3]\n",
+    "\n",
+    "# mean_f(my_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kY91ANMfXeRG"
+   },
+   "source": [
+    "#### Глобальные и локальные переменные"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "id": "a4HSsRlBVBT2"
+   },
+   "outputs": [],
+   "source": [
+    "# # создадим глобальную переменную вне функции\n",
+    "# global_name: str\n",
+    "# global_name = \"Петр\"\n",
+    "\n",
+    "# # а затем используем ее внутри новой функции\n",
+    "\n",
+    "\n",
+    "# def show_name():\n",
+    "#     \"\"\"Выводит глобальную переменную.\"\"\"\n",
+    "#     print(global_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "_trqrh-zTjkk",
+    "outputId": "63d58f51-8dce-46e4-c1b3-a4891aaa7782"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Петр\n"
+     ]
+    }
+   ],
+   "source": [
+    "# show_name()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "id": "jmStHPcqTo3F"
+   },
+   "outputs": [],
+   "source": [
+    "# # а теперь вначале создадим функцию,\n",
+    "# # внутри которой объявим локальную переменную\n",
+    "\n",
+    "\n",
+    "# def show_local_name() -> str:\n",
+    "#     \"\"\"Выводит строку.\"\"\"\n",
+    "#     local_name = \"Алена\"\n",
+    "#     print(local_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9KMmXhAIUJkT",
+    "outputId": "59804313-d846-492d-d3a0-a2328b42da78"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Алена\n"
+     ]
+    }
+   ],
+   "source": [
+    "# show_local_name()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 162
+    },
+    "id": "mcINdfP0UYt9",
+    "outputId": "d6e8c80d-bd5c-40a3-e24c-15f5c2c2a307"
+   },
+   "outputs": [],
+   "source": [
+    "# при попытке обратиться к переменной\n",
+    "# вне функции мы получим ошибку\n",
+    "# local_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "id": "GfAMDzGvUcZ8"
+   },
+   "outputs": [],
+   "source": [
+    "# # превратить локальную\n",
+    "# переменную в глобальную можно через ключевое слово global\n",
+    "\n",
+    "\n",
+    "# def make_global() -> str:\n",
+    "#     \"\"\"Выводит строку.\"\"\"\n",
+    "#     global local_name\n",
+    "#     local_name = \"Алена\"\n",
+    "#     print(local_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "glXQZMD9UpDf",
+    "outputId": "98c62450-2d02-41e5-f5b1-c5d621fbf768"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Алена\n"
+     ]
+    }
+   ],
+   "source": [
+    "# make_global()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "GSHkMhwXUxQK",
+    "outputId": "59b647ec-d65d-45dc-b79d-8fc6e5068040"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Алена'"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # теперь ошибки быть не должно\n",
+    "# local_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "id": "rDAqC_PlZr35"
+   },
+   "outputs": [],
+   "source": [
+    "# # объявим глобальную переменную\n",
+    "# global_number: int\n",
+    "# global_number = 5\n",
+    "\n",
+    "\n",
+    "# def print_number() -> int:\n",
+    "#     \"\"\"Выводит число.\"\"\"\n",
+    "#     # затем объявим локальную переменную\n",
+    "#     local_number = 10\n",
+    "#     print(\"Local number:\", local_number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JvOjd91fGILS",
+    "outputId": "a9546480-1b54-49d3-cb3f-17c627cb6f5e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Local number: 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # функция всегда \"предпочтет\" локальную переменную\n",
+    "# print_number()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "306Y9HOWGIOf",
+    "outputId": "279d2fce-875c-487f-e008-a993a114ffcc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Global number: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # при этом значение глобальной\n",
+    "# переменной для остального кода не изменится\n",
+    "# print(\"Global number:\", global_number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xLJn10TGXhlq"
+   },
+   "source": [
+    "### Lambda-функции"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "CFPwBvbYXrYR",
+    "outputId": "c4aa46d3-1519-447a-a7d1-d26303a709bb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим функцию, которая\n",
+    "# принимает два числа и перемножает их\n",
+    "# lamb_f = lambda a, b: a * b\n",
+    "\n",
+    "# # вызовем функцию и передадим ей числа 2 и 3\n",
+    "# lamb_f(2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "65xVovHHXks9",
+    "outputId": "a77ace77-f3c0-45a2-f04e-2a620af0be7c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # этот же функционал можно поместить в обычную функцию\n",
+    "\n",
+    "\n",
+    "# def normal_f(nums1: int, nums2: int) -> int:\n",
+    "#     \"\"\"Возвращает произведение двух целых чисел.\"\"\"\n",
+    "#     return nums1 * nums2\n",
+    "\n",
+    "\n",
+    "# normal_f(2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "na0O4KAlE1Pk"
+   },
+   "source": [
+    "#### Lambda-функция внутри функции filter()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RMNawYCas6ER",
+    "outputId": "d19078fb-fcff-4356-d321-255babe9a192"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[15, 27, 18]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# создадим список\n",
+    "# ls_nums = list[int]\n",
+    "# ls_nums = [15, 27, 9, 18, 3, 1, 4]\n",
+    "\n",
+    "# напишем lambda-функцию, которая выведет True, если число больше 10, и False, если меньше\n",
+    "# criterion = lambda n: True if (n > 10) else False\n",
+    "\n",
+    "#  поместим список и lambda-функцию в функцию\n",
+    "# filter() и преобразуем результат в список\n",
+    "# list(filter(criterion, ls_nums))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Y86jO6CewXnU",
+    "outputId": "91857cd7-ed8d-4d41-aab2-c6834db21f4e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[15, 27, 18]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  все это можно записать в одну строчку\n",
+    "# list(filter(lambda n: True if (n > 10) else False, ls_nums))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VWAYPtGg0YQh",
+    "outputId": "d86cda49-30e0-420b-925c-3eaa1477dff3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[15, 27, 18]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ту же задачу можно решить через обычную функцию,\n",
+    "# но придется написать больше кода\n",
+    "\n",
+    "\n",
+    "# def criterion_2(num_n) -> bool:\n",
+    "#     \"\"\"Проверка больше ли числи 10 или нет.\"\"\"\n",
+    "#     if num_n > 10:\n",
+    "#         return True\n",
+    "#     else:\n",
+    "#         return False\n",
+    "\n",
+    "\n",
+    "# list(filter(criterion_2, ls_nums))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k9aPF9yVE9eF"
+   },
+   "source": [
+    "#### Lambda-функция внутри функции sorted()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dMFSmXtJYb39",
+    "outputId": "300c7e9a-6fa7-4f0b-ce0d-9b833e774a80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(901, 0.0), (1002, 0.22982440568634488), (442, 0.25401128310081567)]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# indices_distances: List[Tuple[int, float]] = [\n",
+    "#     (901, 0.0),\n",
+    "#     (1002, 0.22982440568634488),\n",
+    "#     (442, 0.25401128310081567),\n",
+    "# ]\n",
+    "\n",
+    "# # lambda-функция возьмет каждый кортеж и вернет второй [1] его элемент\n",
+    "# # передав эту функцию через параметр key, мы отсортируем список по расстоянию\n",
+    "# sorted(indices_distances, key=lambda x: x[1], reverse=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C8bwJf2oLI69"
+   },
+   "source": [
+    "#### Немедленно вызываемые функции"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XjbG4yHOK9in",
+    "outputId": "64f24a93-e73b-4525-ba3d-56e5c11f2b7e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # lambda-функцию можно вызвать сразу в момент объявления\n",
+    "# (lambda x: x * x)(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "id": "QxlLBRqLW0Mu"
+   },
+   "outputs": [],
+   "source": [
+    "# # *args и *kwargs\n",
+    "# # напишем функцию для расчета среднего арифметического двух чисел\n",
+    "\n",
+    "\n",
+    "# def mean(number11: int, number12: int) -> int:\n",
+    "#     \"\"\"Считает среднее арифметичское двух чисел.\"\"\"\n",
+    "#     return (number11 + number12) / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "EhuFz8wpXBVr",
+    "outputId": "d7cc2a22-979c-4b3b-d97a-5faae7db1700"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.5"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# mean(1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "id": "cyQLlIZLVIM3"
+   },
+   "outputs": [],
+   "source": [
+    "# # объявим функцию, которой нужно передать список\n",
+    "\n",
+    "\n",
+    "# def mean(list_):\n",
+    "#     \"\"\"Считает среднее арифметическое.\"\"\"\n",
+    "#     # зададим переменную для суммы,\n",
+    "#     total = 0\n",
+    "\n",
+    "#     # в цикле сложим все числа из списка\n",
+    "#     for i in list_:\n",
+    "#         total += i\n",
+    "\n",
+    "#     # и разделим на количество элементов\n",
+    "#     return total / len(list_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "nh7pebndVIQU",
+    "outputId": "95e69da6-ae02-44e3-be1b-728f04d7e0e6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.5"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим список\n",
+    "# list_: list[int]\n",
+    "# list_ = [1, 2, 3, 4]\n",
+    "\n",
+    "# # и передадим его в новую функцию\n",
+    "# mean(list_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 162
+    },
+    "id": "zy8lFJA9bkBc",
+    "outputId": "b18a1c1d-7999-4c94-c45f-e79c0d1e517d"
+   },
+   "outputs": [],
+   "source": [
+    "# однако новая функция уже не может работать с отдельными числами\n",
+    "# mean(1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "id": "vwrIY7CqVIYO"
+   },
+   "outputs": [],
+   "source": [
+    "# # объявим функцию с *args\n",
+    "\n",
+    "\n",
+    "# def mean(*nums) -> float:\n",
+    "#     \"\"\"Считает среднее арифметическое.\"\"\"\n",
+    "#     # в данном случае мы складываем элементы кортежа\n",
+    "#     total = 0\n",
+    "#     for i in nums:\n",
+    "#         total += i\n",
+    "\n",
+    "#     return total / len(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "P5Q1bhIVXz9p",
+    "outputId": "e96de475-c785-4a1b-a6ed-19081c5bb36b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.5"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # теперь мы можем передать функции отдельные числа\n",
+    "# mean(1, 2, 3, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bhHgfAZ-Ytmp",
+    "outputId": "dcd98549-4fcb-42f3-d0d8-9668bdee4e5d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.5"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # или список\n",
+    "# mean(*list_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L3NKa556r5O0",
+    "outputId": "eb9428ea-847e-4c7b-9e36-ab912a855ea7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 2, 3, 4) <class 'tuple'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # убедимся, что оператор распаковки * формирует кортеж\n",
+    "\n",
+    "\n",
+    "# def test_type(*nums) -> tuple:\n",
+    "#     \"\"\"Распаковывает и формирует кортеж.\"\"\"\n",
+    "#     print(nums, type(nums))\n",
+    "\n",
+    "\n",
+    "# test_type(1, 2, 3, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XHDvzgXFxql2",
+    "outputId": "05e55975-5e49-4bff-b0ee-e1fa91dad322"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 2, 3, 4) <class 'tuple'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # со списком происходит то же самое\n",
+    "# test_type(*list_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JeLoABCMy_om",
+    "outputId": "aac18950-74a2-4679-ada4-195e5dec3c56"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # для наглядности приведем еще один пример\n",
+    "# list1 = [1, 2, 3]\n",
+    "# list2 = [*list1, 4, 5, 6]\n",
+    "\n",
+    "# print(list2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "id": "gzEpZF_13Vfq"
+   },
+   "outputs": [],
+   "source": [
+    "# # **kwargs преобразует именованные параметры в словарь\n",
+    "\n",
+    "\n",
+    "# def funcc(**kwargs) -> dict:\n",
+    "#     \"\"\"Преобразует именованные параметры в словарь.\"\"\"\n",
+    "#     return kwargs.items()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rl4dALTK3plJ",
+    "outputId": "968969c7-1b3f-465c-c0a2-47728f41be11"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_items([('number13', 1), ('number14', 2)])"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# funcc(number13=1, number14=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "id": "Zd930nvoasfs"
+   },
+   "outputs": [],
+   "source": [
+    "# def simple_stats(*nums: Union[int, float], **params: bool) -> None:\n",
+    "#     \"\"\"Возвращает кортеж и словарь.\"\"\"\n",
+    "\n",
+    "#     if params.get(\"mean\") is True:\n",
+    "#         print(f\"mean:\\t{np.round(np.mean(nums), 3)}\")\n",
+    "\n",
+    "#     if params.get(\"std\") is True:\n",
+    "#         print(f\"std:\\t{np.round(np.std(nums), 3)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YQqRLpXvfYJZ",
+    "outputId": "224f2265-b5de-457d-80d2-631ae2eea610"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean:\t12.5\n",
+      "std:\t5.59\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # вызовем функцию simple_stats() и передадим ей числа и именованные аргументы\n",
+    "# simple_stats(5, 10, 15, 20, mean=True, std=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "h5VyRzsjn2ny",
+    "outputId": "ec65473b-1eae-43cf-dea1-08e02c33defb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean:\t12.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # если для одного из параметров задать значение False,\n",
+    "# # функция не выведет соответствующую метрику\n",
+    "# simple_stats(5, 10, 15, 20, mean=True, std=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J6TjmWJ3hbBv",
+    "outputId": "13efb4db-b19c-4470-b41e-fe0222c33a10"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean:\t2.5\n",
+      "std:\t1.118\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # если мы хотим передать параметры списком и словарем,\n",
+    "# llist_: list[int]\n",
+    "# settings: dict[str | bool]\n",
+    "# llist_ = [5, 10, 15, 20]\n",
+    "# settings = {\"mean\": True, \"std\": True}\n",
+    "\n",
+    "# # то нам нужно использовать операторы распаковки * и ** соответственно\n",
+    "# simple_stats(*list_, **settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "55FDFsesoAOP",
+    "outputId": "b70bb120-f3fb-4f91-8475-a0072abd4b75"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean:\t12.5\n",
+      "std:\t5.59\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # ничто не мешает нам добавить еще один параметр\n",
+    "# simple_stats(5, 10, 15, 20, mean=True, std=True, median=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python/makarov/chapter_1_func_in_py.py b/python/makarov/chapter_1_func_in_py.py
new file mode 100644
index 00000000..d3804a29
--- /dev/null
+++ b/python/makarov/chapter_1_func_in_py.py
@@ -0,0 +1,601 @@
+"""Функции в Питоне."""
+
+# ### Встроенные функции
+
+# +
+# напомню, что мы создали список из кортежей
+# и в каждом кортеже был
+# индекс фильма и расстояние до него
+# # функция может возвращать сразу два значения
+# from typing import Callable, List, Tuple, Union
+
+# # импортируем библиотеки
+# import matplotlib.pyplot as plt
+
+# # перед вызовом функции нужно не
+# забыть импортировать соответствующую библиотеку
+# import numpy as np
+
+# # установим точку отсчета
+# np.random.seed(42)
+# # и снова сгенерируем данные о росте
+# (как мы делали на восьмом занятии вводного курса)
+# height = list(np.round(np.random.normal(180, 10, 1000)))
+
+# +
+# # теперь построим гистограмму передав
+# ей два параметра, данные о росте и количество интервалов
+# # первый параметр у нас позиционный, второй - именованный
+# plt.hist(height, bins=10)
+# plt.show()
+
+# +
+# # первый параметр можно также сделать именованным (данные обозначаются через x)
+# # и тогда порядок параметров можно менять
+# plt.hist(bins=10, x=height)
+# plt.show()
+
+# +
+# # у параметра bins есть аргумент по умолчанию (как раз 10 интервалов),
+# # а значит, этот параметр можно не указывать
+# plt.hist(height)
+# plt.show()
+
+# +
+# # функция может не принимать параметров
+# print("Первая строка")
+# print()
+# print("Третья строка")
+
+# +
+# # дана строка
+# some_string: str
+# some_string = "machine learning"
+
+# # применим метод .title()
+# some_string.title()
+
+# +
+# # к списку
+# some_list: list[str]
+# some_list = ["machine", "learning"]
+
+# # этот метод не применить
+# some_list.title()
+# -
+
+# ### Собственные функции в Питоне
+
+# ## Объявление и вызов функции
+# Функции не обязательно должны быть встроены в базовый функционал или библиотеки. Мы вполне можем объявлять (т.е. создавать) собственные функции (user-defined functions). Рассмотрим пример.
+
+# +
+# # создадим функцию, которая удваивает любое передаваемое ей значение
+
+
+# def double(numb1: int) -> int:
+#     """Возвращает число умноженное на 2."""
+#     result1 = numb1 * 2
+#     return result1
+
+# +
+# # и вызовем ее, передав число 2
+# double(2)
+
+# +
+# # тело функции не может быть пустым
+
+
+# def only_return():
+#     # нужно либо указать ключевое слово return
+#     return
+
+# +
+# only_return()
+
+# +
+# # либо оператор pass
+
+
+# def only_pass():
+#     """Ничего не делает."""
+#     pass
+
+# +
+# only_pass()
+
+# +
+# # такая функция вернет тип данных None (отсутствие значения)
+# print(only_return())
+# -
+
+# #### Функция print() вместо return
+
+# +
+# # можно использовать print(), но есть нюансы (см. на странице урока)
+
+
+# def double_print(numb2: int) -> int:
+#     """Возвращает число умноженное на 2."""
+#     result2 = numb2 * 2
+#     print(result2)
+
+# +
+# double_print(5)
+# -
+
+# #### Параметры собственных функций
+
+# +
+# def calc_sum(number1: int, number2: int) -> int:
+#     """Возвращает сумму двух целых чисел."""
+#     return number1 + number2
+
+# +
+# # вызовем эту функцию с одним позиционным и одним именованным параметром
+# calc_sum(1, number2=2)
+
+# +
+# # параметрам функции можно задать аргументы по умолчанию
+
+
+# def calc_sum_default(number3: int = 1, number4: int = 2) -> int:
+#     """Возвращает сумму двух целых чисел."""
+#     return number3 + number4
+
+
+# # и при вызове тогда их указывать не обязательно
+# calc_sum_default()
+
+# +
+# # функция может не иметь параметров
+
+
+# def print_string() -> str:
+#     """Возвращает строку."""
+#     print("Some string")
+
+
+# print_string()
+# -
+
+# #### Аннотация функции
+
+# +
+# # укажем, что на входе функция принимает тип float, а возвращает int
+# # значение 3,5 - это значение параметра x по умолчанию
+
+
+# def f(number5: float = 3.5) -> int:
+#     return int(number5)
+
+# +
+# # желаемый тип данных можно посмотреть через атрибут __annotations__
+# f.__annotations__
+
+# +
+# # вызовем функцию без параметров
+# f()
+
+# +
+# # сохраним аннотации, но изменим суть функции
+
+
+# def f(number6: float) -> int:
+#     """вместо int функция будет возвращать float"""
+#     return float(number6)
+
+# +
+# # вновь вызовем функцию, передав ей на входе int, и ожидая на выходе получить float
+# f(3)
+# -
+
+# #### Дополнительные возможности
+
+# +
+# # вызов функции можно совмещать с арифметическими
+# calc_sum(1, 2) * 2
+
+# +
+# # и логическими операциями
+# calc_sum(1, 2) > 2
+
+# +
+# # можно и так
+
+
+# def first_letter() -> str:
+#     """Возвращает строку."""
+#     return "Python"
+
+
+# first_letter()[0]
+
+# +
+# # функция может не принимать параметров, но использовать input()
+
+
+# def use_input() -> int:
+#     """Возвращает квадрат введеного числа/цифры."""
+#     # запросим у пользователя число и переведем его в тип данных int
+#     user_inp = int(input("Введите число: "))
+
+#     # возведем число в квадрат
+#     result = user_inp**2
+
+#     # вернем результат
+#     return result
+
+
+# # вызовем функцию
+# use_input()
+# -
+
+# #### Результат вызова функции
+
+# +
+# # функция может возвращать также список, кортеж, словарь и др.
+
+
+# # объявим функцию, которая на входе получает число,
+# # а на выходе формирует список чисел от 0 и до числа, предшествующего заданному
+# def create_list(pl_in: int) -> list[int]:
+#     """Возваращает список чисел от 0 до введеного."""
+#     # создадим пустой список
+#     list_qt = []
+
+#     # в цикле for создадим последовательность
+#     for nums in range(pl_in):
+
+#         # и поместим ее в список
+#         list_qt.append(nums)
+
+#     return list_qt
+
+
+# # результатом вызова этой функции будет список
+# create_list(5)
+
+# +
+# """Возвращает строку и целое число."""
+
+
+# def tuple_f() -> Tuple[str, int]:
+#     string = "Python"
+#     number7 = 42
+#     return string, number7
+
+# +
+# # если использовать две переменные
+# number8, number9 = tuple_f()
+
+# # на выходе мы получим строку и число
+# print(number8, number9)
+# print(type(number8), type(number9))
+
+# +
+# # если одну
+# number10 = tuple_f()
+
+# # получится кортеж
+# print(number10)
+# print(type(number10))
+
+# +
+# # выводом может быть логическое значение (True или False)
+
+
+# def if_divisible(div_n: int) -> bool:
+#     """Проверяет четное число или нет."""
+#     # если остаток от деления на два равен нулю
+#     if div_n % 2 == 0:
+
+#         # вернем True
+#         return True
+
+#     else:
+
+#         # в противном случае False
+#         return False
+
+
+# if_divisible(10)
+# -
+
+# #### Использование библиотек
+
+# +
+# # применим функцию mean() библиотеки Numpy для расчета среднего арифметического
+
+
+# # на входе наша функция примет список или массив x,
+# def mean_f(ls_mean: list) -> list:
+#     """Возварщает среднее арифметическое."""
+#     # рассчитает среднее арифметическое и прибавит единицу
+#     return np.mean(ls_mean) + 1
+
+# +
+# # и подготовить данные
+# my_list: List[int]
+# my_list = [1, 2, 3]
+
+# mean_f(my_list)
+# -
+
+# #### Глобальные и локальные переменные
+
+# +
+# # создадим глобальную переменную вне функции
+# global_name: str
+# global_name = "Петр"
+
+# # а затем используем ее внутри новой функции
+
+
+# def show_name():
+#     """Выводит глобальную переменную."""
+#     print(global_name)
+
+# +
+# show_name()
+
+# +
+# # а теперь вначале создадим функцию,
+# # внутри которой объявим локальную переменную
+
+
+# def show_local_name() -> str:
+#     """Выводит строку."""
+#     local_name = "Алена"
+#     print(local_name)
+
+# +
+# show_local_name()
+
+# +
+# при попытке обратиться к переменной
+# вне функции мы получим ошибку
+# local_name
+
+# +
+# # превратить локальную
+# переменную в глобальную можно через ключевое слово global
+
+
+# def make_global() -> str:
+#     """Выводит строку."""
+#     global local_name
+#     local_name = "Алена"
+#     print(local_name)
+
+# +
+# make_global()
+
+# +
+# # теперь ошибки быть не должно
+# local_name
+
+# +
+# # объявим глобальную переменную
+# global_number: int
+# global_number = 5
+
+
+# def print_number() -> int:
+#     """Выводит число."""
+#     # затем объявим локальную переменную
+#     local_number = 10
+#     print("Local number:", local_number)
+
+# +
+# # функция всегда "предпочтет" локальную переменную
+# print_number()
+
+# +
+# # при этом значение глобальной
+# переменной для остального кода не изменится
+# print("Global number:", global_number)
+# -
+
+# ### Lambda-функции
+
+# +
+# # создадим функцию, которая
+# принимает два числа и перемножает их
+# lamb_f = lambda a, b: a * b
+
+# # вызовем функцию и передадим ей числа 2 и 3
+# lamb_f(2, 3)
+
+# +
+# # этот же функционал можно поместить в обычную функцию
+
+
+# def normal_f(nums1: int, nums2: int) -> int:
+#     """Возвращает произведение двух целых чисел."""
+#     return nums1 * nums2
+
+
+# normal_f(2, 3)
+# -
+
+# #### Lambda-функция внутри функции filter()
+
+# +
+# создадим список
+# ls_nums = list[int]
+# ls_nums = [15, 27, 9, 18, 3, 1, 4]
+
+# напишем lambda-функцию, которая выведет True, если число больше 10, и False, если меньше
+# criterion = lambda n: True if (n > 10) else False
+
+#  поместим список и lambda-функцию в функцию
+# filter() и преобразуем результат в список
+# list(filter(criterion, ls_nums))
+
+# +
+#  все это можно записать в одну строчку
+# list(filter(lambda n: True if (n > 10) else False, ls_nums))
+
+# +
+# ту же задачу можно решить через обычную функцию,
+# но придется написать больше кода
+
+
+# def criterion_2(num_n) -> bool:
+#     """Проверка больше ли числи 10 или нет."""
+#     if num_n > 10:
+#         return True
+#     else:
+#         return False
+
+
+# list(filter(criterion_2, ls_nums))
+# -
+
+# #### Lambda-функция внутри функции sorted()
+
+# +
+# indices_distances: List[Tuple[int, float]] = [
+#     (901, 0.0),
+#     (1002, 0.22982440568634488),
+#     (442, 0.25401128310081567),
+# ]
+
+# # lambda-функция возьмет каждый кортеж и вернет второй [1] его элемент
+# # передав эту функцию через параметр key, мы отсортируем список по расстоянию
+# sorted(indices_distances, key=lambda x: x[1], reverse=False)
+# -
+
+# #### Немедленно вызываемые функции
+
+# +
+# # lambda-функцию можно вызвать сразу в момент объявления
+# (lambda x: x * x)(10)
+
+# +
+# # *args и *kwargs
+# # напишем функцию для расчета среднего арифметического двух чисел
+
+
+# def mean(number11: int, number12: int) -> int:
+#     """Считает среднее арифметичское двух чисел."""
+#     return (number11 + number12) / 2
+
+# +
+# mean(1, 2)
+
+# +
+# # объявим функцию, которой нужно передать список
+
+
+# def mean(list_):
+#     """Считает среднее арифметическое."""
+#     # зададим переменную для суммы,
+#     total = 0
+
+#     # в цикле сложим все числа из списка
+#     for i in list_:
+#         total += i
+
+#     # и разделим на количество элементов
+#     return total / len(list_)
+
+# +
+# # создадим список
+# list_: list[int]
+# list_ = [1, 2, 3, 4]
+
+# # и передадим его в новую функцию
+# mean(list_)
+
+# +
+# однако новая функция уже не может работать с отдельными числами
+# mean(1, 2)
+
+# +
+# # объявим функцию с *args
+
+
+# def mean(*nums) -> float:
+#     """Считает среднее арифметическое."""
+#     # в данном случае мы складываем элементы кортежа
+#     total = 0
+#     for i in nums:
+#         total += i
+
+#     return total / len(nums)
+
+# +
+# # теперь мы можем передать функции отдельные числа
+# mean(1, 2, 3, 4)
+
+# +
+# # или список
+# mean(*list_)
+
+# +
+# # убедимся, что оператор распаковки * формирует кортеж
+
+
+# def test_type(*nums) -> tuple:
+#     """Распаковывает и формирует кортеж."""
+#     print(nums, type(nums))
+
+
+# test_type(1, 2, 3, 4)
+
+# +
+# # со списком происходит то же самое
+# test_type(*list_)
+
+# +
+# # для наглядности приведем еще один пример
+# list1 = [1, 2, 3]
+# list2 = [*list1, 4, 5, 6]
+
+# print(list2)
+
+# +
+# # **kwargs преобразует именованные параметры в словарь
+
+
+# def funcc(**kwargs) -> dict:
+#     """Преобразует именованные параметры в словарь."""
+#     return kwargs.items()
+
+# +
+# funcc(number13=1, number14=2)
+
+# +
+# def simple_stats(*nums: Union[int, float], **params: bool) -> None:
+#     """Возвращает кортеж и словарь."""
+
+#     if params.get("mean") is True:
+#         print(f"mean:\t{np.round(np.mean(nums), 3)}")
+
+#     if params.get("std") is True:
+#         print(f"std:\t{np.round(np.std(nums), 3)}")
+
+# +
+# # вызовем функцию simple_stats() и передадим ей числа и именованные аргументы
+# simple_stats(5, 10, 15, 20, mean=True, std=True)
+
+# +
+# # если для одного из параметров задать значение False,
+# # функция не выведет соответствующую метрику
+# simple_stats(5, 10, 15, 20, mean=True, std=False)
+
+# +
+# # если мы хотим передать параметры списком и словарем,
+# llist_: list[int]
+# settings: dict[str | bool]
+# llist_ = [5, 10, 15, 20]
+# settings = {"mean": True, "std": True}
+
+# # то нам нужно использовать операторы распаковки * и ** соответственно
+# simple_stats(*list_, **settings)
+
+# +
+# # ничто не мешает нам добавить еще один параметр
+# simple_stats(5, 10, 15, 20, mean=True, std=True, median=True)
diff --git a/python/makarov/chapter_1_google_colab.ipynb b/python/makarov/chapter_1_google_colab.ipynb
new file mode 100644
index 00000000..ea07a9cc
--- /dev/null
+++ b/python/makarov/chapter_1_google_colab.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Google colab.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Модуль os и метод .walk()\n",
+    "# импортируем модуль os\n",
+    "# import os\n",
+    "\n",
+    "# импортируем библиотеку\n",
+    "# import pandas as pd\n",
+    "\n",
+    "# импортируем класс StandardScaler\n",
+    "# from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# выводим пути к папкам (dirpath)\n",
+    "# и наименования файлов (filenames) и после этого\n",
+    "# for dirpath, _, filenames in os.walk(\"/content/\"):\n",
+    "\n",
+    "#  во вложенном цикле проходимся по названиям файлов\n",
+    "#     for filename in filenames:\n",
+    "\n",
+    "# и соединяем путь до папок и входящие в эти папки файлы\n",
+    "# с помощью метода path.join()\n",
+    "#         print(os.path.join(dirpath, filename))\n",
+    "\n",
+    "# Кроме того, если нас интересуют\n",
+    "# только видимые файлы и папки,\n",
+    "# мы можем воспользоваться командой !ls (ls означает to list, т.е. «перечислить»)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# посмотрим на тип значений словаря uploaded\n",
+    "# type(uploaded[\"test.csv\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Использование функции open() и конструкции with open()\n",
+    "# передадим функции open() адрес файла\n",
+    "# параметр 'r' означает, что мы хотим прочитать (read) файл\n",
+    "# f1 = open(\"/content/train.csv\")\n",
+    "\n",
+    "# метод .read() помещает весь файл в одну строку\n",
+    "# выведем первые 142 символа (если параметр не указывать, выведется все содержимое)\n",
+    "# print(f1.read(142))\n",
+    "\n",
+    "# в конце файл необходимо закрыть\n",
+    "# f1.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# снова откроем файл\n",
+    "# f2 = open(\"/content/train.csv\")\n",
+    "\n",
+    "# пройдемся по нашему объекту в цикле for\n",
+    "# и параллельно создадим индекс\n",
+    "# for i, line in enumerate(f2):\n",
+    "\n",
+    "# выведем строки без служебных символов по краям\n",
+    "#     print(line.strip())\n",
+    "\n",
+    "# дойдя до четвертой строки, прервемся\n",
+    "#     if i == 3:\n",
+    "#         break\n",
+    "\n",
+    "# не забудем закрыть файл\n",
+    "# f2.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# применим функцию read_csv()\n",
+    "# и посмотрим на первые три записи файла train.csv\n",
+    "# train = pd.read_csv(\"/content/train.csv\")\n",
+    "# train.head(3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/chapter_1_google_colab.py b/python/makarov/chapter_1_google_colab.py
new file mode 100644
index 00000000..2782821e
--- /dev/null
+++ b/python/makarov/chapter_1_google_colab.py
@@ -0,0 +1,68 @@
+"""Google colab."""
+
+# +
+# Модуль os и метод .walk()
+# импортируем модуль os
+# import os
+
+# импортируем библиотеку
+# import pandas as pd
+
+# импортируем класс StandardScaler
+# from sklearn.preprocessing import StandardScaler
+
+# выводим пути к папкам (dirpath)
+# и наименования файлов (filenames) и после этого
+# for dirpath, _, filenames in os.walk("/content/"):
+
+#  во вложенном цикле проходимся по названиям файлов
+#     for filename in filenames:
+
+# и соединяем путь до папок и входящие в эти папки файлы
+# с помощью метода path.join()
+#         print(os.path.join(dirpath, filename))
+
+# Кроме того, если нас интересуют
+# только видимые файлы и папки,
+# мы можем воспользоваться командой !ls (ls означает to list, т.е. «перечислить»)
+
+# +
+# посмотрим на тип значений словаря uploaded
+# type(uploaded["test.csv"])
+
+# +
+# Использование функции open() и конструкции with open()
+# передадим функции open() адрес файла
+# параметр 'r' означает, что мы хотим прочитать (read) файл
+# f1 = open("/content/train.csv")
+
+# метод .read() помещает весь файл в одну строку
+# выведем первые 142 символа (если параметр не указывать, выведется все содержимое)
+# print(f1.read(142))
+
+# в конце файл необходимо закрыть
+# f1.close()
+
+# +
+# снова откроем файл
+# f2 = open("/content/train.csv")
+
+# пройдемся по нашему объекту в цикле for
+# и параллельно создадим индекс
+# for i, line in enumerate(f2):
+
+# выведем строки без служебных символов по краям
+#     print(line.strip())
+
+# дойдя до четвертой строки, прервемся
+#     if i == 3:
+#         break
+
+# не забудем закрыть файл
+# f2.close()
+
+# +
+# применим функцию read_csv()
+# и посмотрим на первые три записи файла train.csv
+# train = pd.read_csv("/content/train.csv")
+# train.head(3)
diff --git a/python/makarov/chapter_1_numpy.ipynb b/python/makarov/chapter_1_numpy.ipynb
new file mode 100644
index 00000000..cad5e065
--- /dev/null
+++ b/python/makarov/chapter_1_numpy.ipynb
@@ -0,0 +1,2408 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Numpy.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Массив Numpy — это многомерный\n",
+    "# массив (ndarray, n-dimensional array) данных,\n",
+    "# над которыми можно быстро и эффективно выполнять\n",
+    "# множество математических, статистических,\n",
+    "# логических и других операций.\n",
+    "\n",
+    "# импортируем библиотеку matplotlib\n",
+    "# import matplotlib.pyplot as plt\n",
+    "# import numpy as np\n",
+    "\n",
+    "# импортируем функцию csr_matrix()\n",
+    "# from scipy.sparse import csr_matrix\n",
+    "\n",
+    "# создадим массив из списка\n",
+    "# arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "# arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# или кортежа\n",
+    "# arr = np.array((0, 1, 2, 3, 4, 5, 6, 7, 8, 9))\n",
+    "# arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'float' object cannot be interpreted as an integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# создадим список с помощью функций range() и list()\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mlist\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m2\u001b[39m, \u001b[39m5.5\u001b[39m, \u001b[39m0.5\u001b[39m))\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
+     ]
+    }
+   ],
+   "source": [
+    "# создадим список с помощью функций range() и list()\n",
+    "# list(range(2, 5.5, 0.5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.arange(2, 5.5, 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n",
+      "float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# создадим массив с элементами типа float\n",
+    "# arr_f = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], float)\n",
+    "\n",
+    "# print(arr_f)\n",
+    "\n",
+    "# тип данных можно посмотреть через атрибут dtype\n",
+    "# print(arr_f.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# arr.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10,)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# arr.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# пока что у нас одно измерение, в котором четыре элемента\n",
+    "# arr.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int64')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# в нашем случае - это целое число длиной 64 бита\n",
+    "# arr.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# arr.itemsize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # у нас четыре элемента по восемь байтов или 32 байта\n",
+    "# arr.nbytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(42)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Массив с нулевой размерностью — это число (скаляр) и квадратных скобок не имеет.\n",
+    "\n",
+    "# arr_0D = np.array(42)\n",
+    "# arr_0D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "()\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(arr_0D.ndim)\n",
+    "# print(arr_0D.shape)\n",
+    "# print(arr_0D.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Одномерный массив (вектор)\n",
+    "# arr_1D = np.array([1, 2, 3])\n",
+    "# arr_1D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "(3,)\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(arr_1D.ndim)\n",
+    "# print(arr_1D.shape)\n",
+    "# print(arr_1D.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Двумерный массив (матрица)\n",
+    "# с точки зрения синтаксиса - это просто вложенные списки\n",
+    "# arr_2D = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "# arr_2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "(2, 3)\n",
+      "6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(arr_2D.ndim)\n",
+    "# print(arr_2D.shape)\n",
+    "# print(arr_2D.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1],\n",
+       "       [2],\n",
+       "       [3]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# column = np.array([[1], [2], [3]])\n",
+    "# column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 1)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# посмотрим на размерность\n",
+    "# column.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# row = np.array([[1, 2, 3]])\n",
+    "# row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 3)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# размерность будет иной\n",
+    "# row.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2],\n",
+       "        [ 3,  4,  5]],\n",
+       "\n",
+       "       [[ 6,  7,  8],\n",
+       "        [ 9, 10, 11]]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Трехмерный массив\n",
+    "# arr_3D = np.arange(12).reshape(2, 2, 3)\n",
+    "# arr_3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "(2, 2, 3)\n",
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(arr_3D.ndim)\n",
+    "# print(arr_3D.shape)\n",
+    "# print(arr_3D.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Массив из нулей\n",
+    "# ей мы можем передать одно значение для создания одномерного массива\n",
+    "# np.zeros(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0.],\n",
+       "       [0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# или кортеж из чисел для указания количества нулей в каждом измерении\n",
+    "# np.zeros((2, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[1., 1., 1.],\n",
+       "        [1., 1., 1.]],\n",
+       "\n",
+       "       [[1., 1., 1.],\n",
+       "        [1., 1., 1.]]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Массив из единиц\n",
+    "# создадим трехмерный массив\n",
+    "# np.ones((2, 2, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4, 4, 4],\n",
+       "       [4, 4, 4]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Массив, заполненный заданным значением\n",
+    "# np.full((2, 3), 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0.],\n",
+       "       [0., 0.],\n",
+       "       [0., 0.]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Пустой массив Numpy\n",
+    "# создадим пустую матрицу 3 х 2\n",
+    "# np.empty((3, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# любой массив Numpy можно преобразовать\n",
+    "# в описанные выше массивы с помощью функций\n",
+    "# np.zeros_like(), np.ones_like(),\n",
+    "# np.full_like() и np.empty_like().\n",
+    "# Приведу пример для np.zeros_like().\n",
+    "# создадим массив 2 x 3 с числами от 1 до 6\n",
+    "# arr1 = np.arange(1, 7).reshape(2, 3)\n",
+    "# arr1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0],\n",
+       "       [0, 0, 0]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# и превратим его в массив с нулями\n",
+    "# np.zeros_like(arr1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Функция np.linspace()\n",
+    "# создадим диапазон от 0 до 0,9 и\n",
+    "# разделим его на десять точек, включая 0 и 0,9\n",
+    "# np.linspace(0, 0.9, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# с функцией np.arange мы точно знаем, где будут расположены точки\n",
+    "# np.arange(0, 1, 0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Lj12F+95qZmkBAAgmtefRD22fqPRmwXHV7fdRaAPEj/q3kiS9vzZXHq/PcBoAANAULMuqO93g6r6tDKcxh0IbIEZ2TlGLmHAVlrm0aEeh6TgAAKAJrMs5qt2FNVfdXtYjzXQcYyi0ASI81Kmretesm3mXZQcAAASF2u/5l3UPrqtuv49CG0Bqlx3M25yv4gq34TQAAKAxVbm9dUd2XntepuE0ZlFoA0j3jHh1SYtTtdenj9ZzJi0AAIFs3uZ8lVZ5lNEsUkPaJZqOYxSFNoA4HI66WVqWHQAAENhqv9df07+VnM7gO3v2uyi0AWZcn5YKcTq0LueodhWUmY4DAAAaQV5xlRbtKJD07ZLDYEahDTDJcREa0SlZEmfSAgAQqN5fe0A+SxrYpoWyEmNMxzHO7wrt1KlTNWDAAMXFxSklJUXjx4/Xtm3bjnsfy7I0ZcoUZWRkKCoqSiNHjtSmTZsMJfY/ta/UZq05IK/PMpwGAAA0JMuy9O7qHEnMztbyu0K7YMECTZo0ScuWLdO8efPk8Xg0evRolZeX173PU089pWeeeUbPPfecVq5cqbS0NI0aNUqlpaUGk/uPi7umqFlUmPJKqrRkF2fSAgAQSGqWFZYrKixEl/dKNx3HL/hdof388881ceJEde/eXb1799arr76q7OxsrV69WlLNq5Jnn31Wv/rVrzRhwgT16NFDr732mioqKjRjxgzD6f1DRGiIxvau+QRn2QEAAIGldjPYmB7Bffbsd/n9s1BcXCxJatGihSRpz549ysvL0+jRo+veJyIiQiNGjNCSJUt09913n/B3uFwuuVyuut+XlJRIktxut9zuwDyvdXzvdE1flq3PN+XpcGml4iIbd6hrn8dAfT4DHeNnf4yh/TGG9tZU4+f6ztmz4/ukBfTnS33+3/y60FqWpcmTJ+v8889Xjx49JEl5eXmSpNTU1OPeNzU1Vfv27Tvp3zN16lQ98cQTJzw+d+5cRUdHN3Bq/2BZUmpUiPIrfXrqzXkakto0a2nnzZvXJB8HjYPxsz/G0P4YQ3tr7PFbU+hQaVWImodbKtqyXJ9ubdQPZ1RFRcUZv69fF9r77rtP69ev1+LFi094m8Nx/HlrlmWd8Fitxx57TJMnT677fUlJiTIzMzV69GjFx8c3bGg/sj9uj/48b4d2ehP1u8sHNurHcrvdmjdvnkaNGqWwsLBG/VhoeIyf/TGG9scY2ltTjd+7r62WVKSbhrbXlRd3aLSP4w9qf6J+Jvy20N5///368MMPtXDhQrVq9e0OvrS0NEk1M7Xp6d8uhD506NAJs7a1IiIiFBERccLjYWFhAf2Pxo/Oa61nvtihVfuOKrekukmO9Qj05zTQMX72xxjaH2Nob405fnnFVfp6V5Ek6boBrQP+86Q+/39+tynMsizdd999mjVrlr788ku1bdv2uLe3bdtWaWlpx03pV1dXa8GCBRo6dGhTx/Vrac0iNaxDkiTpvTUHDKcBAADnYtba/Zw9ewp+V2gnTZqk6dOna8aMGYqLi1NeXp7y8vJUWVkpqWapwYMPPqg//OEPev/997Vx40ZNnDhR0dHRuummmwyn9z/fnkm7Xz7OpAUAwJZqzp6tOd2As2dP5HdLDqZNmyZJGjly5HGPv/rqq5o4caIk6eGHH1ZlZaXuvfdeHTlyRIMGDdLcuXMVFxfXxGn93+huaYqLCNX+I5VatrtIQ4/N2AIAAPtYm3NUuzl79pT8rtBa1g/PIjocDk2ZMkVTpkxp/EA2FxUeorF9MjRjebbeXpVDoQUAwIbqzp7tydmzJ+N3Sw7Q8K47L1OS9NnGPBVXBu55dQAABKIqt1cfHTt7luUGJ0ehDQK9WzVT59Q4uTy+usOYAQCAPczdnK/SKo9aJkRpcNtE03H8EoU2CDgcDl17Xs0rundW5RhOAwAA6qP2e/c1/VvJ6Tz5mfvBjkIbJK7u21JhIQ6t31+sLQfP/KBiAABgzoGjlVq8s1CSdE2/lobT+C8KbZBIjI3QJV1rLp54Z9V+w2kAAMCZeHfVflmWNLgdZ8+eDoU2iNRuDnt/7X65PF7DaQAAwOn4fJbeWV2z3OD6AZmG0/g3Cm0QGd4xSanxETpS4dZ/txwyHQcAAJzGkl1F2n+kUnGRoRrTg7NnT4dCG0RCQ5x1x328zeYwAAD82sxj36vH92mpyLAQw2n8G4U2yFzbv+ZHFgu3F+hgcaXhNAAA4GSOVlRrzqY8SSw3OBMU2iDTJilGA9u2kM+S3lvN5jAAAPzR7LUHVO3xqVt6vHq0bGY6jt+j0Aah649tDnt71X75fD981TAAAGg6lmVp5rETiZidPTMU2iBUew909uEKrdh72HQcAADwHRsPlGjLwRKFhzo1vg9nz54JCm0Qig4P1djeNbsl317J5jAAAPzJWyuzJUmXdU9Ts+gww2nsgUIbpGrPpP1040GVVLkNpwEAAJJUWe3Vh+tyJbHcoD4otEGqT2aCOqbEqsrt08ffHDQdBwAASPps40GVujzKbBGlIe0STcexDQptkHI4HHWztJxJCwCAf5h5bCngtf0z5XQ6DKexDwptELu6X0uFOh1al3NU2/NLTccBACCo7S0s1/I9h+VwqO4iJJwZCm0QS4qN0MVdUyR9+4oQAACYUfsT0ws6JisjIcpwGnuh0Aa52gXns9bsl8vjNZwGAIDg5PH69O5qzp49WxTaIHdBx2SlN4vUkQq35m7KNx0HAICgtGB7gQ6VutQiJlyXdE01Hcd2KLRBLjTEqWuPbQ57c0W24TQAAASn2qV/V/dtqfBQ6ll98YxB1w/IlMMhLdlVpL2F5abjAAAQVApKXfpy6yFJLDc4WxRaqGVClEZ0SpYkvcXmMAAAmtR7a/bL47PUJzNBnVLjTMexJQotJEk3DmwtSXp3dY6qPT7DaQAACA4+n6W3ji35u4HZ2bNGoYUk6aIuKUqOi1BhWbX+u4XNYQAANIVlu4u0t6hCsRGhGts7w3Qc26LQQpIUFuLUdefVHOL8JssOAABoEjOOzc6O65OhmIhQw2nsi0KLOtefV7PsYNGOAuUcrjCcBgCAwFZU5tKcTXmSvl36h7NDoUWd1onROr9Dkizr29tKAABA43hvzX65vZZ6tWqmHi2bmY5jaxRaHKf2FeLbq3Lk8bI5DACAxmBZlt5cUTN5dBOzs+eMQovjjOqWqsSYcOWXuPTVtgLTcQAACEhLdxdpT2G5YsJD2AzWACi0OE54qFM/6l+zOewtbg4DAKBR1M7Ojuvbks1gDYBCixPU3lLy1bZDOlhcaTgNAACBpajMpTkbazaDsdygYVBocYJ2ybEa3K6FfJb09sr9puMAABBQZq05oGqvj81gDYhCi5Oq3Rw2c2W2vD7LcBoAAAJDzWawmiV9HNXVcCi0OKlLu6cpITpMucVVWriDzWEAADSEZbsPazebwRochRYnFRkWogl9j90ctpzNYQAANITa2dmr+rRULJvBGgyFFqd048CazWH/3XpIh0qqDKcBAMDeDpdX6/Njm8FuHsRyg4ZEocUpdUyN03lZzeX1WZq5kpvDAAA4F7PW7Fe116eeLdkM1tAotDitmwfXvIJ8cwWbwwAAOFuWZWkGm8EaDYUWpzWmR7qaH9sc9uXWQ6bjAABgS8v3HNbugprNYFf1YTNYQ6PQ4rQiw0J03Xk1a2mnL9tnOA0AAPbEZrDGRaHFD7rp2ML1hTsKlF1UYTgNAAD2UlTm0mcbajaD1W64RsOi0OIHZSXGaHjHJFmW6tb/AACAM/P2qv11N4P1apVgOk5AotDijNwyOEuS9PaqHLk8XsNpAACwB6/P0owVNUv2ar+XouFRaHFGLu6SovRmkcedoQcAAE5v4fYC5RyuVHxkqMb2YjNYY6HQ4oyEhjh1w4CatbRvLGPZAQAAZ6J2Q/W152UqKjzEcJrARaHFGbthYKZCnA6t2HtY2/JKTccBAMCv5Ryu0Jfbao685GawxkWhxRlLjY/UqK6pkqQ3lnOEFwAAp/PmimxZlnR+hyS1S441HSegUWhRL7UL2metOaByl8dwGgAA/JPL4627Nv6WwczONjYKLeplaPtEtU2KUZnLow+/yTUdBwAAv/T5xjwVlVcrNT5Clxz76SYaD4UW9eJ0OurWAU1ftk+WZRlOBACA/6ndDHbjwNYKDaFuNTaeYdTbNf1aKTzUqU25JVqXc9R0HAAA/MrWvBKt3HtEIU6HbhzIcoOmQKFFvTWPCdeVvdIlSdM5wgsAgOPUzs6O7paq1PhIw2mCA4UWZ6V2c9jH63N1tKLacBoAAPxDmcuj99cckCTdys1gTYZCi7PSNzNB3dLj5fL49O7q/abjAADgFz745qDKq71qlxyjIe0TTccJGhRanBWHw6Gbjx1D8sbybPl8bA4DAAQ3y5LeXHHsqK5BWXI4HIYTBQ8KLc7a+D4tFRcRqj2F5fp6V5HpOAAAGLW7VNqWX6bIMKeu6d/KdJygQqHFWYuJCNWPzqv5gn19OZvDAADB7ev8mlo1rndLNYsKM5wmuFBocU5uG9JGkjR/e6EKq8xmAQDAlKIyl9YV1SwxuIXNYE2OQotz0jYpRiM6JcuypMV5fDoBAILTzFUH5LUc6tUyXj1bNTMdJ+jQQHDObh9a80p02SGHKqu9htMAANC03F6fZqys2Qx262AuUjCBQotzNrJTijKbR6nS69CH6w+ajgMAQJOauylf+SUuxYZZGtMjzXScoEShxTlzOh26ZVCmpJqbwyyLI7wAAMHjtSV7JUlDUyxFhFKtTOBZR4O4pl9LhTktbc0v08q9R0zHAQCgSWzOLdGKvYcV6nRoWKrPdJygRaFFg2gWFabzkmpmZl9butdsGAAAmkjt7OzobilKiDCbJZhRaNFghqfVvDL9fGOe8oo5wwsAENiOlFdr9roDktgMZhqFFg2mZYw0oE1zeX2WZizfZzoOAACNauaqHLk8PnVLj1f/1gmm4wQ1Ci0a1K3HNofNWJEtl4cjvAAAgcnrs/T60prJm4lD28jhcBhOFNwotGhQl3RNUVp8pArLqvXZhjzTcQAAaBRfbMnXgaOVah4dpqv6ZJiOE/QotGhQYSFO3TyoZh0Rm8MAAIGqdjPY9QNaKzIsxGwYUGjR8G4Y2FphIQ6tzT6q9fuPmo4DAECD2p5fqiW7iuR0SLewGcwvUGjR4JLjInRFz3RJ0mtL2BwGAAgstbOzo7qlqlXzaLNhIIlCi0Zy+9A2kqSP1ueqqMxlNgwAAA2kuNKtWWtqjuqq/V4H8/yu0C5cuFBjx45VRkaGHA6HZs+efdzbJ06cKIfDcdyvwYMHmwmLU+qTmaBerZqp2uPTWytzTMcBAKBBvLMqR5VurzqlxmpIu0TTcXCM3xXa8vJy9e7dW88999wp3+eyyy7TwYMH6359+umnTZgQZ8LhcOgnw9pKkv6zdK+qPVwHCACwN5/P0uvLapbS3TaEo7r8SajpAN83ZswYjRkz5rTvExERobS0tCZKhLN1ec90/eHTLcovcemzjQc1rk9L05EAADhrC7YXaF9RheIiQ3V1X76n+RO/K7RnYv78+UpJSVFCQoJGjBih3//+90pJSTnl+7tcLrlc367jLCkpkSS53W653e5GzxsMap/H7z6fDkk3DczUs//dqX8t2q0x3ZJ5NeunTjZ+sBfG0P4YQ//38uLdkqQf9WupcKd13Fgxfg2vPs+lw7IsqxGznBOHw6H3339f48ePr3ts5syZio2NVVZWlvbs2aPHH39cHo9Hq1evVkRExEn/nilTpuiJJ5444fEZM2YoOprdiY2pzC39ZnWIPJZDD/bwqG2c6UQAANTfwQrpj9+EyiFLj/f1KjHSdKLAV1FRoZtuuknFxcWKj48/7fvartB+38GDB5WVlaW33npLEyZMOOn7nGyGNjMzU4WFhT/4BOHMuN1uzZs3T6NGjVJYWNhxb/vl7E16Z/UBjemeqv+7obehhDid040f7IExtD/G0L/9avYmvb36gEZ3S9E/buxzwtsZv4ZXUlKipKSkMyq0tlxy8F3p6enKysrSjh07Tvk+ERERJ529DQsL45OugZ3sOb1jeDu9s/qA5mzOV36ZmzP7/BhfE/bHGNofY+h/ispcmv3NQUnSncPbn3Z8GL+GU5/n0e9OOaivoqIi5eTkKD093XQUnEKXtHgN65AonyW9vpSLFgAA9jJjebaqPT71bNlMA9o0Nx0HJ+F3hbasrEzr1q3TunXrJEl79uzRunXrlJ2drbKyMj300ENaunSp9u7dq/nz52vs2LFKSkrS1VdfbTY4Tqv2CK83V2Sr3OUxnAYAgDPj8nj1n2NHdd1xfls2N/spvyu0q1atUt++fdW3b19J0uTJk9W3b1/9+te/VkhIiDZs2KBx48apU6dOuv3229WpUyctXbpUcXHsNvJnF3ZOUZvEaJVUeTRrzX7TcQAAOCOfrD+oglKXUuMjdHlPfhrsr/xuDe3IkSN1un1qc+bMacI0aChOp0M/HtZWv/lwk179eq9uHpQlp5NXuQAA/2VZll5evEdSzUUK4aF+Nw+IYxgZNJkf9W+luMhQ7S4s14LtBabjAABwWsv3HNam3BJFhjl108DWpuPgNCi0aDIxEaG6YUCmJOmVr/cYTgMAwOm9cmx2dkK/VmoeE244DU6HQosmdduQNnI6pEU7CrU9v9R0HAAATmpfUbnmbcmXJP1kWBuzYfCDKLRoUpktonVp9zRJ0qvM0gIA/NS/l+yVZUkjOiWrQwobz/0dhRZN7ifn1xzhNWvNAR0urzacBgCA45VUufX2yhxJNUd1wf9RaNHkzstqrp4tm8nl8WnGci5aAAD4l7dX5qi82quOKbEa3jHJdBycAQotmpzD4dBPzm8jSfr3kn1yebxmAwEAcIzH69OrX++VVPMTRS5SsAcKLYy4sleG0uIjVVjm0gdrc03HAQBAkjRvc74OHK1Ui5hwXd23pek4OEMUWhgRFuKsm6V9adFu+XynvkwDAICmUnuRws2DWisyLMRwGpwpCi2MuWFga8VGhGrnoTIuWgAAGLd63xGt2ndE4SFO3To4y3Qc1AOFFsbER4bpxoE1Fy38c9Fuw2kAAMHunwtrvheN75uhlPhIw2lQHxRaGPXjYW0V6nRoya4ibTxQbDoOACBI7Sks15zNeZKku4a3M5wG9UWhhVEZCVG6sle6JGZpAQDm/GvRblmWdFGXFHVM5SIFu6HQwrg7j70S/nj9QR04Wmk4DQAg2BSVufTu6v2SpJ9ewOysHVFoYVyPls00tH2ivD5Lry7mOlwAQNP6z9J9cnl86tWqmQa1bWE6Ds4ChRZ+4a5jr4jfXJGt4kq34TQAgGBRWe3V68tqbq386QXtuEjBpii08AsjOyWrY0qsyqu9emtFtuk4AIAg8e6a/TpcXq1WzaN0Wfc003Fwlii08AsOh6NulvbVr/eq2uMznAgAEOi8PksvH9uQfOf5bRUaQi2yK0YOfmNcnwwlx0Uor6RKn2zgOlwAQOOatzlPe4sq1CwqTNeel2k6Ds4BhRZ+IyI0RBOHtpEkvbRwjyyL63ABAI3npWMXKdw6OEsxEaGG0+BcUGjhV24e1FrR4SHacrBEX+8sMh0HABCgVu09rDXZRxUe4tRtQ7nm1u4otPArCdHhuu7Yj31eXLjLcBoAQKB68djs7IR+LZUSxzW3dkehhd+54/y2CnE6tGhHIdfhAgAa3K6CMn2xJV/St5f7wN4otPA7mS2i667DfWEBs7QAgIb1r0V7ZFnSJV1T1CEl1nQcNAAKLfzSPSPaS5I+3XBQewvLDacBAASKQ6VVem9NzTW3dzE7GzAotPBLXdPjdWHnZPks6aVjZwQCAHCuXllcc9Z5v9YJGsg1twGDQgu/9bORHSRJ767ar0MlVYbTAADsrrjSrenHrrm9d2QHrrkNIBRa+K0BbZqrf1ZzVXt9euXrvabjAABsbvqyfSpzedQpNVYXdUkxHQcNiEILv+VwOPSzY2tp31i2TyVVbsOJAAB2VeX26pXFeyRJPxvZXk4ns7OBhEILv3ZRlxR1So1VqctT92MiAADq6+1VOSoqr1ar5lEa2yvDdBw0MAot/JrT6ag78eCVxXtV5fYaTgQAsBu316cXF9RsMP7pBe0UGkL9CTRnPaJXX321PvvsM1mW1ZB5gBOM7Z2hlglRKixz6d3V+03HAQDYzMfrc3XgaKUSY769jRKB5awL7QcffKArr7xSWVlZmjJlirKzsxsyF1AnLMSpu4a3lSS9tHC3PF6f4UQAALvw+SxNm19zSc9Pzm+ryLAQw4nQGM660G7atEkPPPCAqqqq9Nvf/lbt2rXTmDFjNGvWLHk8nobMCOj6Aa3VIiZc2Ycr9OnGPNNxAAA28eXWQ9qeX6bYiFDdMjjLdBw0krMutF27dtUzzzyjAwcOaObMmbrkkks0b948XXvttWrZsqUeeeQRbd++vSGzIohFhYdo4tA2kqRp83ex1AUA8IMsy9Lz83dKkm4ZnKVmUWGGE6GxnPOq6LCwMF177bX6/PPPtWfPHj3++OOKiorSn//8Z3Xt2lUjR47UG2+8IZfL1RB5EcRuG5Kl6PAQbTlYogXbC0zHAQD4ueV7DmtN9lGFhzr1k/PbmI6DRtSg2/wyMzP1+OOP6y9/+YvS09NlWZYWLlyo2267Ta1atdKf//xn+Xysf8TZSYgO100DW0uSnj+2HgoAgFOpXTt7bf9WSomLNJwGjanBCu3OnTv16KOPKjMzU9ddd52Kiop00003ac6cOZo6daqio6P1yCOP6JFHHmmoD4kgdMfwtgoLcWjFnsNaseew6TgAAD+18UCxFmwvkNMh3X1Be9Nx0MjOqdC6XC5Nnz5dI0eOVOfOnfXUU0+pWbNmevrpp3XgwAFNnz5do0aN0sMPP6zt27dr2LBh+s9//tNQ2RGE0ptF6Uf9a45cee6rnYbTAAD81bQFNbOzV/bKUOvEaMNp0NhCz/YP3n///XrjjTdUXFys8PBw3XDDDfrpT3+qESNGnPT9IyIidOmll+rrr78+67CAJN07sr3eXpWjhdsLtC7nqPpkJpiOBADwIzsPlenTDQcl1Vxzi8B31jO0//jHP5SSkqKnnnpK+/fv1xtvvHHKMltr5MiR+vWvf322HxKQJGW2iNb4Pi0lSc99ySwtAOB4z3+1U5YlXdI1VV3T403HQRM46xnaL7/8UiNHjqzXnxk2bJiGDRt2th8SqHPvhe01a+1+fbElX5tzS9Qtg3+wAADSvqJyffBNriTp/13cwXAaNJWznqGtb5kFGlL75Fhd2StDkvQP1tICAI6ZNn+XvD5LIzolq1erBNNx0EQa9NguoClNurBmXdSnGw9q56FSw2kAAKYdOFqp99bsl8TsbLCh0MK2uqTFa3S3VFmW9PxXnEsLAMHuhfm75PZaGto+Uf2zWpiOgyZEoYWt3X9RR0nSB9/kal9RueE0AABT8kuqNHNVjqRvvzcgeFBoYWs9WzXTyM7J8vqsuhthAADB58UFu1Xt8WlAm+Ya3I7Z2WBDoYXt3X9RzTqp99bs14GjlYbTAACaWmGZSzNW7JNUMzvrcDgMJ0JTo9DC9vpntdCQdolyey29uIBZWgAINv9ctFtVbp96ZyZoeMck03FgAIUWAeH+Y7tZ31qZo0MlVYbTAACaypHyar2+9Njs7IUdmJ0NUhRaBIQh7RLVP6u5qj0+vbRwt+k4AIAm8srXe1RR7VW39Hhd3DXFdBwYQqFFQHA4HLrv2Fra6cv3qbDMZTgRAKCxFVe69e+v90qq2U/B7GzwotAiYIzslKzemQmqcvtYSwsAQeC1JXtV6vKoU2qsLu2eZjoODKLQImA4HA49eEnN2YOvL9unQ6WspQWAQFVS5da/FtUsMZt0YQc5nczOBjMKLQLKyE7J6lM3S8taWgAIVK8u3quSKo86pMTqyl4ZpuPAMAotAorD4dDPR3WSJE1fto8TDwAgABVXuvWvxTWTFg9c3FEhzM4GPQotAs4FHZPUr3WCXB6fprGWFgACzsuL96i0qmbt7BU9003HgR+g0CLgfHeW9o3l2cpnlhYAAsbRimq9sniPJOnnl3Ri7SwkUWgRoM7vkKTzjp1LO20+s7QAECj+tWiPylwedUmL42QD1KHQIiB9d5Z2xops5RUzSwsAdne4vFqvfn1sdnYUs7P4FoUWAWto+0QNbNNC1R6fnp+/03QcAMA5emnhbpVXe9U9I16ju6WajgM/QqFFwHI4HHpwVM25tG+tyFHu0UrDiQAAZ6uwzKX/LN0rqWbtLLeC4bsotAhoQ9snaVDbFqr2spYWAOzspYW7VVHtVa9WzXRx1xTTceBnKLQIeLVraWeuZJYWAOyooJTZWZwehRYBb3C7RA1pl6hqr0/PfcVaWgCwmxcW7FKV26c+mQka2TnZdBz4IQotgsLk0TWztG+vzFF2UYXhNACAM3WopErTl+2TVPMTN2ZncTIUWgSFAW1aaESnZHl8lp79YrvpOACAM/T8/F1yeXzq1zpBF3RMMh0HfopCi6Dx0OjOkqT31x3Q9vxSw2kAAD8k53CF3lheMzv7P6M7MzuLU6LQImj0bNVMY3qkybKkZ+YySwsA/u5v/90ht9fSsA6JGtaB2VmcGoUWQWXyqE5yOqTPN+Xpm5yjpuMAAE5h56FSzVqzX9K3P2EDToVCi6DSMTVO4/u2lCT9ee42w2kAAKfyzLzt8lnS6G6p6tu6uek48HMUWgSdn1/SSWEhDi3aUailu4pMxwEAfM+G/cX6dEOeHI6atbPAD6HQIuhktojWDQNaS6qZpbUsy3AiAMB3PX3sJ2jj+7RU57Q4w2lgB35XaBcuXKixY8cqIyNDDodDs2fPPu7tlmVpypQpysjIUFRUlEaOHKlNmzaZCQvbuu+iDooIdWr1viOav63AdBwAwDHLdhdp4fYChTod+vklnUzHgU34XaEtLy9X79699dxzz5307U899ZSeeeYZPffcc1q5cqXS0tI0atQolZZyDBPOXGp8pCYObSNJenrONvl8zNICgGmWZenpOTWzszcMzFTrxGjDiWAXfldox4wZoyeffFITJkw44W2WZenZZ5/Vr371K02YMEE9evTQa6+9poqKCs2YMcNAWtjZPSPaKzYiVJsPlujTjQdNxwGAoPfVtkNave+IIsOcuv+ijqbjwEZCTQeojz179igvL0+jR4+ueywiIkIjRozQkiVLdPfdd5/0z7lcLrlcrrrfl5SUSJLcbrfcbnfjhg4Stc+jnZ7P2HCHfjIsS//35S79Zc42XdwpUaEhfvcar0nYcfxwPMbQ/oJ9DH0+S099XjM7e+ug1moRFWKr5yLYx68x1Oe5tFWhzcvLkySlpqYe93hqaqr27dt3yj83depUPfHEEyc8PnfuXEVH8+OMhjRv3jzTEeqlpUeKCQ3RnqIKPfGfORqSGtxLD+w2fjgRY2h/wTqGawod2poXosgQS22rdurTT3eajnRWgnX8GkNFRcUZv6+tCm2t7199Z1nWaa/De+yxxzR58uS635eUlCgzM1OjR49WfHx8o+UMJm63W/PmzdOoUaMUFhZmOk69HEncqz9+vl1fFUbrlzefr6jwENORmpydxw81GEP7C+YxdHt9+uvfl0iq0N0jOujaC9ubjlRvwTx+jaX2J+pnwlaFNi0tTVLNTG16enrd44cOHTph1va7IiIiFBERccLjYWFhfNI1MDs+pxOHtdPry3J04Gilpq/cr3tHdjAdyRg7jh+OxxjaXzCO4czV+7S3qEKJMeG6a0QHhYXZqp4cJxjHr7HU53m01YLBtm3bKi0t7bjp/Orqai1YsEBDhw41mAx2FhkWosmjao6GmTZ/l46UVxtOBADBo9zl0bNf7JAk3X9RB8VG2LfMwhy/K7RlZWVat26d1q1bJ6lmI9i6deuUnZ0th8OhBx98UH/4wx/0/vvva+PGjZo4caKio6N10003mQ0OWxvft6W6pMWptMqjf3xlz3VbAGBH/1y0W4VlLmUlRuumQVmm48Cm/K7Qrlq1Sn379lXfvn0lSZMnT1bfvn3161//WpL08MMP68EHH9S9996r8847TwcOHNDcuXMVF8dNIjh7IU6HHh3TRZL0n6X7lHP4zBeiAwDOTkGpSy8t3C1J+sWlnRUe6ne1BDbhd585I0eOlGVZJ/z697//LalmQ9iUKVN08OBBVVVVacGCBerRo4fZ0AgIIzola2j7RFV7fXpm3nbTcQAg4P3tv9tVUe1V71bNdEXP9B/+A8Ap+F2hBUxxOBx6bExXSdLsdQe0KbfYcCIACFy7C8r05oocSdJjl3c97WlFwA+h0ALf0bNVM43tnSHLkv742VbTcQAgYD09Z5u8PksXd0nR4HaJpuPA5ii0wPf8YnRnhYU4tGhHoRbvKDQdBwACzup9R/TZxjw5HdIjx/YvAOeCQgt8T+vEaN18bKft1M+2yOcL7tvDAKAhWZalP362RZJ0bf9MdUplUzfOHYUWOInasxA35Zboo/W5puMAQMCYtzlfK/ceUWSYUz8/dgY4cK4otMBJJMZG6J4R7STVrPNyebyGEwGA/Xm8Pv3p85r9CT8Z1lZpzSINJ0KgoNACp/CT89sqJS5C+49U6j9L9pmOAwC2987q/dpVUK7m0WG6Z2R703EQQCi0wClEh4fqoUs7S5L+78sdKipzGU4EAPZV5vLoL3Nrzvi+/6KOio8MM5wIgYRCC5zGNf1aqVt6vEqrPPrbf3eYjgMAtvX8VztVWOZS26QY3TKYK27RsCi0wGmEOB363ytrLlt4Y3m2duSXGk4EAPaTc7hC/1q8R5L02JguXHGLBsdnFPADhrZP0qhuqfL6LP3h0y2m4wCA7fzp862q9vg0pF2iRnVLNR0HAYhCC5yBX17eVaFOh77aVqCF2wtMxwEA21i977A+Xn9QDof0v1dyxS0aB4UWOANtk2J025A2kqQnP9ksj9dnNhAA2IDPZ+m3H9f8ZOu6/pnqntHMcCIEKgotcIYeuLijEqLDtD2/TDNX5ZiOAwB+76P1ufom56hiwkP0P5dyiQIaD4UWOEPNosP0wMUdJUnPzN2u0iq34UQA4L8qq73602c1lyjce2EHpcRxiQIaD4UWqIdbBmepXVKMisqr9Y+vdpmOAwB+61+Ldiu3uEotE6J0x/ltTcdBgKPQAvUQFuLULy+vOcbrlcV7lHO4wnAiAPA/+SVVmrag5kX/w5d1VmRYiOFECHQUWqCeLu6aomEdElXt9emPx36cBgD41p/nbFNFtVd9Wyfoqt4ZpuMgCFBogXpyOBz63yu6yemQPtlwUEt3FZmOBAB+Y+OBYr27Zr8k6fEru3FMF5oEhRY4C13T43XzoJqrG5/4aBPHeAGAao7p+vUHG2VZ0rg+GerXurnpSAgSFFrgLE0e1UkJ0WHamleqN5Znm44DAMbNXndAa7KPKjo8RI+N6Wo6DoIIhRY4S81jwvXQ6M6SpL/M3abD5dWGEwGAOaVVbk09tq/gvos6KK0Zx3Sh6VBogXNw48DW6poer5Iqj56es810HAAw5u9f7lRBqUttk2I4pgtNjkILnIMQp0NPXNVdkvTWymxtPFBsOBEANL2dh8r0yuI9kqRfX9lNEaEc04WmRaEFztHAti10Ve8MWZb0mw83ybIs05EAoMlYllWzOdZn6eIuKbqwS4rpSAhCFFqgAfzy8q6KDg/R6n1H9MG6XNNxAKDJzNucr0U7ChUe4tTjV3YzHQdBikILNIC0ZpGadGEHSdIfPt2iMpfHcCIAaHxVbq9+98lmSdKdw9uqTVKM4UQIVhRaoIHccX5bZSVG61CpS899udN0HABodC8t3K2cw5VKi//2RT1gAoUWaCCRYSF6/IqaH7e9vHi3dheUGU4EAI3nwNFKPT+/5sX7L6/oqpiIUMOJEMwotEADurhrikZ2Tpbba+nXH7BBDEDgevLjzapy+zSwbQuN7ZVuOg6CHIUWaEAOh0NTxnZXeKhTi3cW6uP1B01HAoAG99W2Q/psY17d0YUOh8N0JAQ5Ci3QwNokxWjSyJq1ZL/7eLNKq9yGEwFAw6lye/WbDzZJkn48tI26pscbTgRQaIFGcfeIdmqbFKNDpS49M2+76TgA0GD+8dVOZR+uUFp8pB4c1cl0HEAShRZoFJFhIfrtuJobxF5bspcbxAAEhF0FZXphwS5J0m/GdlMsG8HgJyi0QCMZ3jFZV/ZKl8+S/nf2Rvl8bBADYF+WZenx2Rvl9loa2TlZl/VIMx0JqEOhBRrR41fWzGCsyzmqt1bmmI4DAGftw29ytWRXkSJCnfrtVT3YCAa/QqEFGlFqfKQmH1tj9qfPt6qozGU4EQDUX3GlW7/7eIsk6b4LO6h1YrThRMDxKLRAI7ttSJa6pceruNKtqZ9tNR0HAOrtL3O3qbDMpXbJMfrpiHam4wAnoNACjSw0xKknr+4hh0N6d/V+rdhz2HQkADhj6/cf1evL9kmSnhzXQxGhIYYTASei0AJNoF/r5rphQGtJ0i/f3yCXx2s4EQD8MK/P0v/O3ijLksb1ydDQDkmmIwEnRaEFmsgjl3VWUmy4dh4q07T5u0zHAYAf9OrXe7R+f7HiIkP1qyu6mo4DnBKFFmgiCdHh+s3YmrNpn/9ql3YeKjWcCABOLedwhf4yt+ZimF9e3lUpcZGGEwGnRqEFmtCVvdJ1cZcUVXt9evS9DZxNC8AvWZalX83eqEq3V4PattD152WajgScFoUWaEIOh0O/G99DMeEhWrXviN5YkW06EgCc4IN1uVq4vUDhoU5NndBTTidnzsK/UWiBJpaREKVfXNpZkvSnz7Yqr7jKcCIA+Nbh8mr99uPNkqQHLu6odsmxhhMBP4xCCxhw65A26pOZoDKXR49/sFGWxdIDAP7hyY8363B5tbqkxemnF3DmLOyBQgsYEOJ06E/X9FKo06F5m/P1+cY805EAQAu2F2jW2gNyOKQ/XtNLYSHUBNgDn6mAIZ3T4vSzke0lSb/+cJOKK92GEwEIZhXVHv3q/Q2SpB8Pbas+mQlmAwH1QKEFDJp0YQe1S45RQalLf/xsi+k4AILYM3O3a/+RSrVMiNL/jO5kOg5QLxRawKDIsBBNvbqnJOnNFTn6emeh4UQAgtG6nKN65es9kqQnr+6hmIhQw4mA+qHQAoYNapeoWwdnSZIefne9ylwew4kABJMqt1f/8/Y6+SxpfJ8MXdg5xXQkoN4otIAfeHRMF7VqHqUDRytZegCgST37xQ7tKihXUmyEplzV3XQc4KxQaAE/EBMRqqeu6SVJmr4sW0tYegCgCazNPqKXFu6SJP3h6h5KiA43nAg4OxRawE8M7ZCkWwa3liT9gqUHABpZldurh975pm6pwejuaaYjAWeNQgv4kcfGdGXpAYAm8dcvtrPUAAGDQgv4EZYeAGgKa7OP6J8Ld0tiqQECA4UW8DMsPQDQmFhqgEBEoQX80KNjuqplAksPADQ8lhogEFFoAT8UGxGqp3/07dKDRTsKDCcCEAhW72OpAQIThRbwU0M7JNVduPDQO9/oaEW14UQA7Kzc5dHk71ygwFIDBBIKLeDHHru8i9olxSi/xKXHP9hkOg4AG3vyky3aV1Sh9GaRemJcD9NxgAZFoQX8WHR4qJ65vo9CnA599E2uPlh3wHQkADb0xeZ8vbkiW5L0l2t7q1lUmOFEQMOi0AJ+rk9mgu6/qIMk6fHZG5V7tNJwIgB2Uljm0qOz1kuS7jy/rYZ2SDKcCGh4FFrABu67sIN6ZyaopMpTc9yOzzIdCYANWJalx2ZtUGFZtTqlxuqhSzubjgQ0CgotYAOhIU799breigoL0ZJdRXp1yV7TkQDYwNurcjRvc77CQhx69vq+igwLMR0JaBQUWsAm2iXH6ldXdJUk/enzrdqeX2o4EQB/tq+oXE98tFmS9D+jO6tbRrzhREDjodACNnLzoNYa2TlZ1R6fHnxrnao9PtORAPghr8/S5Le/UUW1VwPbtNBdw9uZjgQ0KgotYCMOh0NPXdNLzaPDtPlgif48d5vpSAD80HNf7tTqfUcUGxGqv1zXWyFOh+lIQKOi0AI2kxIfqT9dU3OL2EsLd2v+tkOGEwHwJyv2HNbf/rtdkvTbcd2V2SLacCKg8VFoARsa3T1Ntw359haxQ6VVhhMB8AdHK6r14Ftr5bOkCX1bakK/VqYjAU2CQgvY1C8v76ouaXEqLKvW/7zNUV5AsLMsS4+8t165xVVqkxit347nNjAEDwotYFORYSF67qa+igxzatGOQv1z0W7TkQAY9MbybM3ZVHNE199v7KfYiFDTkYAmQ6EFbKxDSpymjO0uSXp6zjatyzlqNhAAI7bllep3H9cc0fXIZV3Us1Uzw4mApmW7QjtlyhQ5HI7jfqWlpZmOBRhz/YBMXdEzXR6fpf/35lqVVrlNRwLQhCqrvbr/zTVyeXwa2TlZPxnW1nQkoMnZrtBKUvfu3XXw4MG6Xxs2bDAdCTDG4XDoDxN6qmVClLIPV+h/Z2+UZbGeFggWv/tks7bnlyk5LkJ/vra3nBzRhSBky0IbGhqqtLS0ul/JycmmIwFGNYsK0//d2FchToc+WJert1flmI4EoAl8vD5XM5Zny+GQ/npdHyXFRpiOBBhhyxXjO3bsUEZGhiIiIjRo0CD94Q9/ULt2p74FxeVyyeVy1f2+pKREkuR2u+V28+PZhlD7PPJ8mtMrI1Y/v7iD/jxvh379wSZ1SY1Rt/Qzu+qS8bM/xtD+6juGuwvK9ci76yVJdw9vq0FtmjH+BvE12PDq81w6LJv9bPKzzz5TRUWFOnXqpPz8fD355JPaunWrNm3apMTExJP+mSlTpuiJJ5444fEZM2YoOpoDpxE4fJb0z61ObT7qVFKEpYd6eRVly5etAE6n2is9syFEBysd6hDv073dfAphpQECTEVFhW666SYVFxcrPv70EzS2K7TfV15ervbt2+vhhx/W5MmTT/o+J5uhzczMVGFh4Q8+QTgzbrdb8+bN06hRoxQWFmY6TlA7WuHW+GlLdeBolUZ1TdE/buwth+P03+kYP/tjDO3vTMfQsiw98v4mvb82V8mx4frg3iFKjmOpgWl8DTa8kpISJSUlnVGhtf3cTUxMjHr27KkdO3ac8n0iIiIUEXHiF3tYWBifdA2M59S85GZhev7m/vrRC0s0b8sh/Wf5ft05/NRLcr6L8bM/xtD+fmgMZ67M1vtrc+V0SP93Yz9ltIhtwnT4IXwNNpz6PI+23BT2XS6XS1u2bFF6errpKIDf6J2ZoMev7CZJ+uNnW7V632HDiQA0hE25xXr8g02SpIcu7awh7U++1A4INrYrtA899JAWLFigPXv2aPny5frRj36kkpIS3X777aajAX7l1sFZGts7Qx6fpUlvrFVRmeuH/xAAv1VS5da9b6xRtceni7uk6J4L2puOBPgN2xXa/fv368Ybb1Tnzp01YcIEhYeHa9myZcrKyjIdDfArDodDUyf0VLvkGOWVVOnBmevk9dl6yTwQtCzL0sPvrNe+ogq1TIjSX67jvFngu2y3hvatt94yHQGwjdiIUE27ub/G/WOxFu0o1DPztukXl3YxHQtAPU1bsEufb8pTWIhDz9/cTwnR4aYjAX7FdjO0AOqnc1qc/nRNL0nSP77apU83HDScCEB9zN92SE/P2SZJmnJVd/XOTDAbCPBDFFogCIzr01J3nl9zv/tD73yjbXmlhhMBOBN7C8v1/95cK8uSbhyYqZsHsbwOOBkKLRAkHh3TRcM6JKqi2qufvr5KxRXcZgP4s3KXRz99fZVKqjzq1zpBU67qbjoS4LcotECQCA1x6rkb+6lV8yjtK6rQ/W+tZZMY4Kcsy9Iv3v1G2/PLlBwXoWm39FdEaIjpWIDfotACQaR5TLheuvU8RYY5tXB7gf48d5vpSABO4vn5u/TphppNYC/c0k+p8ZGmIwF+jUILBJluGfF66ke9JUnT5u/Sx+tzDScC8F0LvvNi84mreqh/VgvDiQD/R6EFgtBVvTN09wU11+H+4p312pRbYjgRAEnKq5AefHvDsU1grXXToNamIwG2QKEFgtTDl3XRBZ2SVen26u431qq42nQiILgdLq/WS1tDVObyaGCbFppyVTfTkQDboNACQSrE6dBzN/VVx5RY5Ze49M+tIaqs9pqOBQQll8erSW+uU5HLoczmUXrhVjaBAfVBoQWCWHxkmF6+fYCaR4cpp9yhX7y3QT5OPgCalGVZ+uWsjVq176iiQiy9dEtftYjhJjCgPii0QJBrnRit52/qoxCHpTmbD3HyAdDEXliwW++t2a8Qp0MTO/nUISXWdCTAdii0AHReVnPd2N4nqea4oHdX7zecCAgOn2/M01NztkqSHr+8s7ok8BMS4GxQaAFIkgYkW/rZBTXX4z42a72W7y4ynAgIbBsPFOvnM9fJsqTbh2TpZk40AM4ahRZAnQcv7qAxPdLk9lq6e/pq7SooMx0JCEg5hys08dWVqnR7dUGnZD1+JScaAOeCQgugjtPp0DPX9VHvzAQdrXDr9ldW6FBJlelYQEA5Ul6t219docIyl7qmx+sfN/VVaAjfjoFzwVcQgONEhYfoldvPU5vEaO0/Uqkf/3ulylwe07GAgFDl9uqO11Zqd0G5WiZE6d8/HqC4yDDTsQDbo9ACOEFibIRe+8lAJcWGa1NuiX42fbXcXp/pWICteX2W/t+ba7Um+6jiI0P17x8PUGp8pOlYQECg0AI4qazEGL0ycYCiw0O0aEehHnlvvSyLHdjA2bAsS098tElzN+crPNSpf90+QB1T40zHAgIGhRbAKfVqlaB/3NxPIU6HZq05wBm1wFl6YcFu/WfpPjkc0rPX99HAti1MRwICCoUWwGld2DlFU6/uKUn6x1e79NqSvWYDATbz9soc/enzmrNm//eKbrq8Z7rhREDgodAC+EHXDcjU5FGdJEm/+XCT3uPiBeCMfLrhoB6dtV6SdPcF7XTH+W0NJwICE4UWwBm5/6IO+vGwNpKkh99br8835pkNBPi5hdsL9MBba+WzpBsGZOrRMV1MRwICFoUWwBlxOBx6/Ipu+lH/VnW7tRfvKDQdC/BLq/cd1t2vr5bba+mKXun6/dU95XA4TMcCAhaFFsAZczod+uOEnhrTI03VXp/u+s8qrd53xHQswK9sOViiHx+7BWxEp2T99bo+CnFSZoHGRKEFUC+hIU49e0MfDe+YpEq3Vz9+dYU255aYjgX4hT2F5br15RUqqfJoQJvmeuGW/goP5Vst0Nj4KgNQbxGhIXrx1v7qn9VcJVUe3fbKcu08VGo6FmDUvqJy3fTPZSosc6lberz+dfsARYWHmI4FBAUKLYCzEh0eqlcmDlC39HgVllXrhpeWa+ehMtOxACOyiyp040vLdLC4Sh1SYvWfOwaqWRRX2gJNhUIL4Kw1iwrT9DsHqUtanArLXLrxn8u0q4BSi+CSc7hCN/5zmXKLq9Q+OUYz7hqkpNgI07GAoEKhBXBOWsSEa8Zdg9UlLU4FpS7d+NIy7abUIkjsP1JTZg8crVS7pBi9eddgpcRFmo4FBB0KLYBz1iImXG/cOUidU+N0qLRmpnZPYbnpWECjyj1aqRv/uUz7j1SqTWK0Ztw1WCnxlFnABAotgAaRGBuhN+4apE6pscovqZmp3UupRYCqnZnNOVyprMRovfnTwUprRpkFTKHQAmgwSbERmnHXYHVMiVVeSZWue3Gptudz+gECy57Ccl33wlLtK6pQZosovXnXYKU3izIdCwhqFFoADaq21HZJq1l+cP2LS7Vhf7HpWECD2J5fquteXKrc4iq1S47R23cPUUYCZRYwjUILoMElx0XorZ8OVu/MBB2pcOumfy7Tyr2HTccCzsnGA8W6/sWlKih1qUtanGb+dAgzs4CfoNACaBQJ0TUbxQa2baFSl0e3vrxci3YUmI4FnJXV+w7rxpeW6UiFW71bNdNbPx2s5DiO5gL8BYUWQKOJjQjVaz8eqBGdklXl9umOf6/SnE15pmMB9bJoR4FufXmFSl0eDWzTQtPvHKSE6HDTsQB8B4UWQKOKCg/RS7f115geaar2+nTvG2v01ops07GAMzJ77QH9+NWVqqj2anjHJL32k4GKi+QGMMDfUGgBNLqI0BD9/ca+urZ/K3l9lh6dtUF/+2KHLMsyHQ04pX8u3K0HZ66Tx2fpqt4Zevn2AYoKDzEdC8BJUGgBNInQEKee+lEv3XdhB0nSX7/Yrl++v0Eer89wMuB4Pp+l3328Wb//dIsk6c7z2+rZ6/soPJRvmYC/CjUdAEDwcDgceujSzkptFqnffLBRb67IUUGpS3+/sR8zX/ALLo9Xv3hnvT78JleS9KvLu+quC9oZTgXgh/ByE0CTu3Vwlqbd0l8RoU59seWQbvznMhWWuUzHQpA7WlGt219ZoQ+/yVWo06Fnr+9DmQVsgkILwIhLu6dpxl2DlBAdpnU5RzX+H19rWx63isGM3QVluvr5JVq2+7BiI0L1ysQBGt+3pelYAM4QhRaAMf2zWui9nw1VVmK09h+p1ITnv9aXW/NNx0KQWbKrUFc/v0R7CsvVMiFK7/1sqC7olGw6FoB6oNACMKp9cqxm3ztMg9u1UHm1V3e8tkr/WrSbExDQJN5aka3bXl6h4kq3+rZO0OxJw9Q5Lc50LAD1RKEFYFzzmHC9fscg3TgwU5YlPfnJFj363gZVezgBAY3D4/Xpdx9v1qOzNtQdy/XmXdz+BdgVhRaAXwgLceoPV/fUr6/sJqdDmrkqRzf9c5nyS6pMR0OAKSxz6ZaXl+vlxXskST+/pJP+dkMfRYZx0gZgVxRaAH7D4XDoJ+e31csTByguIlSr9h3RFf+3WMt3F5mOhgCxNvuIxv59sZbtPqyY8BC9cEs/PXBJRzkcDtPRAJwDCi0Av3Nh5xR9eP/56pIWp8Iyl27613LW1eKcvbkiW9e/uEwHi6vULjlGH9w3TJf1SDcdC0ADoNAC8Ettk2I0696hGt8nQ16fpSc/2aL7ZqxVmctjOhpsprLaq4ff/UaPzdqgaq9Pl3ZP1QeThqlDCpu/gEBBoQXgt6LDQ/XX6/vot+O6K9Tp0CcbDuqq5xZrU26x6WiwiW15pbrqucV6e9V+OR3Sw5d11gu39FdcZJjpaAAaEIUWgF9zOBy6bUgbzbx7sNLiI7W7oFxX/2OJXlm8hyUIOCXLsjRjebauem6xdhwqU3JchF6/Y5DuHdmB9bJAAKLQArCF/lkt9OkDw3VJ11RVe3367cebdcdrq1TElbn4nuJKt+6bsVa/fH+DXB6fLuiUrM8eGK5hHZJMRwPQSCi0AGyjRUy4/nlbf/12XHeFhzr15dZDuuxvi7RoR4HpaPATy3YX6Yr/W6RPNhxUqNOhX17eRf+eOEBJsZwvCwSyUNMBAKA+apcgDGjTQv/vzbXacahMt768QrcMbq3HxnRVTAT/rAWjKrdXT8/Zple+3iPLkjJbROnvN/ZTn8wE09EANAFmaAHYUtf0eH143/m6dXCWJGn6smxd9reFWsaZtUFnXc5RXf5/i/Ty4poye+PATH32wAWUWSCIMJUBwLaiwkP0u/E9dGn3ND3y3nrlHK7UDS8t08ShbfTwZZ0VHc4/cYGsyu3Vc1/u1LQFu+T1WUqJi9CfrumlC7ukmI4GoIkxQwvA9s7vmKTPHxyuGwdmSpL+vWSvLnt2kRZuZ21toFqyq1CX/22Rnvtqp7w+S1f1ztDcn19AmQWCFNMXAAJCXGSYpk7opct6pOvR99Yr+3CFbntlhcb2ztDjV3ZVSlyk6YhoAEfKq/X7T7fo3dX7JUkpcRF64qruGtOTG7+AYMYMLYCAMqJTsub+/AL9eFgbOR3SR9/k6uK/LNDrS/fK6+PcWrvy+Sy9t3q/Ln5mgd5dvV8Oh3TL4Nb64n9GUGYBMEMLIPDERYbpN2O7a0LfVvrV7A1av79Yj3+wSe+s3q9fX9lN57VpYToi6mFdzlE98dEmrc0+KknqnBqnP0zoqf5Zzc0GA+A3KLQAAlbPVs30/r3DNH3ZPj09Z5vW7y/Wj15Yqit6pevRy7oos0W06Yg4jfySKv3p862ateaAJCk6PET3XdRBd57fTuGh/IARwLcotAACWojToduHttGYnml6Zu52zVyVo0/WH9S8zfm64/y2undke8VFhpmOie8od3n0yuI9mrZglyqqvZKka/q10sOXdVZqPGuhAZyIQgsgKKTEReqP1/TSbUPa6MlPNmvJriJNm79LM1fm6Gcj2uuWwVmKCg8xHTOouTxevbEsW//4aqeKyqslSf1aJ+g3Y7urN2fKAjgNCi2AoNItI15v3DlIX2w5pKmfbtHuwnL9/tMtemnRbt13YQfdMDBTEaEU26bk8fo0a+0B/e2LHTpwtFKS1CYxWpNHd9bYXulyOByGEwLwdxRaAEHH4XBoVLdUXdg5+bgi9ZsPN+nFBbv0sws76Nr+rRQZRrFtTC6PV++tPqAXF+7SvqIKSVJafKT+38Udde15rRQWwjpZAGeGQgsgaIWGOHXdeZka36elZq7K0XNf7lBucZUen71Rf/tiuyYObaNbB7dRs2jW2DakcpdHM5Zn61+Ldyu/xCVJah4dpp+NbK/bhrThhQSAeqPQAgh64aFO3To4S9f2b6W3VmTrn4v26MDRSv157nZNm79LNw5srduHtuFUhHO0/0iF3lierTdXZOtohVtSzYzsXRe0040DM7mqGMBZ418PADgmMixEE4e11c2Ds/TJ+oN6YcEubc0r1b8W79HLX+/RRZ1TdMuQLI3omCynk3WdZ8KyLC3dVaR/L9mrL7bkq/Zui7ZJMbpnRDuN79uSNcsAzhmFFgC+JyzEqfF9W2pcnwzN316gfy3ara93Fum/Ww/pv1sPqXWLaN00qLUm9G2pFI6ROqlDpVWavfaA3lm1XzsOldU9PrR9om4b0kajuqUqhBcFABoIhRYATsHhcOjCzim6sHOKdhWU6Y1l2XpndY6yD1foj59t1VOfb9WwDkma0K+lLu2eFvQ/Mnd5vPrvlkN6d/V+LdheUHfVcHR4iCb0a6nbh7RRx9Q4wykBBKLg/tcXAM5Q++RY/XpsN/3i0s768JsDenvVfq3ed0SLdhRq0Y5CRYdv1Ohuqbq0e5ou6JSsmIjg+Oe1stqrBdsL9PnGg/rvlkMqdXnq3tavdYKu6d9KY3tnKJ7LKwA0ouD4FxcAGkhUeIiuH9Ba1w9orX1F5Xp/7QG9v/aA9hVVaPa6XM1el6vwUKeGd0jS6O6purBLilLiAmtZwqGSKi3aUagvtuRr/rYCVbq9dW9Lbxapq/u21DX9W6l9cqzBlACCCYUWAM5SVmKMHrykkx64uKPW5hzV5xvzNGdTnvYVVdStt5WkTqmxGto+Sed3SNKgdi1sd9VumcujNfuOaNGOAi3aUaiteaXHvb1lQpQu65GmMT3S1K91czbMAWhyti20zz//vJ5++mkdPHhQ3bt317PPPqvhw4ebjgUgCDkcDvVr3Vz9WjfXY2O6aHt+meZuytO8LfnacKBY2/PLtD2/TP9esldOh9QpNU59WyeoT2aC+mQ2V4eUWL/ZIOXx+rS3qELr9x/V6n1HtCb7qLblldSdTiBJDofUs2UzDe+YpEu7p6lny2bc5gXAKFsW2pkzZ+rBBx/U888/r2HDhunFF1/UmDFjtHnzZrVu3dp0PABBzOFwqHNanDqnxen+izvqaEW1lu4q0uKdhVqyq0h7Csu1Na9UW/NK9eaKHElSZJhT7ZNj1TElVh1T49QxJVatE6OVkRDVaGtPS6vcyjlcqf1HKrSvqOJYphLtOFSmao/vhPdv1TxKQ9olaninZJ3fIUktYsIbJRcAnA1bFtpnnnlGd9xxh+68805J0rPPPqs5c+Zo2rRpmjp1quF0APCthOhwjemZrjE90yVJ+SVVWpt9VOtyjmpdzhGt31+simqvNuWWaFNuyQl/Pi4iVBkJUUptFqmEqDAlRIepWVTNr6jwEIU5nQoNcchh+bS+yCHPNwfltRxyeX1yub0qd3l1pKJah8urdaSiWkVl1cotrqy72OBkosJC1DU9Tv2zmqt/Vs3MM8eTAfBntiu01dXVWr16tR599NHjHh89erSWLFly0j/jcrnkcrnqfl9SUvNNw+12y+0+9T/qOHO1zyPPpz0xfk2nRVSILu6cqIs7J0qSvD5L+49UasehMu08VKadBeXaWVCm3KNVOlLhVqnLo235pdqWX/oDf7MkhUjbN5xxlubRYWrVPEqtEqLUMTVWnVNj1Tk1TpnNo05YB8vnRuPj69DeGL+GV5/n0naFtrCwUF6vV6mpqcc9npqaqry8vJP+malTp+qJJ5444fG5c+cqOpqrLBvSvHnzTEfAOWD8zGotqXW0dFGWpCzJ5ZWOVktHXA4VV0sVHqnC41ClRyr3SG6f5LUkn1X7X4dCnJZCHVKoUwpzSuFOKSZMig21FBsmxYRKCeGWWkRKkSEeSZU1H7xS8uyVNu2VNhl7BiDxdWh3jF/DqaioOOP3tV2hrfX9DQiWZZ1yU8Jjjz2myZMn1/2+pKREmZmZGj16tOLj4xs1Z7Bwu92aN2+eRo0apbAwe+3gBuMXCBhD+2MM7Y3xa3i1P1E/E7YrtElJSQoJCTlhNvbQoUMnzNrWioiIUERExAmPh4WF8UnXwHhO7Y3xsz/G0P4YQ3tj/BpOfZ5HZyPmaBTh4eHq37//CVP68+bN09ChQw2lAgAAgCm2m6GVpMmTJ+vWW2/VeeedpyFDhuill15Sdna27rnnHtPRAAAA0MRsWWivv/56FRUV6be//a0OHjyoHj166NNPP1VWVpbpaAAAAGhitiy0knTvvffq3nvvNR0DAAAAhtluDS0AAADwXRRaAAAA2BqFFgAAALZGoQUAAICtUWgBAABgaxRaAAAA2BqFFgAAALZGoQUAAICtUWgBAABgaxRaAAAA2BqFFgAAALZGoQUAAICtUWgBAABga6GmA5hgWZYkqaSkxHCSwOF2u1VRUaGSkhKFhYWZjoN6YvzsjzG0P8bQ3hi/hlfb02p72+kEZaEtLS2VJGVmZhpOAgAAgNMpLS1Vs2bNTvs+DutMam+A8fl8ys3NVVxcnBwOh+k4AaGkpESZmZnKyclRfHy86TioJ8bP/hhD+2MM7Y3xa3iWZam0tFQZGRlyOk+/SjYoZ2idTqdatWplOkZAio+P5wvZxhg/+2MM7Y8xtDfGr2H90MxsLTaFAQAAwNYotAAAALA1Ci0aREREhH7zm98oIiLCdBScBcbP/hhD+2MM7Y3xMysoN4UBAAAgcDBDCwAAAFuj0AIAAMDWKLQAAACwNQotAAAAbI1Ci0bjcrnUp08fORwOrVu3znQcnKG9e/fqjjvuUNu2bRUVFaX27dvrN7/5jaqrq01Hwyk8//zzatu2rSIjI9W/f38tWrTIdCScoalTp2rAgAGKi4tTSkqKxo8fr23btpmOhXMwdepUORwOPfjgg6ajBBUKLRrNww8/rIyMDNMxUE9bt26Vz+fTiy++qE2bNumvf/2rXnjhBf3yl780HQ0nMXPmTD344IP61a9+pbVr12r48OEaM2aMsrOzTUfDGViwYIEmTZqkZcuWad68efJ4PBo9erTKy8tNR8NZWLlypV566SX16tXLdJSgw7FdaBSfffaZJk+erPfee0/du3fX2rVr1adPH9OxcJaefvppTZs2Tbt37zYdBd8zaNAg9evXT9OmTat7rGvXrho/frymTp1qMBnORkFBgVJSUrRgwQJdcMEFpuOgHsrKytSvXz89//zzevLJJ9WnTx89++yzpmMFDWZo0eDy8/N111136fXXX1d0dLTpOGgAxcXFatGihekY+J7q6mqtXr1ao0ePPu7x0aNHa8mSJYZS4VwUFxdLEl9vNjRp0iRdccUVuuSSS0xHCUqhpgMgsFiWpYkTJ+qee+7Reeedp71795qOhHO0a9cu/f3vf9df/vIX01HwPYWFhfJ6vUpNTT3u8dTUVOXl5RlKhbNlWZYmT56s888/Xz169DAdB/Xw1ltvac2aNVq5cqXpKEGLGVqckSlTpsjhcJz216pVq/T3v/9dJSUleuyxx0xHxvec6Rh+V25uri677DJde+21uvPOOw0lxw9xOBzH/d6yrBMeg/+77777tH79er355pumo6AecnJy9MADD2j69OmKjIw0HSdosYYWZ6SwsFCFhYWnfZ82bdrohhtu0EcffXTcN1Ov16uQkBDdfPPNeu211xo7Kk7hTMew9h/k3NxcXXjhhRo0aJD+/e9/y+nk9a+/qa6uVnR0tN555x1dffXVdY8/8MADWrdunRYsWGAwHerj/vvv1+zZs7Vw4UK1bdvWdBzUw+zZs3X11VcrJCSk7jGv1yuHwyGn0ymXy3Xc29A4KLRoUNnZ2SopKan7fW5uri699FK9++67GjRokFq1amUwHc7UgQMHdOGFF6p///6aPn06/xj7sUGDBql///56/vnn6x7r1q2bxo0bx6YwG7AsS/fff7/ef/99zZ8/Xx07djQdCfVUWlqqffv2HffYj3/8Y3Xp0kWPPPIIy0eaCGto0aBat2593O9jY2MlSe3bt6fM2kRubq5Gjhyp1q1b689//rMKCgrq3paWlmYwGU5m8uTJuvXWW3XeeedpyJAheumll5Sdna177rnHdDScgUmTJmnGjBn64IMPFBcXV7f2uVmzZoqKijKcDmciLi7uhNIaExOjxMREymwTotACOM7cuXO1c+dO7dy584QXIfxAx/9cf/31Kioq0m9/+1sdPHhQPXr00KeffqqsrCzT0XAGao9bGzly5HGPv/rqq5o4cWLTBwJsiiUHAAAAsDV2eQAAAMDWKLQAAACwNQotAAAAbI1CCwAAAFuj0AIAAMDWKLQAAACwNQotAAAAbI1CCwAAAFuj0AIAAMDWKLQAAACwNQotAAAAbI1CCwA2ZFmWRo8eLYfDoffee++4t/l8Pl100UVyOByaPXu2mYAA0IQclmVZpkMAAOovLy9PvXr1ksfj0TfffKPMzExJ0h//+Ec99thjuvvuu/XCCy8YTgkAjY9CCwA29vHHH2vs2LEaPny45s+frzVr1mjo0KHq0KGDVq9eraioKNMRAaDRUWgBwObuv/9+Pffcc3rkkUc0a9Ys7du3T8uXL1efPn1MRwOAJkGhBQCbq6qq0sCBA7VhwwZJ0jPPPKOf//znhlMBQNNhUxgA2FxkZKTGjBkjSYqOjtZPfvITw4kAoGlRaAHA5pYuXapnnnlGiYmJqqio0KRJk0xHAoAmRaEFABsrKSnRzTffrLCwMC1cuFDjxo3TG2+8oenTp5uOBgBNhjW0AGBjN998s2bMmKFp06bpnnvuUVFRkXr16qXS0lKtW7dO7dq1Mx0RABodM7QAYFOvv/66ZsyYoauuukr33HOPJCkxMVGvvfaaysrKdPPNN8vj8RhOCQCNj0ILADa0e/duTZo0Senp6Xr55ZePe9sll1yi//mf/9GyZcs0ZcoUMwEBoAmx5AAAAAC2xgwtAAAAbI1CCwAAAFuj0AIAAMDWKLQAAACwNQotAAAAbI1CCwAAAFuj0AIAAMDWKLQAAACwNQotAAAAbI1CCwAAAFuj0AIAAMDWKLQAAACwNQotAAAAbO3/A4H1nsihvhk9AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# зададим размер графика в дюймах\n",
+    "# plt.figure(figsize=(8, 6))\n",
+    "\n",
+    "# зададим интервал, например, от -5 до 5 и сформируем на нем 5000 точек\n",
+    "# это будут наши координаты по оси x\n",
+    "# x = np.linspace(-5, 5, 5000)\n",
+    "\n",
+    "# по оси y отложим квадрат этих точек\n",
+    "# y = x**2\n",
+    "\n",
+    "# создадим сетку\n",
+    "# plt.grid()\n",
+    "\n",
+    "# выведем кривую и подписи на графике\n",
+    "# plt.plot(x, y)\n",
+    "# plt.xlabel(\"x\", fontsize=14)\n",
+    "# plt.ylabel(\"y\", fontsize=14)\n",
+    "\n",
+    "# результатом будет парабола\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.86013412, 0.07898952, 0.31171914],\n",
+       "       [0.69224994, 0.4676353 , 0.05405754],\n",
+       "       [0.16735234, 0.92704432, 0.02961371],\n",
+       "       [0.85617454, 0.71795665, 0.90539567]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Функции np.random.rand() и np.random.randint()\n",
+    "# np.random.rand(4, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[-2, -2],\n",
+       "        [-1, -2],\n",
+       "        [-2, -1]],\n",
+       "\n",
+       "       [[ 2,  0],\n",
+       "        [ 2, -1],\n",
+       "        [-2, -2]]])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# создадим массив размерностью 2 x 3 x 2 c числами [-3, 3)\n",
+    "# np.random.randint(-3, 3, size=(2, 3, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Функция np.fromfunction()\n",
+    "# берет координаты (i, j) каждой ячейки\n",
+    "# и передает их в собственную функцию.\n",
+    "# создадим собственную функцию,\n",
+    "# которая принимает два числа\n",
+    "# и возводит первое число в степень второго\n",
+    "\n",
+    "\n",
+    "# def power(i, j) -> int:\n",
+    "#     return i**j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0.],\n",
+       "       [1., 1., 1.],\n",
+       "       [1., 2., 4.]])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.fromfunction(power, (3, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 0, 0, 1, 0, 0, 0],\n",
+       "       [0, 0, 3, 0, 0, 2, 0],\n",
+       "       [0, 0, 0, 1, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Матрица csr и метод .toarray()\n",
+    "# создадим матрицу с преобладанием нулевых значений\n",
+    "# A = np.array([[2, 0, 0, 1, 0, 0, 0],\n",
+    "# [0, 0, 3, 0, 0, 2, 0], [0, 0, 0, 1, 0, 0, 0]])\n",
+    "# A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7619047619047619"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1.0 - np.count_nonzero(A) / A.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (0, 0)\t2\n",
+      "  (0, 3)\t1\n",
+      "  (1, 2)\t3\n",
+      "  (1, 5)\t2\n",
+      "  (2, 3)\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# и применим ее к матрице А\n",
+    "# B = csr_matrix(A)\n",
+    "# print(B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 0, 0, 1, 0, 0, 0],\n",
+       "       [0, 0, 3, 0, 0, 2, 0],\n",
+       "       [0, 0, 0, 1, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# C = B.toarray()\n",
+    "# C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Индекс элемента массива\n",
+    "# a = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "# a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# первый элемент представляет собой вектор\n",
+    "# a[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 6)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# a[0][0], a[1][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5, 6, 7, 8])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Срез массива\n",
+    "# b = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
+    "# b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 4, 6])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# b[1:6:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 4],\n",
+       "       [5, 6, 7, 8]])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# c = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
+    "# c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 0 указывает на первую строку, диапазон :2 - на первые два столбца\n",
+    "# c[0, :2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 6])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# c[:, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # выведем элемент в первой строке и первом столбце\n",
+    "# c[0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # выведем элемент в последней строке и последнем столбце\n",
+    "# c[-1, -1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 7])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# c[1, ::2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1],\n",
+       "        [ 2,  3]],\n",
+       "\n",
+       "       [[ 4,  5],\n",
+       "        [ 6,  7]],\n",
+       "\n",
+       "       [[ 8,  9],\n",
+       "        [10, 11]],\n",
+       "\n",
+       "       [[12, 13],\n",
+       "        [14, 15]]])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# d = np.arange(16).reshape(4, 2, -1)\n",
+    "# d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# d[2][1][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10, 11],\n",
+       "       [14, 15]])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # выведем третью и четвертую матрицу [2:]\n",
+    "# # и в них вторую строку [1] и все столбцы [:]\n",
+    "# d[2:, 1, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2],\n",
+       "       [3, 4]])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Оси массива\n",
+    "\n",
+    "# arr_2D = np.array([[1, 2], [3, 4]])\n",
+    "# arr_2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4, 6])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.sum(arr_2D, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 7])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.sum(arr_2D, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 10)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.sum(arr_2D, axis=(0, 1)), np.sum(arr_2D)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([3, 7]), array([4, 6]))"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Отрицательные значения в параметре axis\n",
+    "# np.sum(arr_2D, axis=-1), np.sum(arr_2D, axis=-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6,  8, 10],\n",
+       "       [12, 14, 16]])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Массив 3D\n",
+    "# np.sum(arr_3D, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [3, 4, 5]])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # возьмем первую матрицу\n",
+    "# arr_3D[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6,  7,  8],\n",
+       "       [ 9, 10, 11]])"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # возьмем вторую матрицу\n",
+    "# arr_3D[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6,  8, 10],\n",
+       "       [12, 14, 16]])"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # и поэлементно сложим их\n",
+    "# arr_3D[0] + arr_3D[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# total = np.zeros((2, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2., 2., 2.],\n",
+       "       [2., 2., 2.]])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# for i in range(2):\n",
+    "#     total += i\n",
+    "# total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3,  5,  7],\n",
+       "       [15, 17, 19]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Сложение вдоль второй оси (axis = 1)\n",
+    "# np.sum(arr_3D, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 5, 7])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # сложим столбцы первой\n",
+    "# arr_3D[0][0] + arr_3D[0][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([15, 17, 19])"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # и второй матрицы\n",
+    "# arr_3D[1][0] + arr_3D[1][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# total1 = np.zeros((2, 3))\n",
+    "# for i in range(2):\n",
+    "#     for j in range(2):\n",
+    "#         total1[i] += arr_3D[i][j]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3, 12],\n",
+       "       [21, 30]])"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Сложение вдоль третьей оси (axis = 2)\n",
+    "# # применим np.sum()\n",
+    "# np.sum(arr_3D, axis=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# arr_3D[0][0][0] + arr_3D[0][0][1] + arr_3D[0][0][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3., 12.],\n",
+       "       [21., 30.]])"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим нулевой массив 2 x 2 для записи результатов\n",
+    "# total = np.zeros((2, 2))\n",
+    "\n",
+    "# # пройдемся по матрицам\n",
+    "# for i in range(2):\n",
+    "\n",
+    "#     # по строкам матрицы\n",
+    "#     for j in range(2):\n",
+    "\n",
+    "#         # и по столбцам\n",
+    "#         for k in arr_3D[i][j]:\n",
+    "\n",
+    "#             # индексы i, j запишут результат сложения элементов строк k\n",
+    "#             # в квадратную матрицу 2 x 2\n",
+    "#             total[i][j] += k\n",
+    "\n",
+    "# total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([18, 22, 26])"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Сложение вдоль первой и второй осей (axis = (0, 1))\n",
+    "# # применим функцию np.sum()\n",
+    "# np.sum(arr_3D, axis=(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6.,  8., 10.],\n",
+       "       [12., 14., 16.]])"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # произведем сложение по оси 0\n",
+    "# total_0 = np.zeros((2, 3))\n",
+    "\n",
+    "# for i in range(2):\n",
+    "#     total_0 += arr_3D[i]\n",
+    "\n",
+    "# total_0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([18., 22., 26.])"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # произведем сложение по оси 1\n",
+    "# total_1 = np.zeros(3)\n",
+    "\n",
+    "# for j in range(2):\n",
+    "#     total_1 += total_0[j]\n",
+    "\n",
+    "# total_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([18., 22., 26.])"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# total = np.zeros(3)\n",
+    "\n",
+    "# for i in range(2):\n",
+    "#     for j in range(2):\n",
+    "#         total += arr_3D[i][j]\n",
+    "\n",
+    "# total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Сложение вдоль всех трех осей (axis = (0, 1, 2))\n",
+    "# np.sum(arr_3D, axis=(0, 1, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# total = 0\n",
+    "# # в трех вложенных циклах мы пройдемся по всем элементам массива\n",
+    "# for i in range(2):\n",
+    "#     for j in range(2):\n",
+    "#         for k in range(3):\n",
+    "\n",
+    "#             # и запишем сумму этих элементов в переменную total\n",
+    "#             total += arr_3D[i][j][k]\n",
+    "\n",
+    "# total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Операции с массивами\n",
+    "# arr_3D = np.arange(12).reshape(2, 2, 3)\n",
+    "# len(arr_3D), len(arr_3D[0][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Распаковка массива\n",
+    "# a = np.arange(1, 28).reshape(3, 9)\n",
+    "# x, y, z = a\n",
+    "# x, *y, z = a[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "[2, 3, 4, 5, 6, 7, 8]\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(x)\n",
+    "# print(y)\n",
+    "# print(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Изменение элементов массива\n",
+    "\n",
+    "# arr_2D = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "# arr_2D[0, 0] = 2\n",
+    "# arr_2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1, 1],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Запишем значение 1 в первую строку.\n",
+    "# arr_2D[0] = 1\n",
+    "# arr_2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1, 0],\n",
+       "       [4, 5, 0]])"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Пусть третий столбец массива состоит из нулей.\n",
+    "# arr_2D[:, 2] = 0\n",
+    "# arr_2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2],\n",
+       "        [ 3,  4,  5]],\n",
+       "\n",
+       "       [[ 6,  0,  8],\n",
+       "        [ 9,  1, 11]]])"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Теперь потренируемся с трехмерным массивом.\n",
+    "# arr_3D = np.arange(12).reshape(2, 2, 3)\n",
+    "# # Выберем второй столбец второй матрицы и заменим значения столбца 7 и 10 на 0 и 1.\n",
+    "# # при такой операции размер среза должен совпадать\n",
+    "# # с количеством передаваемых значений\n",
+    "# arr_3D[1, :, 1] = [0, 1]\n",
+    "# arr_3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[7, 7, 7],\n",
+       "        [7, 7, 7]],\n",
+       "\n",
+       "       [[7, 7, 7],\n",
+       "        [7, 7, 7]]])"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# arr_3D.fill(7)\n",
+    "# arr_3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 4, 8],\n",
+       "       [1, 2, 3]])"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Сортировка массива и обратный порядок его элементов\n",
+    "# a = np.array([[4, 8, 2], [2, 3, 1]])\n",
+    "# np.sort(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 4, 8],\n",
+       "       [1, 2, 3]])"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.sort(a, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3, 1],\n",
+       "       [4, 8, 2]])"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.sort(a, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 2, 3, 4, 8])"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.sort(a, axis=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Обратный порядок элементов массива\n",
+    "# np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])[::-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7, 6, 5, 4])"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # обратите внимание, мы используем и положительный, и отрицательный индексы\n",
+    "# np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])[-3:3:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7, 6, 5, 4, 3, 2, 1])"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Сортировка в убывающем порядке\n",
+    "\n",
+    "# # возьмем простой одномерный массив\n",
+    "# a = np.array([4, 2, 6, 1, 7, 3, 5])\n",
+    "\n",
+    "# np.sort(a)[::-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # здесь нужно сначала задать обратный порядок, а потом отсортировать\n",
+    "# a[::-1].sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2],\n",
+       "        [ 3,  4,  5]],\n",
+       "\n",
+       "       [[ 6,  7,  8],\n",
+       "        [ 9, 10, 11]]])"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Изменение размерности\n",
+    "# arr_3D = np.arange(12).reshape(2, 2, 3)\n",
+    "# arr_3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # в нем 12 элементов\n",
+    "# arr_3D.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5],\n",
+       "       [ 6,  7,  8,  9, 10, 11]])"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # при этом важно, чтобы общее количество элементов было тем же\n",
+    "# arr_2D = arr_3D.reshape(2, 6)\n",
+    "# arr_2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5],\n",
+       "       [ 6,  7,  8,  9, 10, 11],\n",
+       "       [ 0,  1,  2,  3,  4,  5]])"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Функция np.resize() и метод .resize()\n",
+    "# # функция np.resize() позволяет не сохранять прежнее количество элементов\n",
+    "# # существующие элементы копируются в новые ячейки\n",
+    "# np.resize(arr_2D, (3, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# arr_2D_copy = arr_2D.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Методы .flatten() и .ravel()\n",
+    "# arr_3D.flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# arr_3D.ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # np.newaxis\n",
+    "\n",
+    "# # создадим одномерный массив\n",
+    "# a = np.array([1, 2, 3])\n",
+    "# a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]]\n",
+      "(1, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# b = a[np.newaxis, :]\n",
+    "\n",
+    "# print(b)\n",
+    "# print(b.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Функция np.concatenate()\n",
+    "# a = np.arange(4).reshape(2, 2)\n",
+    "# b = np.arange(4, 8).reshape(2, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1],\n",
+       "       [2, 3],\n",
+       "       [4, 5],\n",
+       "       [6, 7]])"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# np.concatenate((a, b), axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0, 1],\n",
+       "        [2, 3]],\n",
+       "\n",
+       "       [[4, 5],\n",
+       "        [6, 7]]])"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Функция np.stack()\n",
+    "# # при axis = 0 мы просто добавляем внешнее измерение\n",
+    "# np.stack((a, b), axis=0)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/chapter_1_numpy.py b/python/makarov/chapter_1_numpy.py
new file mode 100644
index 00000000..97ed49a9
--- /dev/null
+++ b/python/makarov/chapter_1_numpy.py
@@ -0,0 +1,556 @@
+"""Numpy."""
+
+# +
+# Массив Numpy — это многомерный
+# массив (ndarray, n-dimensional array) данных,
+# над которыми можно быстро и эффективно выполнять
+# множество математических, статистических,
+# логических и других операций.
+
+# импортируем библиотеку matplotlib
+# import matplotlib.pyplot as plt
+# import numpy as np
+
+# импортируем функцию csr_matrix()
+# from scipy.sparse import csr_matrix
+
+# создадим массив из списка
+# arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+# arr
+
+# +
+# или кортежа
+# arr = np.array((0, 1, 2, 3, 4, 5, 6, 7, 8, 9))
+# arr
+
+# +
+# создадим список с помощью функций range() и list()
+# list(range(2, 5.5, 0.5))
+
+# +
+# np.arange(2, 5.5, 0.5)
+
+# +
+# создадим массив с элементами типа float
+# arr_f = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], float)
+
+# print(arr_f)
+
+# тип данных можно посмотреть через атрибут dtype
+# print(arr_f.dtype)
+
+# +
+# arr.ndim
+
+# +
+# arr.shape
+
+# +
+# пока что у нас одно измерение, в котором четыре элемента
+# arr.size
+
+# +
+# в нашем случае - это целое число длиной 64 бита
+# arr.dtype
+
+# +
+# arr.itemsize
+
+# +
+# # у нас четыре элемента по восемь байтов или 32 байта
+# arr.nbytes
+
+# +
+# Массив с нулевой размерностью — это число (скаляр) и квадратных скобок не имеет.
+
+# arr_0D = np.array(42)
+# arr_0D
+
+# +
+# print(arr_0D.ndim)
+# print(arr_0D.shape)
+# print(arr_0D.size)
+
+# +
+# Одномерный массив (вектор)
+# arr_1D = np.array([1, 2, 3])
+# arr_1D
+
+# +
+# print(arr_1D.ndim)
+# print(arr_1D.shape)
+# print(arr_1D.size)
+
+# +
+# Двумерный массив (матрица)
+# с точки зрения синтаксиса - это просто вложенные списки
+# arr_2D = np.array([[1, 2, 3], [4, 5, 6]])
+# arr_2D
+
+# +
+# print(arr_2D.ndim)
+# print(arr_2D.shape)
+# print(arr_2D.size)
+
+# +
+# column = np.array([[1], [2], [3]])
+# column
+
+# +
+# посмотрим на размерность
+# column.shape
+
+# +
+# row = np.array([[1, 2, 3]])
+# row
+
+# +
+# размерность будет иной
+# row.shape
+
+# +
+# Трехмерный массив
+# arr_3D = np.arange(12).reshape(2, 2, 3)
+# arr_3D
+
+# +
+# print(arr_3D.ndim)
+# print(arr_3D.shape)
+# print(arr_3D.size)
+
+# +
+# Массив из нулей
+# ей мы можем передать одно значение для создания одномерного массива
+# np.zeros(5)
+
+# +
+# или кортеж из чисел для указания количества нулей в каждом измерении
+# np.zeros((2, 3))
+
+# +
+# Массив из единиц
+# создадим трехмерный массив
+# np.ones((2, 2, 3))
+
+# +
+# Массив, заполненный заданным значением
+# np.full((2, 3), 4)
+
+# +
+# Пустой массив Numpy
+# создадим пустую матрицу 3 х 2
+# np.empty((3, 2))
+
+# +
+# любой массив Numpy можно преобразовать
+# в описанные выше массивы с помощью функций
+# np.zeros_like(), np.ones_like(),
+# np.full_like() и np.empty_like().
+# Приведу пример для np.zeros_like().
+# создадим массив 2 x 3 с числами от 1 до 6
+# arr1 = np.arange(1, 7).reshape(2, 3)
+# arr1
+
+# +
+# и превратим его в массив с нулями
+# np.zeros_like(arr1)
+
+# +
+# Функция np.linspace()
+# создадим диапазон от 0 до 0,9 и
+# разделим его на десять точек, включая 0 и 0,9
+# np.linspace(0, 0.9, 10)
+
+# +
+# с функцией np.arange мы точно знаем, где будут расположены точки
+# np.arange(0, 1, 0.1)
+
+# +
+# зададим размер графика в дюймах
+# plt.figure(figsize=(8, 6))
+
+# зададим интервал, например, от -5 до 5 и сформируем на нем 5000 точек
+# это будут наши координаты по оси x
+# x = np.linspace(-5, 5, 5000)
+
+# по оси y отложим квадрат этих точек
+# y = x**2
+
+# создадим сетку
+# plt.grid()
+
+# выведем кривую и подписи на графике
+# plt.plot(x, y)
+# plt.xlabel("x", fontsize=14)
+# plt.ylabel("y", fontsize=14)
+
+# результатом будет парабола
+# plt.show()
+
+# +
+# Функции np.random.rand() и np.random.randint()
+# np.random.rand(4, 3)
+
+# +
+# создадим массив размерностью 2 x 3 x 2 c числами [-3, 3)
+# np.random.randint(-3, 3, size=(2, 3, 2))
+
+# +
+# Функция np.fromfunction()
+# берет координаты (i, j) каждой ячейки
+# и передает их в собственную функцию.
+# создадим собственную функцию,
+# которая принимает два числа
+# и возводит первое число в степень второго
+
+
+# def power(i, j) -> int:
+#     return i**j
+
+# +
+# np.fromfunction(power, (3, 3))
+
+# +
+# Матрица csr и метод .toarray()
+# создадим матрицу с преобладанием нулевых значений
+# A = np.array([[2, 0, 0, 1, 0, 0, 0],
+# [0, 0, 3, 0, 0, 2, 0], [0, 0, 0, 1, 0, 0, 0]])
+# A
+
+# +
+# 1.0 - np.count_nonzero(A) / A.size
+
+# +
+# и применим ее к матрице А
+# B = csr_matrix(A)
+# print(B)
+
+# +
+# C = B.toarray()
+# C
+
+# +
+# Индекс элемента массива
+# a = np.array([[1, 2, 3], [4, 5, 6]])
+# a
+
+# +
+# первый элемент представляет собой вектор
+# a[0]
+
+# +
+# a[0][0], a[1][2]
+
+# +
+# Срез массива
+# b = np.array([1, 2, 3, 4, 5, 6, 7, 8])
+# b
+
+# +
+# b[1:6:2]
+
+# +
+# c = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
+# c
+
+# +
+# 0 указывает на первую строку, диапазон :2 - на первые два столбца
+# c[0, :2]
+
+# +
+# c[:, 1]
+
+# +
+# # выведем элемент в первой строке и первом столбце
+# c[0, 0]
+
+# +
+# # выведем элемент в последней строке и последнем столбце
+# c[-1, -1]
+
+# +
+# c[1, ::2]
+
+# +
+# d = np.arange(16).reshape(4, 2, -1)
+# d
+
+# +
+# d[2][1][0]
+
+# +
+# # выведем третью и четвертую матрицу [2:]
+# # и в них вторую строку [1] и все столбцы [:]
+# d[2:, 1, :]
+
+# +
+# # Оси массива
+
+# arr_2D = np.array([[1, 2], [3, 4]])
+# arr_2D
+
+# +
+# np.sum(arr_2D, axis=0)
+
+# +
+# np.sum(arr_2D, axis=1)
+
+# +
+# np.sum(arr_2D, axis=(0, 1)), np.sum(arr_2D)
+
+# +
+# # Отрицательные значения в параметре axis
+# np.sum(arr_2D, axis=-1), np.sum(arr_2D, axis=-2)
+
+# +
+# # Массив 3D
+# np.sum(arr_3D, axis=0)
+
+# +
+# # возьмем первую матрицу
+# arr_3D[0]
+
+# +
+# # возьмем вторую матрицу
+# arr_3D[1]
+
+# +
+# # и поэлементно сложим их
+# arr_3D[0] + arr_3D[1]
+
+# +
+# total = np.zeros((2, 3))
+
+# +
+# for i in range(2):
+#     total += i
+# total
+
+# +
+# # Сложение вдоль второй оси (axis = 1)
+# np.sum(arr_3D, axis=1)
+
+# +
+# # сложим столбцы первой
+# arr_3D[0][0] + arr_3D[0][1]
+
+# +
+# # и второй матрицы
+# arr_3D[1][0] + arr_3D[1][1]
+
+# +
+# total1 = np.zeros((2, 3))
+# for i in range(2):
+#     for j in range(2):
+#         total1[i] += arr_3D[i][j]
+
+# +
+# # Сложение вдоль третьей оси (axis = 2)
+# # применим np.sum()
+# np.sum(arr_3D, axis=2)
+
+# +
+# arr_3D[0][0][0] + arr_3D[0][0][1] + arr_3D[0][0][2]
+
+# +
+# # создадим нулевой массив 2 x 2 для записи результатов
+# total = np.zeros((2, 2))
+
+# # пройдемся по матрицам
+# for i in range(2):
+
+#     # по строкам матрицы
+#     for j in range(2):
+
+#         # и по столбцам
+#         for k in arr_3D[i][j]:
+
+#             # индексы i, j запишут результат сложения элементов строк k
+#             # в квадратную матрицу 2 x 2
+#             total[i][j] += k
+
+# total
+
+# +
+# # Сложение вдоль первой и второй осей (axis = (0, 1))
+# # применим функцию np.sum()
+# np.sum(arr_3D, axis=(0, 1))
+
+# +
+# # произведем сложение по оси 0
+# total_0 = np.zeros((2, 3))
+
+# for i in range(2):
+#     total_0 += arr_3D[i]
+
+# total_0
+
+# +
+# # произведем сложение по оси 1
+# total_1 = np.zeros(3)
+
+# for j in range(2):
+#     total_1 += total_0[j]
+
+# total_1
+
+# +
+# total = np.zeros(3)
+
+# for i in range(2):
+#     for j in range(2):
+#         total += arr_3D[i][j]
+
+# total
+
+# +
+# # Сложение вдоль всех трех осей (axis = (0, 1, 2))
+# np.sum(arr_3D, axis=(0, 1, 2))
+
+# +
+# total = 0
+# # в трех вложенных циклах мы пройдемся по всем элементам массива
+# for i in range(2):
+#     for j in range(2):
+#         for k in range(3):
+
+#             # и запишем сумму этих элементов в переменную total
+#             total += arr_3D[i][j][k]
+
+# total
+
+# +
+# # Операции с массивами
+# arr_3D = np.arange(12).reshape(2, 2, 3)
+# len(arr_3D), len(arr_3D[0][0])
+
+# +
+# # Распаковка массива
+# a = np.arange(1, 28).reshape(3, 9)
+# x, y, z = a
+# x, *y, z = a[0]
+
+# +
+# print(x)
+# print(y)
+# print(z)
+
+# +
+# # Изменение элементов массива
+
+# arr_2D = np.array([[1, 2, 3], [4, 5, 6]])
+# arr_2D[0, 0] = 2
+# arr_2D
+
+# +
+# # Запишем значение 1 в первую строку.
+# arr_2D[0] = 1
+# arr_2D
+
+# +
+# # Пусть третий столбец массива состоит из нулей.
+# arr_2D[:, 2] = 0
+# arr_2D
+
+# +
+# # Теперь потренируемся с трехмерным массивом.
+# arr_3D = np.arange(12).reshape(2, 2, 3)
+# # Выберем второй столбец второй матрицы и заменим значения столбца 7 и 10 на 0 и 1.
+# # при такой операции размер среза должен совпадать
+# # с количеством передаваемых значений
+# arr_3D[1, :, 1] = [0, 1]
+# arr_3D
+
+# +
+# arr_3D.fill(7)
+# arr_3D
+
+# +
+# # Сортировка массива и обратный порядок его элементов
+# a = np.array([[4, 8, 2], [2, 3, 1]])
+# np.sort(a)
+
+# +
+# np.sort(a, axis=1)
+
+# +
+# np.sort(a, axis=0)
+
+# +
+# np.sort(a, axis=None)
+
+# +
+# # Обратный порядок элементов массива
+# np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])[::-1]
+
+# +
+# # обратите внимание, мы используем и положительный, и отрицательный индексы
+# np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])[-3:3:-1]
+
+# +
+# # Сортировка в убывающем порядке
+
+# # возьмем простой одномерный массив
+# a = np.array([4, 2, 6, 1, 7, 3, 5])
+
+# np.sort(a)[::-1]
+
+# +
+# # здесь нужно сначала задать обратный порядок, а потом отсортировать
+# a[::-1].sort()
+
+# +
+# # Изменение размерности
+# arr_3D = np.arange(12).reshape(2, 2, 3)
+# arr_3D
+
+# +
+# # в нем 12 элементов
+# arr_3D.size
+
+# +
+# # при этом важно, чтобы общее количество элементов было тем же
+# arr_2D = arr_3D.reshape(2, 6)
+# arr_2D
+
+# +
+# # Функция np.resize() и метод .resize()
+# # функция np.resize() позволяет не сохранять прежнее количество элементов
+# # существующие элементы копируются в новые ячейки
+# np.resize(arr_2D, (3, 6))
+
+# +
+# arr_2D_copy = arr_2D.copy()
+
+# +
+# # Методы .flatten() и .ravel()
+# arr_3D.flatten()
+
+# +
+# arr_3D.ravel()
+
+# +
+# # np.newaxis
+
+# # создадим одномерный массив
+# a = np.array([1, 2, 3])
+# a.shape
+
+# +
+# b = a[np.newaxis, :]
+
+# print(b)
+# print(b.shape)
+
+# +
+# # Функция np.concatenate()
+# a = np.arange(4).reshape(2, 2)
+# b = np.arange(4, 8).reshape(2, 2)
+
+# +
+# np.concatenate((a, b), axis=0)
+
+# +
+# # Функция np.stack()
+# # при axis = 0 мы просто добавляем внешнее измерение
+# np.stack((a, b), axis=0)
diff --git a/python/makarov/chapter_1_oop.ipynb b/python/makarov/chapter_1_oop.ipynb
new file mode 100644
index 00000000..46fcf0f4
--- /dev/null
+++ b/python/makarov/chapter_1_oop.ipynb
@@ -0,0 +1,1002 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"ООП.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# создадим класс для работы с данными DataClass\n",
+    "# from typing import Dict, List, Union\n",
+    "\n",
+    "# # возьмем два двумерных массива\n",
+    "# import numpy as np\n",
+    "\n",
+    "\n",
+    "# class CatClass:\n",
+    "#     def __init__(self) -> None:\n",
+    "#         pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.CatClass"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Matrsokin = CatClass()\n",
+    "# type(Matrsokin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Атрибуты класса\n",
+    "# Давайте дополним наш класс\n",
+    "# CatClass атрибутом типа (назовем его type_)\n",
+    "# и атрибутом цвета шерсти (color).\n",
+    "\n",
+    "\n",
+    "# class CatClass:\n",
+    "#     color: str\n",
+    "#     type_: str\n",
+    "\n",
+    "#     def __init__(self, color: str) -> None:\n",
+    "#         self.color = color\n",
+    "#         self.type_ = \"cat\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('gray', 'cat')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Названия атрибутов могут быть любыми.\n",
+    "# При этом, обратите внимание,\n",
+    "# чтобы избежать конфликта с\n",
+    "# названием встроенноей функции type(),\n",
+    "# мы снабдили наш атрибут символом\n",
+    "# нижнего подчеркивания _.\n",
+    "# # повторно создадим объект класса CatClass,\n",
+    "# передав ему параметр цвета шерсти\n",
+    "# Matroskin = CatClass(\"gray\")\n",
+    "\n",
+    "# # и выведем атрибуты класса\n",
+    "# Matroskin.color, Matroskin.type_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Методы класса\n",
+    "# Дополним наш класс возможностью\n",
+    "# выполнять определенные действия (то есть # создадим методы класса).\n",
+    "\n",
+    "\n",
+    "# class CatClass:\n",
+    "#     color: str\n",
+    "#     type_: str\n",
+    "\n",
+    "#     def __init__(self, color) -> None:\n",
+    "#         self.color = color\n",
+    "#         self.type_ = \"cat\"\n",
+    "\n",
+    "#     def meow(self) -> None:\n",
+    "#         for meooww in range(3):\n",
+    "#             print(\"Мяу\")\n",
+    "\n",
+    "#     def info(self) -> None:\n",
+    "#         print(self.color, self.type_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Мяу\n",
+      "Мяу\n",
+      "Мяу\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Matroskin = CatClass(\"gray\")\n",
+    "# Matroskin.meow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gray cat\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Matroskin.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dog'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Принципы объектно-ориентированного программирования\n",
+    "# # Продолжим изучать тему классов\n",
+    "# и объектов и рассмотрим некоторые\n",
+    "# принципы объектно-ориентированного программирования\n",
+    "\n",
+    "# # Инкапсуляция\n",
+    "# # Инкапсуляция (encapsulation) —\n",
+    "# это способность класса хранить данные\n",
+    "# и методы внутри себя. Другими словами,\n",
+    "# объект класса можно представить\n",
+    "# в виде капсулы, в которой содержатся\n",
+    "# необходимые данные и методы.\n",
+    "\n",
+    "# # Публичные и частные атрибуты класса\n",
+    "# # С понятием инкапсуляции тесно\n",
+    "# связаны понятия публичных\n",
+    "# и частных атрибутов\n",
+    "# (public and private attributes).\n",
+    "# Публичные атрибуты — это те атрибуты,\n",
+    "# к которым можно получить доступ за пределами «капсулы» класса.\n",
+    "\n",
+    "# # изменим атрибут type_ объекта Matroskin на dog\n",
+    "# Matroskin.type_ = \"dog\"\n",
+    "\n",
+    "# # выведем этот атрибут\n",
+    "# Matroskin.type_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# class CatClass:\n",
+    "#     color: str\n",
+    "#     _type_: str\n",
+    "\n",
+    "#     def __init__(self, color: str) -> None:\n",
+    "#         self.color = color\n",
+    "\n",
+    "#         self._type_ = \"cat\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dog'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # вновь создадим объект класса CatClass\n",
+    "# Matroskin = CatClass(\"gray\")\n",
+    "\n",
+    "# # и изменим значение атрибута _type_\n",
+    "# Matroskin._type_ = \"dog\"\n",
+    "# Matroskin._type_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# class CatClass:\n",
+    "#     __type_: str\n",
+    "#     color: str\n",
+    "\n",
+    "#     def __init__(self, color):\n",
+    "#         self.color = color\n",
+    "#\n",
+    "#         self.__type_ = \"cat\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'CatClass' object has no attribute '__type_'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m Matroskin \u001b[39m=\u001b[39m CatClass(\u001b[39m'\u001b[39m\u001b[39mgray\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[39m# теперь при вызове этого атрибута Питон выдаст ошибку\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m Matroskin\u001b[39m.\u001b[39;49m__type_\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'CatClass' object has no attribute '__type_'"
+     ]
+    }
+   ],
+   "source": [
+    "# Matroskin = CatClass(\"gray\")\n",
+    "\n",
+    "# Matroskin.__type_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dog'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # поставим _CatClass перед __type_,\n",
+    "# # изменим значение атрибута и\n",
+    "# Matroskin._CatClass__type_ = \"dog\"\n",
+    "\n",
+    "# # выведем его\n",
+    "# Matroskin._CatClass__type_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Наследование\n",
+    "# # Принцип наследования (inheritance) предполагает,\n",
+    "# что один класс наследует атрибуты и методы другого.\n",
+    "# В этом случае, говорят про\n",
+    "# Родителя или Суперкласс (parent class, base class)\n",
+    "# и Потомка или Подкласс (child class, derived class).\n",
+    "# # Создание родительского класса и класса-потомка\n",
+    "\n",
+    "\n",
+    "# class Animal:\n",
+    "#     weight: int\n",
+    "#     length: int\n",
+    "\n",
+    "#     def __init__(self, weight: int, length: int) -> None:\n",
+    "#\n",
+    "#         self.weight = weight\n",
+    "#         self.length = length\n",
+    "\n",
+    "#     # объявим методы .eat()\n",
+    "\n",
+    "#     def eat(self) -> None:\n",
+    "#         print(\"Eating\")\n",
+    "\n",
+    "#     def sleep(self) -> None:\n",
+    "#         print(\"Sleeping\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# class Bird(Animal):\n",
+    "#     def move(self) -> None:\n",
+    "#         print(\"Flying\")\n",
+    "\n",
+    "\n",
+    "# # создадим объект pigeon\n",
+    "# и передадим ему значения веса и длины\n",
+    "# pigeon = Bird(0.3, 30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.3, 30)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # посмотрим на унаследованные\n",
+    "# у класса Animal атрибуты\n",
+    "# pigeon.weight, pigeon.length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eating\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # и методы\n",
+    "# pigeon.eat()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flying\n"
+     ]
+    }
+   ],
+   "source": [
+    "# pigeon.move()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Обратите внимание, в предыдущем\n",
+    "# примере класс Bird получил только\n",
+    "# новые методы, новых атрибутов в нем не появилось.\n",
+    "# Все дело в том,\n",
+    "# что если мы хотим добавить атрибут\n",
+    "# в классе-потомке, сохранив атрибуты\n",
+    "# родительского класса, нам нужно явным образом\n",
+    "# вызвать последние с помощью функции super().\n",
+    "\n",
+    "\n",
+    "# class Bird(Animal):\n",
+    "#     weight: int\n",
+    "#     length: int\n",
+    "#     flying_speed: int\n",
+    "\n",
+    "#     def __init__(self, weight: int, length: int, flying_speed: int) -> None:\n",
+    "#         super().__init__(weight, length)\n",
+    "#         self.flying_speed = flying_speed\n",
+    "\n",
+    "#     # вновь пропишем метод .move()\n",
+    "\n",
+    "#     def move(self) -> None:\n",
+    "#         print(\"Flying\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # вновь создадим объект pigeon класса\n",
+    "# Bird, но уже с тремя параметрами\n",
+    "# pigeon = Bird(0.3, 30, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.3, 30, 100)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # вызовем как унаследованные,\n",
+    "# так и собственные атрибуты класса Bird\n",
+    "# pigeon.weight, pigeon.length, pigeon.flying_speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sleeping\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # вызовем унаследованный метод .sleep()\n",
+    "# pigeon.sleep()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flying\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # и собственный метод .move()\n",
+    "# pigeon.move()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# class Flightless(Bird):\n",
+    "#     running_speed: int\n",
+    "\n",
+    "#     def __init__(self, running_speed: int) -> None:\n",
+    "#         self.running_speed = running_speed\n",
+    "\n",
+    "#     # кроме того, результатом метода .move() будет 'Running'\n",
+    "\n",
+    "#     def move(self) -> None:\n",
+    "#         print(\"Running\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ostrich = Flightless(60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "60"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # страусы бегают довольно быстро\n",
+    "# ostrich.running_speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ostrich.move()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eating\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # применим метод .eat() класса Animal\n",
+    "# ostrich.eat()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Множественное наследование\n",
+    "# # Питон позволяет классу наследовать\n",
+    "# методы двух и более классов\n",
+    "\n",
+    "\n",
+    "# class Fish:\n",
+    "#     def swim(self) -> None:\n",
+    "#         print(\"Swim\")\n",
+    "\n",
+    "\n",
+    "# class Bird:\n",
+    "#     def fly(self) -> None:\n",
+    "#         print(\"Flying\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # родительские классы мы перечисляем\n",
+    "# в скобках через зяпятую\n",
+    "\n",
+    "\n",
+    "# class SwimmingBird(Bird, Fish):\n",
+    "#     pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flying\n"
+     ]
+    }
+   ],
+   "source": [
+    "# duck = SwimmingBird()\n",
+    "# duck.fly()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Swim\n"
+     ]
+    }
+   ],
+   "source": [
+    "# duck.swim()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'классы и объекты'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Полиморфизм\n",
+    "# # Полиморфизм (polymorphism) означает,\n",
+    "# что один и тот же объект может принимать разные формы.\n",
+    "# В программировании, полиморфизм предполагает,\n",
+    "# что операторы, функции и объекты\n",
+    "# могут взаимодействовать с различными типами данных.\n",
+    "\n",
+    "# # для чисел '+' является оператором сложения\n",
+    "# 2 + 2\n",
+    "\n",
+    "\n",
+    "# # для строк - оператором объединения\n",
+    "# \"классы\" + \" и \" + \"объекты\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Полиморфизм функций\n",
+    "# # Полиморфные функции (polymorphic functions) —\n",
+    "# это функции, которые могут работать\n",
+    "# с разными типами данных.\n",
+    "# Классическим примером является встроенная функция len().\n",
+    "\n",
+    "\n",
+    "# class CatClass:\n",
+    "#     name: str\n",
+    "#     color: str\n",
+    "#     _type_: str\n",
+    "\n",
+    "#     def __init__(self, name: str, color: str):\n",
+    "#         self.name = name\n",
+    "#         self._type_ = \"кот\"\n",
+    "#         self.color = color\n",
+    "\n",
+    "#     # создадим метод .info() для вывода этих атрибутов\n",
+    "\n",
+    "#     def info(self):\n",
+    "#         print(f\"Меня зовут {self.name}, я {self._type_}, цвет моей шерсти {self.color}\")\n",
+    "\n",
+    "#     def sound(self):\n",
+    "#         print(\"Я умею мяукать\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# class DogClass:\n",
+    "#     name: str\n",
+    "#     color: str\n",
+    "#     _type_: str\n",
+    "\n",
+    "#     def __init__(self, name: str, color: str) -> None:\n",
+    "#         self.name = name\n",
+    "#         self._type_ = \"пес\"\n",
+    "#         self.color = color\n",
+    "\n",
+    "#     # и методами\n",
+    "\n",
+    "#     def info(self) -> None:\n",
+    "#         print(f\"Меня зовут {self.name}, я {self._type_}, цвет моей шерсти {self.color}\")\n",
+    "\n",
+    "#     # хотя, обратите внимание, действия внутри методов отличаются\n",
+    "#     def sound(self) -> None:\n",
+    "#         print(\"Я умею лаять\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cat = CatClass(\"Бегемот\", \"черный\")\n",
+    "# dog = DogClass(\"Барбос\", \"серый\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Меня зовут Бегемот, я кот, цвет моей шерсти черный\n",
+      "Я умею мяукать\n",
+      "\n",
+      "Меня зовут Барбос, я пес, цвет моей шерсти серый\n",
+      "Я умею лаять\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for animal in (cat, dog):\n",
+    "#     animal.info()\n",
+    "#     animal.sound()\n",
+    "#     print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Парадигма программирования\n",
+    "# Парадигма программирования — это,\n",
+    "# по большому счету, способ организации\n",
+    "# и стиль написания кода.\n",
+    "# Создание различных парадигм необходимо для того,\n",
+    "# чтобы справиться со все возрастающей\n",
+    "# сложностью компьютерных программ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "183.33333333333334"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Например, у нас есть список словарей\n",
+    "# с данными пациентов, и нам нужно посчитать их средний рост.\n",
+    "# patients = [\n",
+    "#     {\"name\": \"Николай\", \"height\": 178},\n",
+    "#     {\"name\": \"Иван\", \"height\": 182},\n",
+    "#     {\"name\": \"Алексей\", \"height\": 190},\n",
+    "# ]\n",
+    "\n",
+    "# # создадим переменные для общего роста и количества пациентов\n",
+    "# total, count = 0, 0\n",
+    "\n",
+    "# # в цикле for пройдемся по пациентам (отдельным словарям)\n",
+    "# for patient in patients:\n",
+    "#     # достанем значение роста и прибавим\n",
+    "# к текущему значению переменной total\n",
+    "#     total += patient[\"height\"]\n",
+    "#     # на каждой итерации будем увеличивать счетчик пациентов на один\n",
+    "#     count += 1\n",
+    "\n",
+    "# # разделим общий рост на количество пациентов,\n",
+    "# # чтобы получить среднее значение\n",
+    "# total / count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# class DataClass:\n",
+    "#     data: List[Dict[str, Union[int, float]]]\n",
+    "#     metric: str\n",
+    "#     __total: float\n",
+    "#     __count: int\n",
+    "\n",
+    "#     def __init__(self, data: List[Dict[str, Union[int, float]]]) -> None:\n",
+    "#         self.data = data\n",
+    "\n",
+    "#     def count_average(self, metric: str) -> float:\n",
+    "#         self.metric = metric\n",
+    "\n",
+    "#         self.__total = 0.0\n",
+    "#         self.__count = 0\n",
+    "\n",
+    "#         for item in self.data:\n",
+    "#             self.__total += item[self.metric]\n",
+    "#             self.__count += 1\n",
+    "\n",
+    "#         return self.__total / self.__count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "183.33333333333334"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим объект класса DataClass\n",
+    "# и передадим ему данные о пациентах\n",
+    "# data_object = DataClass(patients)\n",
+    "\n",
+    "# # вызовем метод .count_average() с метрикой 'height'\n",
+    "# data_object.count_average(\"height\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[178, 182, 190]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # функция list() преобразует результат в список\n",
+    "# heights = list(map(lambda x: x[\"height\"], patients))\n",
+    "# heights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "183.33333333333334"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sum(heights) / len(heights)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5,  2],\n",
+       "       [32, 20]])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# a = np.array([[0, 1, 2], [3, 4, 5]])\n",
+    "\n",
+    "# b = np.array([[5, 4], [3, 2], [1, 0]])\n",
+    "\n",
+    "\n",
+    "# # перемножим a и b по индексу j через функцию np.einsum()\n",
+    "# np.einsum(\"ij, jk -> ik\", a, b)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/chapter_1_oop.py b/python/makarov/chapter_1_oop.py
new file mode 100644
index 00000000..7881cc25
--- /dev/null
+++ b/python/makarov/chapter_1_oop.py
@@ -0,0 +1,442 @@
+"""ООП."""
+
+# +
+# создадим класс для работы с данными DataClass
+# from typing import Dict, List, Union
+
+# # возьмем два двумерных массива
+# import numpy as np
+
+
+# class CatClass:
+#     def __init__(self) -> None:
+#         pass
+
+# +
+# Matrsokin = CatClass()
+# type(Matrsokin)
+
+# +
+# Атрибуты класса
+# Давайте дополним наш класс
+# CatClass атрибутом типа (назовем его type_)
+# и атрибутом цвета шерсти (color).
+
+
+# class CatClass:
+#     color: str
+#     type_: str
+
+#     def __init__(self, color: str) -> None:
+#         self.color = color
+#         self.type_ = "cat"
+
+# +
+# # Названия атрибутов могут быть любыми.
+# При этом, обратите внимание,
+# чтобы избежать конфликта с
+# названием встроенноей функции type(),
+# мы снабдили наш атрибут символом
+# нижнего подчеркивания _.
+# # повторно создадим объект класса CatClass,
+# передав ему параметр цвета шерсти
+# Matroskin = CatClass("gray")
+
+# # и выведем атрибуты класса
+# Matroskin.color, Matroskin.type_
+
+# +
+# Методы класса
+# Дополним наш класс возможностью
+# выполнять определенные действия (то есть # создадим методы класса).
+
+
+# class CatClass:
+#     color: str
+#     type_: str
+
+#     def __init__(self, color) -> None:
+#         self.color = color
+#         self.type_ = "cat"
+
+#     def meow(self) -> None:
+#         for meooww in range(3):
+#             print("Мяу")
+
+#     def info(self) -> None:
+#         print(self.color, self.type_)
+
+# +
+# Matroskin = CatClass("gray")
+# Matroskin.meow()
+
+# +
+# Matroskin.info()
+
+# +
+# # Принципы объектно-ориентированного программирования
+# # Продолжим изучать тему классов
+# и объектов и рассмотрим некоторые
+# принципы объектно-ориентированного программирования
+
+# # Инкапсуляция
+# # Инкапсуляция (encapsulation) —
+# это способность класса хранить данные
+# и методы внутри себя. Другими словами,
+# объект класса можно представить
+# в виде капсулы, в которой содержатся
+# необходимые данные и методы.
+
+# # Публичные и частные атрибуты класса
+# # С понятием инкапсуляции тесно
+# связаны понятия публичных
+# и частных атрибутов
+# (public and private attributes).
+# Публичные атрибуты — это те атрибуты,
+# к которым можно получить доступ за пределами «капсулы» класса.
+
+# # изменим атрибут type_ объекта Matroskin на dog
+# Matroskin.type_ = "dog"
+
+# # выведем этот атрибут
+# Matroskin.type_
+
+# +
+# class CatClass:
+#     color: str
+#     _type_: str
+
+#     def __init__(self, color: str) -> None:
+#         self.color = color
+
+#         self._type_ = "cat"
+
+# +
+# # вновь создадим объект класса CatClass
+# Matroskin = CatClass("gray")
+
+# # и изменим значение атрибута _type_
+# Matroskin._type_ = "dog"
+# Matroskin._type_
+
+# +
+# class CatClass:
+#     __type_: str
+#     color: str
+
+#     def __init__(self, color):
+#         self.color = color
+#
+#         self.__type_ = "cat"
+
+# +
+# Matroskin = CatClass("gray")
+
+# Matroskin.__type_
+
+# +
+# # поставим _CatClass перед __type_,
+# # изменим значение атрибута и
+# Matroskin._CatClass__type_ = "dog"
+
+# # выведем его
+# Matroskin._CatClass__type_
+
+# +
+# # Наследование
+# # Принцип наследования (inheritance) предполагает,
+# что один класс наследует атрибуты и методы другого.
+# В этом случае, говорят про
+# Родителя или Суперкласс (parent class, base class)
+# и Потомка или Подкласс (child class, derived class).
+# # Создание родительского класса и класса-потомка
+
+
+# class Animal:
+#     weight: int
+#     length: int
+
+#     def __init__(self, weight: int, length: int) -> None:
+#
+#         self.weight = weight
+#         self.length = length
+
+#     # объявим методы .eat()
+
+#     def eat(self) -> None:
+#         print("Eating")
+
+#     def sleep(self) -> None:
+#         print("Sleeping")
+
+# +
+# class Bird(Animal):
+#     def move(self) -> None:
+#         print("Flying")
+
+
+# # создадим объект pigeon
+# и передадим ему значения веса и длины
+# pigeon = Bird(0.3, 30)
+
+# +
+# # посмотрим на унаследованные
+# у класса Animal атрибуты
+# pigeon.weight, pigeon.length
+
+# +
+# # и методы
+# pigeon.eat()
+
+# +
+# pigeon.move()
+
+# +
+# # Обратите внимание, в предыдущем
+# примере класс Bird получил только
+# новые методы, новых атрибутов в нем не появилось.
+# Все дело в том,
+# что если мы хотим добавить атрибут
+# в классе-потомке, сохранив атрибуты
+# родительского класса, нам нужно явным образом
+# вызвать последние с помощью функции super().
+
+
+# class Bird(Animal):
+#     weight: int
+#     length: int
+#     flying_speed: int
+
+#     def __init__(self, weight: int, length: int, flying_speed: int) -> None:
+#         super().__init__(weight, length)
+#         self.flying_speed = flying_speed
+
+#     # вновь пропишем метод .move()
+
+#     def move(self) -> None:
+#         print("Flying")
+
+# +
+# # вновь создадим объект pigeon класса
+# Bird, но уже с тремя параметрами
+# pigeon = Bird(0.3, 30, 100)
+
+# +
+# # вызовем как унаследованные,
+# так и собственные атрибуты класса Bird
+# pigeon.weight, pigeon.length, pigeon.flying_speed
+
+# +
+# # вызовем унаследованный метод .sleep()
+# pigeon.sleep()
+
+# +
+# # и собственный метод .move()
+# pigeon.move()
+
+# +
+# class Flightless(Bird):
+#     running_speed: int
+
+#     def __init__(self, running_speed: int) -> None:
+#         self.running_speed = running_speed
+
+#     # кроме того, результатом метода .move() будет 'Running'
+
+#     def move(self) -> None:
+#         print("Running")
+
+# +
+# ostrich = Flightless(60)
+
+# +
+# # страусы бегают довольно быстро
+# ostrich.running_speed
+
+# +
+# ostrich.move()
+
+# +
+# # применим метод .eat() класса Animal
+# ostrich.eat()
+
+# +
+# # Множественное наследование
+# # Питон позволяет классу наследовать
+# методы двух и более классов
+
+
+# class Fish:
+#     def swim(self) -> None:
+#         print("Swim")
+
+
+# class Bird:
+#     def fly(self) -> None:
+#         print("Flying")
+
+# +
+# # родительские классы мы перечисляем
+# в скобках через зяпятую
+
+
+# class SwimmingBird(Bird, Fish):
+#     pass
+
+# +
+# duck = SwimmingBird()
+# duck.fly()
+
+# +
+# duck.swim()
+
+# +
+# # Полиморфизм
+# # Полиморфизм (polymorphism) означает,
+# что один и тот же объект может принимать разные формы.
+# В программировании, полиморфизм предполагает,
+# что операторы, функции и объекты
+# могут взаимодействовать с различными типами данных.
+
+# # для чисел '+' является оператором сложения
+# 2 + 2
+
+
+# # для строк - оператором объединения
+# "классы" + " и " + "объекты"
+
+# +
+# # Полиморфизм функций
+# # Полиморфные функции (polymorphic functions) —
+# это функции, которые могут работать
+# с разными типами данных.
+# Классическим примером является встроенная функция len().
+
+
+# class CatClass:
+#     name: str
+#     color: str
+#     _type_: str
+
+#     def __init__(self, name: str, color: str):
+#         self.name = name
+#         self._type_ = "кот"
+#         self.color = color
+
+#     # создадим метод .info() для вывода этих атрибутов
+
+#     def info(self):
+#         print(f"Меня зовут {self.name}, я {self._type_}, цвет моей шерсти {self.color}")
+
+#     def sound(self):
+#         print("Я умею мяукать")
+
+# +
+# class DogClass:
+#     name: str
+#     color: str
+#     _type_: str
+
+#     def __init__(self, name: str, color: str) -> None:
+#         self.name = name
+#         self._type_ = "пес"
+#         self.color = color
+
+#     # и методами
+
+#     def info(self) -> None:
+#         print(f"Меня зовут {self.name}, я {self._type_}, цвет моей шерсти {self.color}")
+
+#     # хотя, обратите внимание, действия внутри методов отличаются
+#     def sound(self) -> None:
+#         print("Я умею лаять")
+
+# +
+# cat = CatClass("Бегемот", "черный")
+# dog = DogClass("Барбос", "серый")
+
+# +
+# for animal in (cat, dog):
+#     animal.info()
+#     animal.sound()
+#     print()
+
+# +
+# Парадигма программирования
+# Парадигма программирования — это,
+# по большому счету, способ организации
+# и стиль написания кода.
+# Создание различных парадигм необходимо для того,
+# чтобы справиться со все возрастающей
+# сложностью компьютерных программ.
+
+# +
+# # Например, у нас есть список словарей
+# с данными пациентов, и нам нужно посчитать их средний рост.
+# patients = [
+#     {"name": "Николай", "height": 178},
+#     {"name": "Иван", "height": 182},
+#     {"name": "Алексей", "height": 190},
+# ]
+
+# # создадим переменные для общего роста и количества пациентов
+# total, count = 0, 0
+
+# # в цикле for пройдемся по пациентам (отдельным словарям)
+# for patient in patients:
+#     # достанем значение роста и прибавим
+# к текущему значению переменной total
+#     total += patient["height"]
+#     # на каждой итерации будем увеличивать счетчик пациентов на один
+#     count += 1
+
+# # разделим общий рост на количество пациентов,
+# # чтобы получить среднее значение
+# total / count
+
+# +
+# class DataClass:
+#     data: List[Dict[str, Union[int, float]]]
+#     metric: str
+#     __total: float
+#     __count: int
+
+#     def __init__(self, data: List[Dict[str, Union[int, float]]]) -> None:
+#         self.data = data
+
+#     def count_average(self, metric: str) -> float:
+#         self.metric = metric
+
+#         self.__total = 0.0
+#         self.__count = 0
+
+#         for item in self.data:
+#             self.__total += item[self.metric]
+#             self.__count += 1
+
+#         return self.__total / self.__count
+
+# +
+# # создадим объект класса DataClass
+# и передадим ему данные о пациентах
+# data_object = DataClass(patients)
+
+# # вызовем метод .count_average() с метрикой 'height'
+# data_object.count_average("height")
+
+# +
+# # функция list() преобразует результат в список
+# heights = list(map(lambda x: x["height"], patients))
+# heights
+
+# +
+# sum(heights) / len(heights)
+
+# +
+# a = np.array([[0, 1, 2], [3, 4, 5]])
+
+# b = np.array([[5, 4], [3, 2], [1, 0]])
+
+
+# # перемножим a и b по индексу j через функцию np.einsum()
+# np.einsum("ij, jk -> ik", a, b)
diff --git a/python/makarov/chapter_1_tup_ls_set.ipynb b/python/makarov/chapter_1_tup_ls_set.ipynb
new file mode 100644
index 00000000..bb8d368d
--- /dev/null
+++ b/python/makarov/chapter_1_tup_ls_set.ipynb
@@ -0,0 +1,1312 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Списки, кортежи и множества.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[] []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # создадим одно пустое\n",
+    "# # возьмем уже знакомый нам по вводному курсу словарь с овощами\n",
+    "# # создадим две переменные и поместим в них пустые кортежи\n",
+    "# # создадим список из букв\n",
+    "# from typing import Dict, List, Set, Tuple, Union\n",
+    "\n",
+    "# some_list_1: list\n",
+    "# some_list_2: list\n",
+    "# some_list_1 = []\n",
+    "# some_list_2 = list()\n",
+    "\n",
+    "# print(some_list_1, some_list_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[3, 'число три', ['число', 'три'], {'число': 3}]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# number_three: List[Union[int, str, List[str], Dict[str, int]]]\n",
+    "# number_three = [3, \"число три\", [\"число\", \"три\"], {\"число\": 3}]\n",
+    "# number_three"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# len(number_three)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a e\n"
+     ]
+    }
+   ],
+   "source": [
+    "# abc_list: List[str] = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n",
+    "# # выведем первый и последний элементы\n",
+    "# print(abc_list[0], abc_list[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Игорь'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# salary_list: List[List[Union[str, int]]]\n",
+    "# salary_list = [[\"Анна\", 90000], [\"Игорь\", 85000], [\"Алексей\", 95000]]\n",
+    "# salary_list[1][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# abc_list.index(\"c\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Вт', 'Ср', 'Чт', 'Пт']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# days_list: List[str]\n",
+    "# days_list = [\"Пн\", \"Вт\", \"Ср\", \"Чт\", \"Пт\", \"Сб\", \"Вс\"]\n",
+    "# days_list[1:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Пн', 'Ср', 'Пт']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # начнем с Пн и будем брать дни через один вплоть до, но не включая, Сб [5]\n",
+    "# days_list[:5:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Понедельник', 'Вторник', 'Четверг']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# weekdays: List[str]\n",
+    "# weekdays = [\"Понедельник\", \"Вторник\"]\n",
+    "\n",
+    "# weekdays.append(\"Четверг\")\n",
+    "# weekdays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Понедельник', 'Вторник', 'Среда', 'Четверг']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # для этого методу .insert() мы передаем желаемый индекс нового элемента\n",
+    "# # и сам этот элемент\n",
+    "# weekdays.insert(2, \"Среда\")\n",
+    "# weekdays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Понедельник', 'Вторник', 'Среда', 'Пятница']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# weekdays[3] = \"Пятница\"\n",
+    "# weekdays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Понедельник', 'Вторник', 'Среда']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # для удаления по названию можно использовать метод .remove()\n",
+    "# weekdays.remove(\"Пятница\")\n",
+    "# weekdays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Понедельник', 'Вторник']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # ключевое слово del удаляет элемент по индексу\n",
+    "# del weekdays[2]\n",
+    "# weekdays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Вторник'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # метод .pop() не просто удаляет элемент по индексу,\n",
+    "# # но и выводит удаляемый элемент\n",
+    "# weekdays.pop(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Понедельник', 'Вторник', 'Среда', 'Четверг', 'Пятница']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # добавим к списку, в котором есть только понедельник, остальные дни\n",
+    "# more_weekdays: List[str]\n",
+    "# more_weekdays = [\"Вторник\", \"Среда\", \"Четверг\", \"Пятница\"]\n",
+    "\n",
+    "# weekdays.extend(more_weekdays)\n",
+    "# weekdays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Понедельник', 'Вторник', 'Среда', 'Четверг', 'Пятница', 'Суббота', 'Воскресенье']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # прибавим выходные\n",
+    "# weekend: List[str]\n",
+    "# weekend = [\"Суббота\", \"Воскресенье\"]\n",
+    "# print(weekdays + weekend)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # заново создадим список с днями недели\n",
+    "# week: List[str]\n",
+    "# week = [\n",
+    "#     \"Понедельник\",\n",
+    "#     \"Вторник\",\n",
+    "#     \"Среда\",\n",
+    "#     \"Четверг\",\n",
+    "#     \"Пятница\",\n",
+    "#     \"Суббота\",\n",
+    "#     \"Воскресенье\",\n",
+    "# ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Понедельник'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Mon = week[0]\n",
+    "# Mon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('Понедельник', 'Вторник', 'Среда')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # количество переменных должно быть равно количеству элементов среза\n",
+    "# Mon, Tue, Wed = week[:3]\n",
+    "# Mon, Tue, Wed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 10, 15, 20, 25, 30]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # отсортируем список по возрастанию\n",
+    "# nums: List[int]\n",
+    "# nums = [25, 10, 30, 20, 5, 15]\n",
+    "# sorted(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[30, 25, 20, 15, 10, 5]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # с помощью параметра reverse = True сортируем список по убыванию\n",
+    "# nums.sort(reverse=True)\n",
+    "# nums"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 10, 15, 20, 25, 30]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# nums.reverse()\n",
+    "# nums"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[30, 25, 20, 15, 10, 5]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list(reversed(nums))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# str_list: List[str]\n",
+    "# str_list = [\"P\", \"y\", \"t\", \"h\", \"o\", \"n\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Python'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# joined_str = \"\".join(str_list)\n",
+    "# joined_str"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# nums_: List[int]\n",
+    "# nums_ = [3, 2, 1, 4, 5, 12, 3, 3, 7, 9, 11, 15]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 15 75\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(min(nums_), max(nums_), sum(nums_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# names: List[str]\n",
+    "# names = [\"Артем\", \"Антон\", \"Александр\", \"Борис\", \"Виктор\", \"Геннадий\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Артем', 'Антон', 'Александр']"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим пустой список a_names\n",
+    "# a_names: List\n",
+    "# a_names = []\n",
+    "\n",
+    "# # пройдемся по списку имен\n",
+    "# for name in names:\n",
+    "\n",
+    "#     # если имя начинается с 'А'\n",
+    "#     if name.startswith(\"А\"):\n",
+    "\n",
+    "#         # добавим его в список a_names\n",
+    "#         a_names.append(name)\n",
+    "\n",
+    "# # посмотрим на результат\n",
+    "# a_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Артем', 'Антон', 'Александр']"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# a_names: List[str]\n",
+    "# a_names = [name for name in names if name.startswith(\"А\")]\n",
+    "# a_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['артем', 'антон', 'александр', 'борис', 'виктор', 'геннадий']"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lower_names: List[str]\n",
+    "# lower_names = [name.lower() for name in names]\n",
+    "# lower_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Артем', 'Антон', 'Александр', 'Борис', 'Вадим', 'Геннадий']"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# replace_name: List[str]\n",
+    "# replace_name = [name if name != \"Виктор\" else \"Вадим\" for name in names]\n",
+    "# replace_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "() ()\n"
+     ]
+    }
+   ],
+   "source": [
+    "# tuple_1: Tuple[()]\n",
+    "# tuple_2: Tuple[()]\n",
+    "# tuple_1, tuple_2 = (), tuple()\n",
+    "# print(tuple_1, tuple_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим кортеж\n",
+    "# letters: Tuple[str, ...]\n",
+    "# letters = (\"a\", \"b\", \"c\")\n",
+    "\n",
+    "# # и выведем его первый элемент\n",
+    "# letters[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['d', 'b', 'c']"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # преобразуем кортеж в список через функцию list()\n",
+    "# letters: List[str]\n",
+    "# letters = list(letters)\n",
+    "\n",
+    "# # теперь элементы можно изменять\n",
+    "# letters[0] = \"d\"\n",
+    "# letters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0, 'Microsoft') <class 'tuple'>\n",
+      "(1, 'Apple') <class 'tuple'>\n",
+      "(2, 'Tesla') <class 'tuple'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # создадим список с названием трех компаний\n",
+    "# companies: List[str]\n",
+    "# companies = [\"Microsoft\", \"Apple\", \"Tesla\"]\n",
+    "\n",
+    "# # и в цикле поместим результат работы функции enumerate()\n",
+    "# # в одну переменную company\n",
+    "# for company in enumerate(companies):\n",
+    "#     print(company, type(company))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# shopping_dict: Dict[str, int]\n",
+    "# shopping_dict = {\"огурцы\": 2, \"помидоры\": 3, \"лук\": 1, \"картофель\": 2}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('огурцы', 2)\n",
+      "('помидоры', 3)\n",
+      "('лук', 1)\n",
+      "('картофель', 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # пройдемся по ключам и значениям с помощью метода .items(),\n",
+    "# # но поместим результат в одну переменную item\n",
+    "# for item in shopping_dict.items():\n",
+    "#     print(item)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # если в кортеже три элемента, то и переменных должно быть три\n",
+    "\n",
+    "# a, b, c = (\"a\", \"b\", \"c\")\n",
+    "\n",
+    "# # выведем переменную a\n",
+    "# print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 Microsoft\n",
+      "1 Apple\n",
+      "2 Tesla\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # снова возьмем список компаний\n",
+    "# companies: List[str]\n",
+    "# companies = [\"Microsoft\", \"Apple\", \"Tesla\"]\n",
+    "\n",
+    "# # однако с функцией enumerate() используем две переменные\n",
+    "# for i, company in enumerate(companies):\n",
+    "#     print(i, company)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "огурцы 2\n",
+      "помидоры 3\n",
+      "лук 1\n",
+      "картофель 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# shopping_dict: Dict[str, int]\n",
+    "# shopping_dict = {\"огурцы\": 2, \"помидоры\": 3, \"лук\": 1, \"картофель\": 2}\n",
+    "\n",
+    "# # используем две переменные с методом .items()\n",
+    "# for k, v in shopping_dict.items():\n",
+    "#     print(k, v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x104a9a2c0>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим два списка: список имен и список доходов\n",
+    "# names: List[str]\n",
+    "# income: List[str]\n",
+    "# names = [\"Артем\", \"Антон\", \"Александр\", \"Борис\", \"Виктор\", \"Геннадий\"]\n",
+    "# income = [97000, 110000, 95000, 84000, 140000, 120000]\n",
+    "\n",
+    "# # передадим эти списки функции zip()\n",
+    "# zip(names, income)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Артем', 97000),\n",
+       " ('Антон', 110000),\n",
+       " ('Александр', 95000),\n",
+       " ('Борис', 84000),\n",
+       " ('Виктор', 140000),\n",
+       " ('Геннадий', 120000)]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list(zip(names, income))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "set() {'b', 'c', 'a'} {'b', 'c', 'a'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set_1: Set\n",
+    "# set_2: Set[str]\n",
+    "# set_3: Set[str]\n",
+    "# set_1 = set()\n",
+    "\n",
+    "# # и два непустых множества с повторяющимся элементом 'c'\n",
+    "# set_2 = {\"a\", \"b\", \"c\", \"c\"}\n",
+    "# set_3 = {\"a\", \"b\", \"c\", \"c\"}\n",
+    "\n",
+    "# print(set_1, set_2, set_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# not_a_set = {}\n",
+    "# type(not_a_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# vowels: Set[str]\n",
+    "# vowels = {\"а\", \"о\", \"э\", \"е\", \"у\", \"ё\", \"ю\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'а', 'е', 'о', 'у', 'э', 'ю', 'я', 'ё'}"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# vowels.add(\"я\")\n",
+    "# vowels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'а', 'е', 'и', 'о', 'у', 'ы', 'э', 'ю', 'я', 'ё'}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # передадим методу .update() список из двух гласных букв\n",
+    "# vowels.update([\"и\", \"ы\"])\n",
+    "# vowels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'а', 'е', 'и', 'о', 'у', 'щ', 'ы', 'э', 'ю', 'я', 'ё'}"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# vowels.add(\"щ\")\n",
+    "# vowels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'а', 'е', 'и', 'о', 'у', 'ы', 'э', 'ю', 'я', 'ё'}"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# vowels.remove(\"щ\")\n",
+    "# vowels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# {\"a\", \"b\", \"c\"} == {\"c\", \"b\", \"a\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# \"a\" in {\"a\", \"b\", \"c\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# \"a\" in {\"a\", \"b\", \"c\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# set_A: Set[str]\n",
+    "# set_B: Set[str]\n",
+    "# set_A = {\"a\", \"b\", \"c\"}\n",
+    "# set_B = {\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"}\n",
+    "\n",
+    "# set_A.issubset(set_B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# set_B.issuperset(set_A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# nlp: Set[str]\n",
+    "# cv: Set[str]\n",
+    "# nlp = {\"Анна\", \"Николай\", \"Павел\", \"Оксана\"}\n",
+    "# cv = {\"Николай\", \"Евгений\", \"Ольга\", \"Оксана\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Николай', 'Оксана', 'Анна', 'Ольга', 'Евгений', 'Павел'}\n",
+      "{'Николай', 'Оксана', 'Анна', 'Ольга', 'Евгений', 'Павел'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(nlp.union(cv))\n",
+    "# print(nlp | cv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Николай', 'Оксана'}\n",
+      "{'Николай', 'Оксана'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(nlp.intersection(cv))\n",
+    "# print(nlp & cv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Анна', 'Павел'}\n",
+      "{'Анна', 'Павел'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(nlp.difference(cv))\n",
+    "# print(nlp - cv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Евгений', 'Ольга'}\n",
+      "{'Евгений', 'Ольга'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(cv.difference(nlp))\n",
+    "# print(cv - nlp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Анна', 'Ольга', 'Евгений', 'Павел'}\n",
+      "{'Анна', 'Ольга', 'Евгений', 'Павел'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(nlp.symmetric_difference(cv))\n",
+    "# print(nlp ^ cv)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/chapter_1_tup_ls_set.py b/python/makarov/chapter_1_tup_ls_set.py
new file mode 100644
index 00000000..9054ec2d
--- /dev/null
+++ b/python/makarov/chapter_1_tup_ls_set.py
@@ -0,0 +1,346 @@
+"""Списки, кортежи и множества."""
+
+# +
+# # создадим одно пустое
+# # возьмем уже знакомый нам по вводному курсу словарь с овощами
+# # создадим две переменные и поместим в них пустые кортежи
+# # создадим список из букв
+# from typing import Dict, List, Set, Tuple, Union
+
+# some_list_1: list
+# some_list_2: list
+# some_list_1 = []
+# some_list_2 = list()
+
+# print(some_list_1, some_list_2)
+
+# +
+# number_three: List[Union[int, str, List[str], Dict[str, int]]]
+# number_three = [3, "число три", ["число", "три"], {"число": 3}]
+# number_three
+
+# +
+# len(number_three)
+
+# +
+# abc_list: List[str] = ["a", "b", "c", "d", "e"]
+# # выведем первый и последний элементы
+# print(abc_list[0], abc_list[-1])
+
+# +
+# salary_list: List[List[Union[str, int]]]
+# salary_list = [["Анна", 90000], ["Игорь", 85000], ["Алексей", 95000]]
+# salary_list[1][0]
+
+# +
+# abc_list.index("c")
+
+# +
+# days_list: List[str]
+# days_list = ["Пн", "Вт", "Ср", "Чт", "Пт", "Сб", "Вс"]
+# days_list[1:5]
+
+# +
+# # начнем с Пн и будем брать дни через один вплоть до, но не включая, Сб [5]
+# days_list[:5:2]
+
+# +
+# weekdays: List[str]
+# weekdays = ["Понедельник", "Вторник"]
+
+# weekdays.append("Четверг")
+# weekdays
+
+# +
+# # для этого методу .insert() мы передаем желаемый индекс нового элемента
+# # и сам этот элемент
+# weekdays.insert(2, "Среда")
+# weekdays
+
+# +
+# weekdays[3] = "Пятница"
+# weekdays
+
+# +
+# # для удаления по названию можно использовать метод .remove()
+# weekdays.remove("Пятница")
+# weekdays
+
+# +
+# # ключевое слово del удаляет элемент по индексу
+# del weekdays[2]
+# weekdays
+
+# +
+# # метод .pop() не просто удаляет элемент по индексу,
+# # но и выводит удаляемый элемент
+# weekdays.pop(1)
+
+# +
+# # добавим к списку, в котором есть только понедельник, остальные дни
+# more_weekdays: List[str]
+# more_weekdays = ["Вторник", "Среда", "Четверг", "Пятница"]
+
+# weekdays.extend(more_weekdays)
+# weekdays
+
+# +
+# # прибавим выходные
+# weekend: List[str]
+# weekend = ["Суббота", "Воскресенье"]
+# print(weekdays + weekend)
+
+# +
+# # заново создадим список с днями недели
+# week: List[str]
+# week = [
+#     "Понедельник",
+#     "Вторник",
+#     "Среда",
+#     "Четверг",
+#     "Пятница",
+#     "Суббота",
+#     "Воскресенье",
+# ]
+
+# +
+# Mon = week[0]
+# Mon
+
+# +
+# # количество переменных должно быть равно количеству элементов среза
+# Mon, Tue, Wed = week[:3]
+# Mon, Tue, Wed
+
+# +
+# # отсортируем список по возрастанию
+# nums: List[int]
+# nums = [25, 10, 30, 20, 5, 15]
+# sorted(nums)
+
+# +
+# # с помощью параметра reverse = True сортируем список по убыванию
+# nums.sort(reverse=True)
+# nums
+
+# +
+# nums.reverse()
+# nums
+
+# +
+# list(reversed(nums))
+
+# +
+# str_list: List[str]
+# str_list = ["P", "y", "t", "h", "o", "n"]
+
+# +
+# joined_str = "".join(str_list)
+# joined_str
+
+# +
+# nums_: List[int]
+# nums_ = [3, 2, 1, 4, 5, 12, 3, 3, 7, 9, 11, 15]
+
+# +
+# print(min(nums_), max(nums_), sum(nums_))
+
+# +
+# names: List[str]
+# names = ["Артем", "Антон", "Александр", "Борис", "Виктор", "Геннадий"]
+
+# +
+# # создадим пустой список a_names
+# a_names: List
+# a_names = []
+
+# # пройдемся по списку имен
+# for name in names:
+
+#     # если имя начинается с 'А'
+#     if name.startswith("А"):
+
+#         # добавим его в список a_names
+#         a_names.append(name)
+
+# # посмотрим на результат
+# a_names
+
+# +
+# a_names: List[str]
+# a_names = [name for name in names if name.startswith("А")]
+# a_names
+
+# +
+# lower_names: List[str]
+# lower_names = [name.lower() for name in names]
+# lower_names
+
+# +
+# replace_name: List[str]
+# replace_name = [name if name != "Виктор" else "Вадим" for name in names]
+# replace_name
+
+# +
+# tuple_1: Tuple[()]
+# tuple_2: Tuple[()]
+# tuple_1, tuple_2 = (), tuple()
+# print(tuple_1, tuple_2)
+
+# +
+# # создадим кортеж
+# letters: Tuple[str, ...]
+# letters = ("a", "b", "c")
+
+# # и выведем его первый элемент
+# letters[0]
+
+# +
+# # преобразуем кортеж в список через функцию list()
+# letters: List[str]
+# letters = list(letters)
+
+# # теперь элементы можно изменять
+# letters[0] = "d"
+# letters
+
+# +
+# # создадим список с названием трех компаний
+# companies: List[str]
+# companies = ["Microsoft", "Apple", "Tesla"]
+
+# # и в цикле поместим результат работы функции enumerate()
+# # в одну переменную company
+# for company in enumerate(companies):
+#     print(company, type(company))
+
+# +
+# shopping_dict: Dict[str, int]
+# shopping_dict = {"огурцы": 2, "помидоры": 3, "лук": 1, "картофель": 2}
+
+# +
+# # пройдемся по ключам и значениям с помощью метода .items(),
+# # но поместим результат в одну переменную item
+# for item in shopping_dict.items():
+#     print(item)
+
+# +
+# # если в кортеже три элемента, то и переменных должно быть три
+
+# a, b, c = ("a", "b", "c")
+
+# # выведем переменную a
+# print(a)
+
+# +
+# # снова возьмем список компаний
+# companies: List[str]
+# companies = ["Microsoft", "Apple", "Tesla"]
+
+# # однако с функцией enumerate() используем две переменные
+# for i, company in enumerate(companies):
+#     print(i, company)
+
+# +
+# shopping_dict: Dict[str, int]
+# shopping_dict = {"огурцы": 2, "помидоры": 3, "лук": 1, "картофель": 2}
+
+# # используем две переменные с методом .items()
+# for k, v in shopping_dict.items():
+#     print(k, v)
+
+# +
+# # создадим два списка: список имен и список доходов
+# names: List[str]
+# income: List[str]
+# names = ["Артем", "Антон", "Александр", "Борис", "Виктор", "Геннадий"]
+# income = [97000, 110000, 95000, 84000, 140000, 120000]
+
+# # передадим эти списки функции zip()
+# zip(names, income)
+
+# +
+# list(zip(names, income))
+
+# +
+# set_1: Set
+# set_2: Set[str]
+# set_3: Set[str]
+# set_1 = set()
+
+# # и два непустых множества с повторяющимся элементом 'c'
+# set_2 = {"a", "b", "c", "c"}
+# set_3 = {"a", "b", "c", "c"}
+
+# print(set_1, set_2, set_3)
+
+# +
+# not_a_set = {}
+# type(not_a_set)
+
+# +
+# vowels: Set[str]
+# vowels = {"а", "о", "э", "е", "у", "ё", "ю"}
+
+# +
+# vowels.add("я")
+# vowels
+
+# +
+# # передадим методу .update() список из двух гласных букв
+# vowels.update(["и", "ы"])
+# vowels
+
+# +
+# vowels.add("щ")
+# vowels
+
+# +
+# vowels.remove("щ")
+# vowels
+
+# +
+# {"a", "b", "c"} == {"c", "b", "a"}
+
+# +
+# "a" in {"a", "b", "c"}
+
+# +
+# "a" in {"a", "b", "c"}
+
+# +
+# set_A: Set[str]
+# set_B: Set[str]
+# set_A = {"a", "b", "c"}
+# set_B = {"a", "b", "c", "d", "e", "f"}
+
+# set_A.issubset(set_B)
+
+# +
+# set_B.issuperset(set_A)
+
+# +
+# nlp: Set[str]
+# cv: Set[str]
+# nlp = {"Анна", "Николай", "Павел", "Оксана"}
+# cv = {"Николай", "Евгений", "Ольга", "Оксана"}
+
+# +
+# print(nlp.union(cv))
+# print(nlp | cv)
+
+# +
+# print(nlp.intersection(cv))
+# print(nlp & cv)
+
+# +
+# print(nlp.difference(cv))
+# print(nlp - cv)
+
+# +
+# print(cv.difference(nlp))
+# print(cv - nlp)
+
+# +
+# print(nlp.symmetric_difference(cv))
+# print(nlp ^ cv)
diff --git a/python/makarov/pandas.ipynb b/python/makarov/pandas.ipynb
new file mode 100644
index 00000000..f48543e5
--- /dev/null
+++ b/python/makarov/pandas.ipynb
@@ -0,0 +1,3203 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Pandas.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Способ 2. Подключение к базе данных SQL\n",
+    "# # импортируем модуль sqlite3 для работы с базой данных SQL\n",
+    "# import sqlite3 as sql\n",
+    "\n",
+    "# # Способ 3. Создание датафрейма из словаря\n",
+    "# import numpy as np\n",
+    "\n",
+    "# # создадим пустой словарь\n",
+    "# # Создание датафрейма\n",
+    "# # Способ 1. Создание датафрейма из файла\n",
+    "# import pandas as pd\n",
+    "\n",
+    "# # испортируем файл из папки content и выведем первые три строки\n",
+    "# csv_zip = pd.read_csv(\"/content/train.zip\")\n",
+    "# csv_zip.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # импортируем данные в формате Excel, указав номер листа, который хотим использовать\n",
+    "# excel_data = pd.read_excel(\"/content/iris.xlsx\", sheet_name=0)\n",
+    "# excel_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # передадим соответствующую ссылку в функцию pd.read_html()\n",
+    "# # в параметре match укажем ключевые слова, которые помогут найти нужную таблицу\n",
+    "# html_data = pd.read_html(\n",
+    "#     \"https://en.wikipedia.org/wiki/World_population\", match=\"World population\"\n",
+    "# )\n",
+    "# # мы получили пять результатов\n",
+    "# len(html_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # создадим соединение с базой данных chinook\n",
+    "# conn = sql.connect(\"/content/chinook.db\")\n",
+    "\n",
+    "# # выберем все строки из таблицы tracks\n",
+    "# sql_data = pd.read_sql(\"SELECT * FROM tracks\", conn)  # vs. read_sql_query\n",
+    "\n",
+    "# # посмотрим на результат\n",
+    "# sql_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # создадим несколько списков и массивов Numpy с информацией о семи странах мира\n",
+    "# country = np.array(\n",
+    "#     [\n",
+    "#         \"China\",\n",
+    "#         \"Vietnam\",\n",
+    "#         \"United Kingdom\",\n",
+    "#         \"Russia\",\n",
+    "#         \"Argentina\",\n",
+    "#         \"Bolivia\",\n",
+    "#         \"South Africa\",\n",
+    "#     ]\n",
+    "# )\n",
+    "# capital = [\"Beijing\", \"Hanoi\", \"London\", \"Moscow\", \"Buenos Aires\", \"Sucre\", \"Pretoria\"]\n",
+    "# population = [1400, 97, 67, 144, 45, 12, 59]  # млн. человек\n",
+    "# area = [9.6, 0.3, 0.2, 17.1, 2.8, 1.1, 1.2]  # млн. кв. км.\n",
+    "# sea = [1] * 5 + [0, 1]  # выход к морю (в этом списке его нет только у Боливии)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# countries_dict = {}\n",
+    "# # превратим эти списки в значения словаря,\n",
+    "# # одновременно снабдив необходимыми ключами\n",
+    "# countries_dict[\"country\"] = country\n",
+    "# countries_dict[\"capital\"] = capital\n",
+    "# countries_dict[\"popilation\"] = population\n",
+    "# countries_dict[\"area\"] = area\n",
+    "# countries_dict[\"sea\"] = sea\n",
+    "# countries = pd.DataFrame(countries_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0  1  2\n",
+       "0  1  1  1\n",
+       "1  2  2  2\n",
+       "2  3  3  3"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Способ 4. Создание датафрейма из 2D массива Numpy\n",
+    "# # внешнее измерение будет столбцами, внутренее - строками\n",
+    "# arr = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])\n",
+    "\n",
+    "# pd.DataFrame(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['country', 'capital', 'popilation', 'area', 'sea'], dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=7, step=1)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['China', 'Beijing', 1400, 9.6, 1],\n",
+       "       ['Vietnam', 'Hanoi', 97, 0.3, 1],\n",
+       "       ['United Kingdom', 'London', 67, 0.2, 1],\n",
+       "       ['Russia', 'Moscow', 144, 17.1, 1],\n",
+       "       ['Argentina', 'Buenos Aires', 45, 2.8, 1],\n",
+       "       ['Bolivia', 'Sucre', 12, 1.1, 0],\n",
+       "       ['South Africa', 'Pretoria', 59, 1.2, 1]], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=7, step=1)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.axes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, (7, 5), 35)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.ndim, countries.shape, countries.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country        object\n",
+       "capital        object\n",
+       "popilation      int64\n",
+       "area          float64\n",
+       "sea             int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index         132\n",
+       "country        56\n",
+       "capital        56\n",
+       "popilation     56\n",
+       "area           56\n",
+       "sea            56\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.memory_usage()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>popilation</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           country       capital  popilation  area  sea\n",
+       "CN           China       Beijing        1400   9.6    1\n",
+       "VN         Vietnam         Hanoi          97   0.3    1\n",
+       "GB  United Kingdom        London          67   0.2    1\n",
+       "RU          Russia        Moscow         144  17.1    1\n",
+       "AR       Argentina  Buenos Aires          45   2.8    1\n",
+       "BO         Bolivia         Sucre          12   1.1    0\n",
+       "ZA    South Africa      Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Индекс датафрейма\n",
+    "# # создадим список с кодами стран\n",
+    "# custom_index = [\"CN\", \"VN\", \"GB\", \"RU\", \"AR\", \"BO\", \"ZA\"]\n",
+    "\n",
+    "# countries = pd.DataFrame(countries_dict, index=custom_index)\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>popilation</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CN</td>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>VN</td>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>GB</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>RU</td>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AR</td>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BO</td>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>ZA</td>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  index         country       capital  popilation  area  sea\n",
+       "0    CN           China       Beijing        1400   9.6    1\n",
+       "1    VN         Vietnam         Hanoi          97   0.3    1\n",
+       "2    GB  United Kingdom        London          67   0.2    1\n",
+       "3    RU          Russia        Moscow         144  17.1    1\n",
+       "4    AR       Argentina  Buenos Aires          45   2.8    1\n",
+       "5    BO         Bolivia         Sucre          12   1.1    0\n",
+       "6    ZA    South Africa      Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # при этом параметр inplace = True сделает изменения постоянными\n",
+    "# countries.reset_index(inplace=True)\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>popilation</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              country       capital  popilation  area  sea\n",
+       "index                                                     \n",
+       "CN              China       Beijing        1400   9.6    1\n",
+       "VN            Vietnam         Hanoi          97   0.3    1\n",
+       "GB     United Kingdom        London          67   0.2    1\n",
+       "RU             Russia        Moscow         144  17.1    1\n",
+       "AR          Argentina  Buenos Aires          45   2.8    1\n",
+       "BO            Bolivia         Sucre          12   1.1    0\n",
+       "ZA       South Africa      Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # передадим методу название столбца, который хотим сделать индексом\n",
+    "# countries.set_index(\"index\", inplace=True)\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>popilation</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          country       capital  popilation  area  sea\n",
+       "0           China       Beijing        1400   9.6    1\n",
+       "1         Vietnam         Hanoi          97   0.3    1\n",
+       "2  United Kingdom        London          67   0.2    1\n",
+       "3          Russia        Moscow         144  17.1    1\n",
+       "4       Argentina  Buenos Aires          45   2.8    1\n",
+       "5         Bolivia         Sucre          12   1.1    0\n",
+       "6    South Africa      Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.reset_index(drop=True, inplace=True)\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>popilation</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           country       capital  popilation  area  sea\n",
+       "CN           China       Beijing        1400   9.6    1\n",
+       "VN         Vietnam         Hanoi          97   0.3    1\n",
+       "GB  United Kingdom        London          67   0.2    1\n",
+       "RU          Russia        Moscow         144  17.1    1\n",
+       "AR       Argentina  Buenos Aires          45   2.8    1\n",
+       "BO         Bolivia         Sucre          12   1.1    0\n",
+       "ZA    South Africa      Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.index = custom_index\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Многоуровневый индекс\n",
+    "# # создадим список из кортежей с названием континента и кодом страны\n",
+    "# rows = [\n",
+    "#     (\"Asia\", \"CN\"),\n",
+    "#     (\"Asia\", \"VN\"),\n",
+    "#     (\"Europe\", \"GB\"),\n",
+    "#     (\"Europe\", \"RU\"),\n",
+    "#     (\"S. America\", \"AR\"),\n",
+    "#     (\"S. America\", \"BO\"),\n",
+    "#     (\"Africa\", \"ZA\"),\n",
+    "# ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # в столбцах название страны и столицы мы объединим в категорию names\n",
+    "# # а размер населения, площадь и выход к морю в data\n",
+    "# cols = [\n",
+    "#     (\"names\", \"country\"),\n",
+    "#     (\"names\", \"capital\"),\n",
+    "#     (\"data\", \"population\"),\n",
+    "#     (\"data\", \"area\"),\n",
+    "#     (\"data\", \"sea\"),\n",
+    "# ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # создадим многоуровневый индекс для строк\n",
+    "# # индексам присвоим названия через names = ['region', 'code']\n",
+    "# custom_multindex = pd.MultiIndex.from_tuples(rows, names=[\"region\", \"code\"])\n",
+    "\n",
+    "# # сделаем то же самое для столбцов\n",
+    "# custom_multicols = pd.MultiIndex.from_tuples(cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">names</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">data</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>region</th>\n",
+       "      <th>code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Asia</th>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Europe</th>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">S. America</th>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa</th>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          names                     data          \n",
+       "                        country       capital population  area sea\n",
+       "region     code                                                   \n",
+       "Asia       CN             China       Beijing       1400   9.6   1\n",
+       "           VN           Vietnam         Hanoi         97   0.3   1\n",
+       "Europe     GB    United Kingdom        London         67   0.2   1\n",
+       "           RU            Russia        Moscow        144  17.1   1\n",
+       "S. America AR         Argentina  Buenos Aires         45   2.8   1\n",
+       "           BO           Bolivia         Sucre         12   1.1   0\n",
+       "Africa     ZA      South Africa      Pretoria         59   1.2   1"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.index = custom_multindex\n",
+    "# countries.columns = custom_multicols\n",
+    "\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           country       capital  population  area  sea\n",
+       "CN           China       Beijing        1400   9.6    1\n",
+       "VN         Vietnam         Hanoi          97   0.3    1\n",
+       "GB  United Kingdom        London          67   0.2    1\n",
+       "RU          Russia        Moscow         144  17.1    1\n",
+       "AR       Argentina  Buenos Aires          45   2.8    1\n",
+       "BO         Bolivia         Sucre          12   1.1    0\n",
+       "ZA    South Africa      Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # вернемся к обычному индексу и названиям столбцов\n",
+    "# custom_cols = [\"country\", \"capital\", \"population\", \"area\", \"sea\"]\n",
+    "\n",
+    "# countries.index = custom_index\n",
+    "# countries.columns = custom_cols\n",
+    "\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'country': {'CN': 'China', 'VN': 'Vietnam', 'GB': 'United Kingdom', 'RU': 'Russia', 'AR': 'Argentina', 'BO': 'Bolivia', 'ZA': 'South Africa'}, 'capital': {'CN': 'Beijing', 'VN': 'Hanoi', 'GB': 'London', 'RU': 'Moscow', 'AR': 'Buenos Aires', 'BO': 'Sucre', 'ZA': 'Pretoria'}, 'population': {'CN': 1400, 'VN': 97, 'GB': 67, 'RU': 144, 'AR': 45, 'BO': 12, 'ZA': 59}, 'area': {'CN': 9.6, 'VN': 0.3, 'GB': 0.2, 'RU': 17.1, 'AR': 2.8, 'BO': 1.1, 'ZA': 1.2}, 'sea': {'CN': 1, 'VN': 1, 'GB': 1, 'RU': 1, 'AR': 1, 'BO': 0, 'ZA': 1}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Преобразование в другие форматы\n",
+    "# print(countries.to_dict())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['China', 'Beijing', 1400, 9.6, 1],\n",
+       "       ['Vietnam', 'Hanoi', 97, 0.3, 1],\n",
+       "       ['United Kingdom', 'London', 67, 0.2, 1],\n",
+       "       ['Russia', 'Moscow', 144, 17.1, 1],\n",
+       "       ['Argentina', 'Buenos Aires', 45, 2.8, 1],\n",
+       "       ['Bolivia', 'Sucre', 12, 1.1, 0],\n",
+       "       ['South Africa', 'Pretoria', 59, 1.2, 1]], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Создание Series\n",
+    "# country_list = [\n",
+    "#     \"China\",\n",
+    "#     \"South Africa\",\n",
+    "#     \"United Kingdom\",\n",
+    "#     \"Russia\",\n",
+    "#     \"Argentina\",\n",
+    "#     \"Vietnam\",\n",
+    "#     \"Australia\",\n",
+    "# ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0             China\n",
+       "1      South Africa\n",
+       "2    United Kingdom\n",
+       "3            Russia\n",
+       "4         Argentina\n",
+       "5           Vietnam\n",
+       "6         Australia\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# country_series = pd.Series(country_list)\n",
+    "# country_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CN             China\n",
+       "ZA      South Africa\n",
+       "GB    United Kingdom\n",
+       "RU            Russia\n",
+       "AR         Argentina\n",
+       "VN           Vietnam\n",
+       "AU         Australia\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Создание Series из словаря\n",
+    "# country_dict = {\n",
+    "#     \"CN\": \"China\",\n",
+    "#     \"ZA\": \"South Africa\",\n",
+    "#     \"GB\": \"United Kingdom\",\n",
+    "#     \"RU\": \"Russia\",\n",
+    "#     \"AR\": \"Argentina\",\n",
+    "#     \"VN\": \"Vietnam\",\n",
+    "#     \"AU\": \"Australia\",\n",
+    "# }\n",
+    "\n",
+    "# country_Series = pd.Series(country_dict)\n",
+    "# country_Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "country\n",
+      "capital\n",
+      "population\n",
+      "area\n",
+      "sea\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Доступ к строкам, столбцам и элементам\n",
+    "# for column in countries:\n",
+    "#     print(column)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CN\n",
+      "country         China\n",
+      "capital       Beijing\n",
+      "population       1400\n",
+      "area              9.6\n",
+      "sea                 1\n",
+      "Name: CN, dtype: object\n",
+      "VN\n",
+      "country       Vietnam\n",
+      "capital         Hanoi\n",
+      "population         97\n",
+      "area              0.3\n",
+      "sea                 1\n",
+      "Name: VN, dtype: object\n",
+      "GB\n",
+      "country       United Kingdom\n",
+      "capital               London\n",
+      "population                67\n",
+      "area                     0.2\n",
+      "sea                        1\n",
+      "Name: GB, dtype: object\n",
+      "RU\n",
+      "country       Russia\n",
+      "capital       Moscow\n",
+      "population       144\n",
+      "area            17.1\n",
+      "sea                1\n",
+      "Name: RU, dtype: object\n",
+      "AR\n",
+      "country          Argentina\n",
+      "capital       Buenos Aires\n",
+      "population              45\n",
+      "area                   2.8\n",
+      "sea                      1\n",
+      "Name: AR, dtype: object\n",
+      "BO\n",
+      "country       Bolivia\n",
+      "capital         Sucre\n",
+      "population         12\n",
+      "area              1.1\n",
+      "sea                 0\n",
+      "Name: BO, dtype: object\n",
+      "ZA\n",
+      "country       South Africa\n",
+      "capital           Pretoria\n",
+      "population              59\n",
+      "area                   1.2\n",
+      "sea                      1\n",
+      "Name: ZA, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # прервем цикл после первой итерации с помощью break\n",
+    "# for index, row in countries.iterrows():\n",
+    "#     print(index)\n",
+    "#     print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Beijing is the capital of China\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for _, row in countries.iterrows():\n",
+    "#     # например, сформируем вот такое предложение\n",
+    "#     print(row[\"capital\"] + \" is the capital of \" + row[\"country\"])\n",
+    "#     break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CN         Beijing\n",
+       "VN           Hanoi\n",
+       "GB          London\n",
+       "RU          Moscow\n",
+       "AR    Buenos Aires\n",
+       "BO           Sucre\n",
+       "ZA        Pretoria\n",
+       "Name: capital, dtype: object"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Доступ к столбцам\n",
+    "\n",
+    "# # выведем столбец capital датафрейма countries\n",
+    "# countries[\"capital\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CN         Beijing\n",
+       "VN           Hanoi\n",
+       "GB          London\n",
+       "RU          Moscow\n",
+       "AR    Buenos Aires\n",
+       "BO           Sucre\n",
+       "ZA        Pretoria\n",
+       "Name: capital, dtype: object"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # однако в этом случае название не должно содержать пробелов\n",
+    "# countries.capital"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>capital</th>\n",
+       "      <th>area</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>London</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>17.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>2.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>1.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         capital  area\n",
+       "CN       Beijing   9.6\n",
+       "VN         Hanoi   0.3\n",
+       "GB        London   0.2\n",
+       "RU        Moscow  17.1\n",
+       "AR  Buenos Aires   2.8\n",
+       "BO         Sucre   1.1\n",
+       "ZA      Pretoria   1.2"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries[[\"capital\", \"area\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         capital  population\n",
+       "CN       Beijing        1400\n",
+       "VN         Hanoi          97\n",
+       "GB        London          67\n",
+       "RU        Moscow         144\n",
+       "AR  Buenos Aires          45\n",
+       "BO         Sucre          12\n",
+       "ZA      Pretoria          59"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.filter(items=[\"capital\", \"population\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           country       capital  population  area  sea\n",
+       "VN         Vietnam         Hanoi          97   0.3    1\n",
+       "GB  United Kingdom        London          67   0.2    1\n",
+       "RU          Russia        Moscow         144  17.1    1\n",
+       "AR       Argentina  Buenos Aires          45   2.8    1"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Доступ к строкам\n",
+    "# # выведем строки со второй по пятую (не включительно)\n",
+    "# countries[1:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    capital  population  area\n",
+       "CN  Beijing        1400   9.6\n",
+       "RU   Moscow         144  17.1\n",
+       "VN    Hanoi          97   0.3"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Методы .loc[] и .iloc[]\n",
+    "# # для этого передадим методу .loc[] два списка:\n",
+    "# # с индексами строк и названиями столбцов\n",
+    "# countries.loc[[\"CN\", \"RU\", \"VN\"], [\"capital\", \"population\", \"area\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         capital  population  area\n",
+       "CN       Beijing        1400   9.6\n",
+       "VN         Hanoi          97   0.3\n",
+       "GB        London          67   0.2\n",
+       "RU        Moscow         144  17.1\n",
+       "AR  Buenos Aires          45   2.8\n",
+       "BO         Sucre          12   1.1\n",
+       "ZA      Pretoria          59   1.2"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # например, выведем все строки датафрейма\n",
+    "# countries.loc[:, [\"capital\", \"population\", \"area\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sea\n",
+       "CN    1\n",
+       "VN    1\n",
+       "GB    1\n",
+       "RU    1\n",
+       "AR    1\n",
+       "BO    0\n",
+       "ZA    1"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Метод .loc[] также поддерживает значения Boolean.\n",
+    "# # например, выведем все строки и только последний столбец,\n",
+    "# # передав список соответствующих логических значений\n",
+    "# countries.loc[:, [False, False, False, False, True]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# позволяет узнать порядковый номер\n",
+    "# (начиная с нуля) строки или столбца\n",
+    "# по их индексу и названию соответственно.\n",
+    "# # выведем номер строки с индексом RU\n",
+    "# countries.index.get_loc(\"RU\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    country  capital  population\n",
+       "CN    China  Beijing        1400\n",
+       "RU   Russia   Moscow         144\n",
+       "BO  Bolivia    Sucre          12"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # теперь в списки мы передаем номера строк и столбцов,\n",
+    "# # нумерация начинается с нуля\n",
+    "# countries.iloc[[0, 3, 5], [0, 1, 2]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    area  sea\n",
+       "CN   9.6    1\n",
+       "VN   0.3    1\n",
+       "GB   0.2    1"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.iloc[:3, -2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    population  area\n",
+       "CN        1400   9.6\n",
+       "RU         144  17.1"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries[[\"population\", \"area\"]].iloc[[0, 3]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">names</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">data</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>region</th>\n",
+       "      <th>code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Asia</th>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Europe</th>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">S. America</th>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa</th>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          names                     data          \n",
+       "                        country       capital population  area sea\n",
+       "region     code                                                   \n",
+       "Asia       CN             China       Beijing       1400   9.6   1\n",
+       "           VN           Vietnam         Hanoi         97   0.3   1\n",
+       "Europe     GB    United Kingdom        London         67   0.2   1\n",
+       "           RU            Russia        Moscow        144  17.1   1\n",
+       "S. America AR         Argentina  Buenos Aires         45   2.8   1\n",
+       "           BO           Bolivia         Sucre         12   1.1   0\n",
+       "Africa     ZA      South Africa      Pretoria         59   1.2   1"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Многоуровневый индекс и методы .loc[] и .iloc[]\n",
+    "# # вновь создадим датафрейм с многоуровневым индексом по строкам и столбцам\n",
+    "# countries.index = custom_multindex\n",
+    "# countries.columns = custom_multicols\n",
+    "\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "names  country         China\n",
+       "       capital       Beijing\n",
+       "data   population       1400\n",
+       "       area              9.6\n",
+       "       sea                 1\n",
+       "Name: (Asia, CN), dtype: object"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# countries.loc[\"Asia\", \"CN\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "data  population    1400.0\n",
+       "      area             9.6\n",
+       "      sea              1.0\n",
+       "Name: (Asia, CN), dtype: float64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # выведем первую строку и столбцы с числовыми данными\n",
+    "# countries.loc[\n",
+    "#     (\"Asia\", \"CN\"), [(\"data\", \"population\"), (\"data\", \"area\"), (\"data\", \"sea\")]\n",
+    "# ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">names</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">data</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>region</th>\n",
+       "      <th>code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Asia</th>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               names                data         \n",
+       "             country  capital population area sea\n",
+       "region code                                      \n",
+       "Asia   CN      China  Beijing       1400  9.6   1\n",
+       "       VN    Vietnam    Hanoi         97  0.3   1"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # например, выведем только азиатские страны\n",
+    "# countries.loc[(\"Asia\", [\"CN\", \"VN\"]), :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">names</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">data</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               names               data          \n",
+       "             country capital population  area sea\n",
+       "code                                             \n",
+       "GB    United Kingdom  London         67   0.2   1\n",
+       "RU            Russia  Moscow        144  17.1   1"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Метод .xs()\n",
+    "# # выберем Европу из уровня region\n",
+    "# # axis = 0 указывает, что мы берем строки\n",
+    "# countries.xs(\"Europe\", level=\"region\", axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>names</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>region</th>\n",
+       "      <th>code</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Asia</th>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Europe</th>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">S. America</th>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Africa</th>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          names\n",
+       "                        country\n",
+       "region     code                \n",
+       "Asia       CN             China\n",
+       "           VN           Vietnam\n",
+       "Europe     GB    United Kingdom\n",
+       "           RU            Russia\n",
+       "S. America AR         Argentina\n",
+       "           BO           Bolivia\n",
+       "Africa     ZA      South Africa"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # levels указывает, на каких уровнях искать названия столбцов\n",
+    "# # параметр axis = 1 говорит о том, что мы имеем дело со столбцами\n",
+    "# countries.xs((\"names\", \"country\"), level=[0, 1], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>Moscow</td>\n",
+       "      <td>144</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AR</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>45</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BO</th>\n",
+       "      <td>Bolivia</td>\n",
+       "      <td>Sucre</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           country       capital  population  area  sea\n",
+       "CN           China       Beijing        1400   9.6    1\n",
+       "VN         Vietnam         Hanoi          97   0.3    1\n",
+       "GB  United Kingdom        London          67   0.2    1\n",
+       "RU          Russia        Moscow         144  17.1    1\n",
+       "AR       Argentina  Buenos Aires          45   2.8    1\n",
+       "BO         Bolivia         Sucre          12   1.1    0\n",
+       "ZA    South Africa      Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # обновим атрибуты index и columns\n",
+    "# countries.index = custom_index\n",
+    "# countries.columns = custom_cols\n",
+    "\n",
+    "# # посмотрим на исходный датафрейм\n",
+    "# countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Beijing'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Метод .at[]\n",
+    "# countries.at[\"CN\", \"capital\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CN     True\n",
+       "VN    False\n",
+       "GB    False\n",
+       "RU    False\n",
+       "AR    False\n",
+       "BO    False\n",
+       "ZA    False\n",
+       "Name: population, dtype: bool"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # создадим логическую маску для стран с населением больше миллиарда человек\n",
+    "# countries.population > 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CN</th>\n",
+       "      <td>China</td>\n",
+       "      <td>Beijing</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   country  capital  population  area  sea\n",
+       "CN   China  Beijing        1400   9.6    1"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # применим логическую маску к исходному датафрейму\n",
+    "# countries[countries.population > 1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           country   capital  population  area  sea\n",
+       "VN         Vietnam     Hanoi          97   0.3    1\n",
+       "GB  United Kingdom    London          67   0.2    1\n",
+       "ZA    South Africa  Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # отфильтруем датафрейм по критериям численности населения и площади\n",
+    "# countries[(countries.population > 50) & (countries.area < 2)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>population</th>\n",
+       "      <th>area</th>\n",
+       "      <th>sea</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>VN</th>\n",
+       "      <td>Vietnam</td>\n",
+       "      <td>Hanoi</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GB</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>London</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZA</th>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Pretoria</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           country   capital  population  area  sea\n",
+       "VN         Vietnam     Hanoi          97   0.3    1\n",
+       "GB  United Kingdom    London          67   0.2    1\n",
+       "ZA    South Africa  Pretoria          59   1.2    1"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # Метод .query() позволяет задавать условие фильтрации «своими словами».\n",
+    "# # например, выберем страны с населением более 50 млн. человек И\n",
+    "# # площадью менее двух млн. кв. километров\n",
+    "# countries.query(\"population > 50 and area < 2\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/makarov/pandas.py b/python/makarov/pandas.py
new file mode 100644
index 00000000..1fd805b5
--- /dev/null
+++ b/python/makarov/pandas.py
@@ -0,0 +1,344 @@
+"""Pandas."""
+
+# +
+# # Способ 2. Подключение к базе данных SQL
+# # импортируем модуль sqlite3 для работы с базой данных SQL
+# import sqlite3 as sql
+
+# # Способ 3. Создание датафрейма из словаря
+# import numpy as np
+
+# # создадим пустой словарь
+# # Создание датафрейма
+# # Способ 1. Создание датафрейма из файла
+# import pandas as pd
+
+# # испортируем файл из папки content и выведем первые три строки
+# csv_zip = pd.read_csv("/content/train.zip")
+# csv_zip.head(3)
+
+# +
+# # импортируем данные в формате Excel, указав номер листа, который хотим использовать
+# excel_data = pd.read_excel("/content/iris.xlsx", sheet_name=0)
+# excel_data.head(3)
+
+# +
+# # передадим соответствующую ссылку в функцию pd.read_html()
+# # в параметре match укажем ключевые слова, которые помогут найти нужную таблицу
+# html_data = pd.read_html(
+#     "https://en.wikipedia.org/wiki/World_population", match="World population"
+# )
+# # мы получили пять результатов
+# len(html_data)
+
+# +
+# # создадим соединение с базой данных chinook
+# conn = sql.connect("/content/chinook.db")
+
+# # выберем все строки из таблицы tracks
+# sql_data = pd.read_sql("SELECT * FROM tracks", conn)  # vs. read_sql_query
+
+# # посмотрим на результат
+# sql_data.head(3)
+
+# +
+# # создадим несколько списков и массивов Numpy с информацией о семи странах мира
+# country = np.array(
+#     [
+#         "China",
+#         "Vietnam",
+#         "United Kingdom",
+#         "Russia",
+#         "Argentina",
+#         "Bolivia",
+#         "South Africa",
+#     ]
+# )
+# capital = ["Beijing", "Hanoi", "London", "Moscow", "Buenos Aires", "Sucre", "Pretoria"]
+# population = [1400, 97, 67, 144, 45, 12, 59]  # млн. человек
+# area = [9.6, 0.3, 0.2, 17.1, 2.8, 1.1, 1.2]  # млн. кв. км.
+# sea = [1] * 5 + [0, 1]  # выход к морю (в этом списке его нет только у Боливии)
+
+# +
+# countries_dict = {}
+# # превратим эти списки в значения словаря,
+# # одновременно снабдив необходимыми ключами
+# countries_dict["country"] = country
+# countries_dict["capital"] = capital
+# countries_dict["popilation"] = population
+# countries_dict["area"] = area
+# countries_dict["sea"] = sea
+# countries = pd.DataFrame(countries_dict)
+
+# +
+# # Способ 4. Создание датафрейма из 2D массива Numpy
+# # внешнее измерение будет столбцами, внутренее - строками
+# arr = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
+
+# pd.DataFrame(arr)
+
+# +
+# countries.columns
+
+# +
+# countries.index
+
+# +
+# countries.values
+
+# +
+# countries.axes[0]
+
+# +
+# countries.ndim, countries.shape, countries.size
+
+# +
+# countries.dtypes
+
+# +
+# countries.memory_usage()
+
+# +
+# # Индекс датафрейма
+# # создадим список с кодами стран
+# custom_index = ["CN", "VN", "GB", "RU", "AR", "BO", "ZA"]
+
+# countries = pd.DataFrame(countries_dict, index=custom_index)
+# countries
+
+# +
+# # при этом параметр inplace = True сделает изменения постоянными
+# countries.reset_index(inplace=True)
+# countries
+
+# +
+# # передадим методу название столбца, который хотим сделать индексом
+# countries.set_index("index", inplace=True)
+# countries
+
+# +
+# countries.reset_index(drop=True, inplace=True)
+# countries
+
+# +
+# countries.index = custom_index
+# countries
+
+# +
+# # Многоуровневый индекс
+# # создадим список из кортежей с названием континента и кодом страны
+# rows = [
+#     ("Asia", "CN"),
+#     ("Asia", "VN"),
+#     ("Europe", "GB"),
+#     ("Europe", "RU"),
+#     ("S. America", "AR"),
+#     ("S. America", "BO"),
+#     ("Africa", "ZA"),
+# ]
+
+# +
+# # в столбцах название страны и столицы мы объединим в категорию names
+# # а размер населения, площадь и выход к морю в data
+# cols = [
+#     ("names", "country"),
+#     ("names", "capital"),
+#     ("data", "population"),
+#     ("data", "area"),
+#     ("data", "sea"),
+# ]
+
+# +
+# # создадим многоуровневый индекс для строк
+# # индексам присвоим названия через names = ['region', 'code']
+# custom_multindex = pd.MultiIndex.from_tuples(rows, names=["region", "code"])
+
+# # сделаем то же самое для столбцов
+# custom_multicols = pd.MultiIndex.from_tuples(cols)
+
+# +
+# countries.index = custom_multindex
+# countries.columns = custom_multicols
+
+# countries
+
+# +
+# # вернемся к обычному индексу и названиям столбцов
+# custom_cols = ["country", "capital", "population", "area", "sea"]
+
+# countries.index = custom_index
+# countries.columns = custom_cols
+
+# countries
+
+# +
+# # Преобразование в другие форматы
+# print(countries.to_dict())
+
+# +
+# countries.to_numpy()
+
+# +
+# # Создание Series
+# country_list = [
+#     "China",
+#     "South Africa",
+#     "United Kingdom",
+#     "Russia",
+#     "Argentina",
+#     "Vietnam",
+#     "Australia",
+# ]
+
+# +
+# country_series = pd.Series(country_list)
+# country_series
+
+# +
+# # Создание Series из словаря
+# country_dict = {
+#     "CN": "China",
+#     "ZA": "South Africa",
+#     "GB": "United Kingdom",
+#     "RU": "Russia",
+#     "AR": "Argentina",
+#     "VN": "Vietnam",
+#     "AU": "Australia",
+# }
+
+# country_Series = pd.Series(country_dict)
+# country_Series
+
+# +
+# # Доступ к строкам, столбцам и элементам
+# for column in countries:
+#     print(column)
+
+# +
+# # прервем цикл после первой итерации с помощью break
+# for index, row in countries.iterrows():
+#     print(index)
+#     print(row)
+
+# +
+# for _, row in countries.iterrows():
+#     # например, сформируем вот такое предложение
+#     print(row["capital"] + " is the capital of " + row["country"])
+#     break
+
+# +
+# # Доступ к столбцам
+
+# # выведем столбец capital датафрейма countries
+# countries["capital"]
+
+# +
+# # однако в этом случае название не должно содержать пробелов
+# countries.capital
+
+# +
+# countries[["capital", "area"]]
+
+# +
+# countries.filter(items=["capital", "population"])
+
+# +
+# # Доступ к строкам
+# # выведем строки со второй по пятую (не включительно)
+# countries[1:5]
+
+# +
+# # Методы .loc[] и .iloc[]
+# # для этого передадим методу .loc[] два списка:
+# # с индексами строк и названиями столбцов
+# countries.loc[["CN", "RU", "VN"], ["capital", "population", "area"]]
+
+# +
+# # например, выведем все строки датафрейма
+# countries.loc[:, ["capital", "population", "area"]]
+
+# +
+# # Метод .loc[] также поддерживает значения Boolean.
+# # например, выведем все строки и только последний столбец,
+# # передав список соответствующих логических значений
+# countries.loc[:, [False, False, False, False, True]]
+
+# +
+# позволяет узнать порядковый номер
+# (начиная с нуля) строки или столбца
+# по их индексу и названию соответственно.
+# # выведем номер строки с индексом RU
+# countries.index.get_loc("RU")
+
+# +
+# # теперь в списки мы передаем номера строк и столбцов,
+# # нумерация начинается с нуля
+# countries.iloc[[0, 3, 5], [0, 1, 2]]
+
+# +
+# countries.iloc[:3, -2:]
+
+# +
+# countries[["population", "area"]].iloc[[0, 3]]
+
+# +
+# # Многоуровневый индекс и методы .loc[] и .iloc[]
+# # вновь создадим датафрейм с многоуровневым индексом по строкам и столбцам
+# countries.index = custom_multindex
+# countries.columns = custom_multicols
+
+# countries
+
+# +
+# countries.loc["Asia", "CN"]
+
+# +
+# # выведем первую строку и столбцы с числовыми данными
+# countries.loc[
+#     ("Asia", "CN"), [("data", "population"), ("data", "area"), ("data", "sea")]
+# ]
+
+# +
+# # например, выведем только азиатские страны
+# countries.loc[("Asia", ["CN", "VN"]), :]
+
+# +
+# # Метод .xs()
+# # выберем Европу из уровня region
+# # axis = 0 указывает, что мы берем строки
+# countries.xs("Europe", level="region", axis=0)
+
+# +
+# # levels указывает, на каких уровнях искать названия столбцов
+# # параметр axis = 1 говорит о том, что мы имеем дело со столбцами
+# countries.xs(("names", "country"), level=[0, 1], axis=1)
+
+# +
+# # обновим атрибуты index и columns
+# countries.index = custom_index
+# countries.columns = custom_cols
+
+# # посмотрим на исходный датафрейм
+# countries
+
+# +
+# # Метод .at[]
+# countries.at["CN", "capital"]
+
+# +
+# # создадим логическую маску для стран с населением больше миллиарда человек
+# countries.population > 1000
+
+# +
+# # применим логическую маску к исходному датафрейму
+# countries[countries.population > 1000]
+
+# +
+# # отфильтруем датафрейм по критериям численности населения и площади
+# countries[(countries.population > 50) & (countries.area < 2)]
+
+# +
+# # Метод .query() позволяет задавать условие фильтрации «своими словами».
+# # например, выберем страны с населением более 50 млн. человек И
+# # площадью менее двух млн. кв. километров
+# countries.query("population > 50 and area < 2")
diff --git a/python/oop.ipynb b/python/oop.ipynb
new file mode 100644
index 00000000..43d5040b
--- /dev/null
+++ b/python/oop.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"OOP.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ivan\n",
+      "Person\n",
+      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'name']\n",
+      "<class 'type'>\n",
+      "<class '__main__.Person'>\n",
+      "Person\n",
+      "<class '__main__.Person'>\n",
+      "4529167568\n",
+      "4529167504\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Person:\n",
+    "    \"\"\"A class that represents a person with a specified name.\"\"\"\n",
+    "\n",
+    "    name = \"Ivan\"\n",
+    "\n",
+    "\n",
+    "print(Person.name)\n",
+    "print(Person.__name__)\n",
+    "print(dir(Person))\n",
+    "print(Person.__class__)\n",
+    "\n",
+    "p_obj_1 = Person()\n",
+    "print(p_obj_1.__class__)\n",
+    "print(p_obj_1.__class__.__name__)\n",
+    "print(type(p_obj_1))\n",
+    "p_obj_2 = type(p_obj_1)()\n",
+    "print(id(p_obj_1))\n",
+    "print(id(p_obj_2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'name']\n",
+      "{'__module__': '__main__', '__doc__': 'A class that represents a person and stores their name.', 'name': 'Ivan', '__dict__': <attribute '__dict__' of 'Person2' objects>, '__weakref__': <attribute '__weakref__' of 'Person2' objects>}\n",
+      "Ivan\n",
+      "{'__module__': '__main__', '__doc__': 'A class that represents a person and stores their name.', 'name': 'Ivan', '__dict__': <attribute '__dict__' of 'Person2' objects>, '__weakref__': <attribute '__weakref__' of 'Person2' objects>, 'dob': '123'}\n",
+      "{'__module__': '__main__', '__doc__': 'A class that represents a person and stores their name.', 'name': 'Ivan', '__dict__': <attribute '__dict__' of 'Person2' objects>, '__weakref__': <attribute '__weakref__' of 'Person2' objects>}\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Person2:\n",
+    "    \"\"\"A class that represents a person and stores their name.\"\"\"\n",
+    "\n",
+    "    name = \"Ivan\"\n",
+    "\n",
+    "\n",
+    "print(dir(Person2))\n",
+    "print(Person2.__dict__)\n",
+    "\n",
+    "\n",
+    "# Person.__dict__['name'] = 'asdfsdf'  # Error\n",
+    "print(Person.name)\n",
+    "\n",
+    "# Person2.age = 234324\n",
+    "# print(Person2.__dict__)\n",
+    "\n",
+    "\n",
+    "getattr(Person2, \"name\")\n",
+    "setattr(Person2, \"dob\", \"123\")\n",
+    "print(Person2.__dict__)\n",
+    "delattr(Person2, \"dob\")\n",
+    "print(Person2.__dict__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'__module__': '__main__', '__doc__': 'A class that represents a person with a name and a method to greet.', 'name': 'Ivan', 'hello': <function Person3.hello at 0x10df1f100>, '__dict__': <attribute '__dict__' of 'Person3' objects>, '__weakref__': <attribute '__weakref__' of 'Person3' objects>}\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Person3:\n",
+    "    \"\"\"A class that represents a person with a name and a method to greet.\"\"\"\n",
+    "\n",
+    "    name = \"Ivan\"\n",
+    "\n",
+    "    def hello(self: \"Person3\") -> None:\n",
+    "        \"\"\"Print a greeting message.\"\"\"\n",
+    "        print(\"Hello\")\n",
+    "\n",
+    "\n",
+    "print(Person3.__dict__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'__module__': '__main__', '__annotations__': {'name': <class 'str'>}, '__doc__': 'A class that represents a person and stores their name.', 'name': 'Ivan', '__dict__': <attribute '__dict__' of 'Person4' objects>, '__weakref__': <attribute '__weakref__' of 'Person4' objects>}\n",
+      "False\n",
+      "Ivan\n",
+      "Ivan\n",
+      "4529167088\n",
+      "4529167088\n",
+      "4529167088\n",
+      "{}\n",
+      "{}\n",
+      "{'__module__': '__main__', '__annotations__': {'name': <class 'str'>}, '__doc__': 'A class that represents a person and stores their name.', 'name': 'Ivan', '__dict__': <attribute '__dict__' of 'Person4' objects>, '__weakref__': <attribute '__weakref__' of 'Person4' objects>}\n",
+      "Ivan\n",
+      "Ivan\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Person4:\n",
+    "    \"\"\"A class that represents a person and stores their name.\"\"\"\n",
+    "\n",
+    "    name: str = \"Ivan\"\n",
+    "\n",
+    "\n",
+    "print(Person4.__dict__)\n",
+    "\n",
+    "p1 = Person4()\n",
+    "p2 = Person4()\n",
+    "\n",
+    "print(id(p1) == id(p2))\n",
+    "\n",
+    "print(p1.name)\n",
+    "print(p2.name)\n",
+    "\n",
+    "print(id(p1.name))\n",
+    "print(id(p2.name))\n",
+    "print(id(Person4.name))\n",
+    "\n",
+    "print(p1.__dict__)\n",
+    "print(p2.__dict__)\n",
+    "print(Person4.__dict__)\n",
+    "\n",
+    "p1.name = \"Oleg\"\n",
+    "\n",
+    "p2.name = \"Dima\"\n",
+    "# p2.age = 123\n",
+    "\n",
+    "p1 = Person4()\n",
+    "p2 = Person4()\n",
+    "Person.name = \"asdfsdf\"\n",
+    "\n",
+    "print(p1.name)\n",
+    "print(p2.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<function Person5.hello at 0x10df1f2e0>\n",
+      "0x10df65cd0\n",
+      "Hello\n",
+      "<class 'function'>\n",
+      "<class 'method'>\n",
+      "4528927456\n",
+      "4529216576\n",
+      "Hello\n",
+      "0x10df65cd0\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Person5:\n",
+    "    \"\"\"A class that represents a person with a method to greet.\"\"\"\n",
+    "\n",
+    "    def hello(self: \"Person5\") -> None:\n",
+    "        \"\"\"Print a greeting message.\"\"\"\n",
+    "        print(\"Hello\")\n",
+    "\n",
+    "\n",
+    "print(Person5.hello)\n",
+    "\n",
+    "\n",
+    "p3 = Person5()\n",
+    "print(hex(id(p3)))\n",
+    "\n",
+    "p3.hello()\n",
+    "\n",
+    "print(type(Person5.hello))\n",
+    "print(type(p3.hello))\n",
+    "\n",
+    "print(id(Person5.hello))\n",
+    "print(id(p3.hello))\n",
+    "\n",
+    "dir(Person5.hello)\n",
+    "dir(p3.hello)\n",
+    "\n",
+    "p3.__dict__\n",
+    "Person5.__dict__\n",
+    "\n",
+    "\n",
+    "Person5.hello(p3)\n",
+    "# print(p3.hello.__self__)\n",
+    "print(hex(id(p3)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Person6:\n",
+    "    \"\"\"A class that defines a person with create and display functions.\"\"\"\n",
+    "\n",
+    "    def create(self: \"Person6\") -> None:\n",
+    "        \"\"\"Set the person's name.\"\"\"\n",
+    "        self.name = \"Ivan\"  # pylint: disable=attribute-defined-outside-init\n",
+    "\n",
+    "    def display(self: \"Person6\") -> None:\n",
+    "        \"\"\"Print the person's name.\"\"\"\n",
+    "        print(self.name)\n",
+    "\n",
+    "\n",
+    "p4 = Person6()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ivan\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Person7:\n",
+    "    \"\"\"A class that represents a person and stores their name.\"\"\"\n",
+    "\n",
+    "    def __init__(self: \"Person7\") -> None:\n",
+    "        \"\"\"Set the person's name.\"\"\"\n",
+    "        self.name = \"Ivan\"\n",
+    "\n",
+    "    def display(self: \"Person7\") -> None:\n",
+    "        \"\"\"Print the person's name.\"\"\"\n",
+    "        print(self.name)\n",
+    "\n",
+    "\n",
+    "p5 = Person7()\n",
+    "p5.display()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Goodbye\n",
+      "Goodbye\n",
+      "4529215872\n",
+      "4529222528\n",
+      "4528928256\n",
+      "4528928256\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Person8:\n",
+    "    \"\"\"A class that represents a person with a method to greet.\"\"\"\n",
+    "\n",
+    "    def hello(self: \"Person8\") -> None:\n",
+    "        \"\"\"Print a hello greeting.\"\"\"\n",
+    "        print(\"Hello\")\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def goodbye() -> None:\n",
+    "        \"\"\"Print a goodbye message.\"\"\"\n",
+    "        print(\"Goodbye\")\n",
+    "\n",
+    "\n",
+    "p6 = Person8()\n",
+    "p6.goodbye()\n",
+    "\n",
+    "p7 = Person8()\n",
+    "p7.goodbye()\n",
+    "\n",
+    "print(id(p7.hello))\n",
+    "print(id(p6.hello))\n",
+    "\n",
+    "print(id(p7.goodbye))\n",
+    "print(id(p6.goodbye))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) в чём отличия классов и экземпляров\n",
+    "2) как определить класс\n",
+    "3) как инициализировать экземпляр класса\n",
+    "4) как определить методы класса и методы экземпляра\n",
+    "5) как одни классы наследуются от других"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) Класс - абстрактная логическая структура, описывающая\n",
+    "поведение, характеристики и т.п. Экземпляр - конкретная, \n",
+    "реализация класса.\n",
+    "\n",
+    "2) class 'ИмяКласса' в CamelCase\n",
+    "3) class Dog:\n",
+    "    pass\n",
+    "4) написать в поле класса функции\n",
+    "5) через конструкцию class 'ИмяКласс'(Имя родительского класса) "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/oop.py b/python/oop.py
new file mode 100644
index 00000000..b641c15c
--- /dev/null
+++ b/python/oop.py
@@ -0,0 +1,211 @@
+"""OOP."""
+
+
+# +
+class Person:
+    """A class that represents a person with a specified name."""
+
+    name = "Ivan"
+
+
+print(Person.name)
+print(Person.__name__)
+print(dir(Person))
+print(Person.__class__)
+
+p_obj_1 = Person()
+print(p_obj_1.__class__)
+print(p_obj_1.__class__.__name__)
+print(type(p_obj_1))
+p_obj_2 = type(p_obj_1)()
+print(id(p_obj_1))
+print(id(p_obj_2))
+
+
+# +
+class Person2:
+    """A class that represents a person and stores their name."""
+
+    name = "Ivan"
+
+
+print(dir(Person2))
+print(Person2.__dict__)
+
+
+# Person.__dict__['name'] = 'asdfsdf'  # Error
+print(Person.name)
+
+# Person2.age = 234324
+# print(Person2.__dict__)
+
+
+getattr(Person2, "name")
+setattr(Person2, "dob", "123")
+print(Person2.__dict__)
+delattr(Person2, "dob")
+print(Person2.__dict__)
+
+
+# +
+class Person3:
+    """A class that represents a person with a name and a method to greet."""
+
+    name = "Ivan"
+
+    def hello(self: "Person3") -> None:
+        """Print a greeting message."""
+        print("Hello")
+
+
+print(Person3.__dict__)
+
+
+# +
+class Person4:
+    """A class that represents a person and stores their name."""
+
+    name: str = "Ivan"
+
+
+print(Person4.__dict__)
+
+p1 = Person4()
+p2 = Person4()
+
+print(id(p1) == id(p2))
+
+print(p1.name)
+print(p2.name)
+
+print(id(p1.name))
+print(id(p2.name))
+print(id(Person4.name))
+
+print(p1.__dict__)
+print(p2.__dict__)
+print(Person4.__dict__)
+
+p1.name = "Oleg"
+
+p2.name = "Dima"
+# p2.age = 123
+
+p1 = Person4()
+p2 = Person4()
+Person.name = "asdfsdf"
+
+print(p1.name)
+print(p2.name)
+
+
+# +
+class Person5:
+    """A class that represents a person with a method to greet."""
+
+    def hello(self: "Person5") -> None:
+        """Print a greeting message."""
+        print("Hello")
+
+
+print(Person5.hello)
+
+
+p3 = Person5()
+print(hex(id(p3)))
+
+p3.hello()
+
+print(type(Person5.hello))
+print(type(p3.hello))
+
+print(id(Person5.hello))
+print(id(p3.hello))
+
+dir(Person5.hello)
+dir(p3.hello)
+
+p3.__dict__
+Person5.__dict__
+
+
+Person5.hello(p3)
+# print(p3.hello.__self__)
+print(hex(id(p3)))
+
+
+# +
+class Person6:
+    """A class that defines a person with create and display functions."""
+
+    def create(self: "Person6") -> None:
+        """Set the person's name."""
+        self.name = "Ivan"  # pylint: disable=attribute-defined-outside-init
+
+    def display(self: "Person6") -> None:
+        """Print the person's name."""
+        print(self.name)
+
+
+p4 = Person6()
+
+
+# +
+class Person7:
+    """A class that represents a person and stores their name."""
+
+    def __init__(self: "Person7") -> None:
+        """Set the person's name."""
+        self.name = "Ivan"
+
+    def display(self: "Person7") -> None:
+        """Print the person's name."""
+        print(self.name)
+
+
+p5 = Person7()
+p5.display()
+
+
+# +
+class Person8:
+    """A class that represents a person with a method to greet."""
+
+    def hello(self: "Person8") -> None:
+        """Print a hello greeting."""
+        print("Hello")
+
+    @staticmethod
+    def goodbye() -> None:
+        """Print a goodbye message."""
+        print("Goodbye")
+
+
+p6 = Person8()
+p6.goodbye()
+
+p7 = Person8()
+p7.goodbye()
+
+print(id(p7.hello))
+print(id(p6.hello))
+
+print(id(p7.goodbye))
+print(id(p6.goodbye))
+# -
+
+# 1) в чём отличия классов и экземпляров
+# 2) как определить класс
+# 3) как инициализировать экземпляр класса
+# 4) как определить методы класса и методы экземпляра
+# 5) как одни классы наследуются от других
+
+# 1) Класс - абстрактная логическая структура, описывающая
+# поведение, характеристики и т.п. Экземпляр - конкретная,
+# реализация класса.
+#
+# 2) class 'ИмяКласса' в CamelCase
+# 3) class Dog:
+#     pass
+# 4) написать в поле класса функции
+# 5) через конструкцию class 'ИмяКласс'(Имя родительского класса)
diff --git a/python/venv.ipynb b/python/venv.ipynb
new file mode 100644
index 00000000..4d564f30
--- /dev/null
+++ b/python/venv.ipynb
@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Виртуальное окружение.\"\"\""
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) Что делает команда python -m venv venv?\n",
+    "\n",
+    "Создает виртуальное окружение\n",
+    "\n",
+    "1.1) Что делает каждая команда в списке ниже?\n",
+    "\n",
+    "pip list - список зависимостей, которые у нас установлены\n",
+    "pip freeze > requirements.txt - сохраняет зависимости в файл requirements.txt\n",
+    "pip install -r requirements.txt - устанваливает зависимости из файла\n",
+    "\n",
+    "2) Что делает каждая команда в списке ниже?\n",
+    "\n",
+    "conda env list - показывает созданные conda окружения\n",
+    "conda create -n env_name python=3.5 - создает виртуальное окружение \n",
+    "с версией питона 3.5\n",
+    "conda env update -n env_name -f file.yml - обновляет файл с зависимостями\n",
+    "source activate env_name - активирует виртуальную среду\n",
+    "source deactivate - выключает виртуальную среду\n",
+    "conda clean -a - очищает память\n",
+    "\n",
+    "3) Вставьте скрин вашего терминала, где вы активировали сначала venv, потом conda,\n",
+    " назовите окружение \"SENATOROV\"\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "4) Как установить необходимые пакеты внутрь виртуального окружения для conda/venv?\n",
+    "\n",
+    "Conda: cоздаем окружение -> активиурем его -> устанавливаем пакеты с помощью conda\n",
+    "Venv: cоздаем окружение -> активиурем его -> устанавливаем пакеты с помощью pip\n",
+    "\n",
+    "5) Что делают эти команды?\n",
+    "pip freeze > requirements.txt - сохраняет зависимости,\n",
+    "пакеты в файл requirements.txt\n",
+    "conda env export > environment.yml  - сохраняет зависимости,\n",
+    "пакеты в файл environment.yml\n",
+    "\n",
+    "5.1)  Вставьте скрин, где будет видна папка VENV в вашем репозитории, \n",
+    "а также файлы зависимостей requirements.txt и environment.yml, \n",
+    "файлы должны содержать зависимости\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "6) Что делают эти команды?\n",
+    "pip install -r requirements.txt - устанвливает зависимости из файла requirements.txt\n",
+    "conda env create -f environment.yml - создает виртуальное окружение, с зависимости,\n",
+    "указанным именем\n",
+    "\n",
+    "7) Что делают эти команды?\n",
+    "pip list - показывает установленные зависимости\n",
+    "pip show - показывает информацию о пакетах\n",
+    "conda list - показывает установленные зависимости,  в среде Conda\n",
+    "\n",
+    "8) Где по умолчанию больше пакетов venv/pip или conda? \n",
+    "и почему дата сайнинисты используют conda?\n",
+    "\n",
+    "В pip больше пакетов из-за PyPI, дата сайнтисты используют conda из-за того, \n",
+    "что в ней находятся основные библиотеки для DS/DL(numpy, pandas, \n",
+    "keras,matplotlib и т.п.)\n",
+    "\n",
+    "9) Вставьте скрин где будет видно, \n",
+    "Выбор интерпретатора Python (conda) в VS Code/cursor\n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "\n",
+    "10) Добавьте в .gitignore папку SENATOROV\n",
+    "\n",
+    "SENATOROV/, добавил\n",
+    "\n",
+    "11) Зачем нужно виртуальное окружение?\n",
+    "\n",
+    "Виртуальное окружение нужно, чтобы мы могли без конфликтов \n",
+    "устанавливать разные библиотеки/зависимости\n",
+    "\n",
+    "12) С этого момента надо работать в виртуальном окружении conda, \n",
+    "ты научился(-ась) выгружать зависимости и работать с окружением?\n",
+    "\n",
+    "Да"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/venv.py b/python/venv.py
new file mode 100644
index 00000000..942d6dec
--- /dev/null
+++ b/python/venv.py
@@ -0,0 +1,83 @@
+"""Виртуальное окружение."""
+
+# 1) Что делает команда python -m venv venv?
+#
+# Создает виртуальное окружение
+#
+# 1.1) Что делает каждая команда в списке ниже?
+#
+# pip list - список зависимостей, которые у нас установлены
+# pip freeze > requirements.txt - сохраняет зависимости в файл requirements.txt
+# pip install -r requirements.txt - устанваливает зависимости из файла
+#
+# 2) Что делает каждая команда в списке ниже?
+#
+# conda env list - показывает созданные conda окружения
+# conda create -n env_name python=3.5 - создает виртуальное окружение
+# с версией питона 3.5
+# conda env update -n env_name -f file.yml - обновляет файл с зависимостями
+# source activate env_name - активирует виртуальную среду
+# source deactivate - выключает виртуальную среду
+# conda clean -a - очищает память
+#
+# 3) Вставьте скрин вашего терминала, где вы активировали сначала venv, потом conda,
+#  назовите окружение "SENATOROV"
+#
+# ![image.png](attachment:image.png)
+#
+# 4) Как установить необходимые пакеты внутрь виртуального окружения для conda/venv?
+#
+# Conda: cоздаем окружение -> активиурем его -> устанавливаем пакеты с помощью conda
+# Venv: cоздаем окружение -> активиурем его -> устанавливаем пакеты с помощью pip
+#
+# 5) Что делают эти команды?
+# pip freeze > requirements.txt - сохраняет зависимости,
+# пакеты в файл requirements.txt
+# conda env export > environment.yml  - сохраняет зависимости,
+# пакеты в файл environment.yml
+#
+# 5.1)  Вставьте скрин, где будет видна папка VENV в вашем репозитории,
+# а также файлы зависимостей requirements.txt и environment.yml,
+# файлы должны содержать зависимости
+#
+# ![image.png](attachment:image.png)
+#
+# 6) Что делают эти команды?
+# pip install -r requirements.txt - устанвливает зависимости из файла requirements.txt
+# conda env create -f environment.yml - создает виртуальное окружение, с зависимости,
+# указанным именем
+#
+# 7) Что делают эти команды?
+# pip list - показывает установленные зависимости
+# pip show - показывает информацию о пакетах
+# conda list - показывает установленные зависимости,  в среде Conda
+#
+# 8) Где по умолчанию больше пакетов venv/pip или conda?
+# и почему дата сайнинисты используют conda?
+#
+# В pip больше пакетов из-за PyPI, дата сайнтисты используют conda из-за того,
+# что в ней находятся основные библиотеки для DS/DL(numpy, pandas,
+# keras,matplotlib и т.п.)
+#
+# 9) Вставьте скрин где будет видно,
+# Выбор интерпретатора Python (conda) в VS Code/cursor
+#
+# ![image-2.png](attachment:image-2.png)
+#
+# 10) Добавьте в .gitignore папку SENATOROV
+#
+# SENATOROV/, добавил
+#
+# 11) Зачем нужно виртуальное окружение?
+#
+# Виртуальное окружение нужно, чтобы мы могли без конфликтов
+# устанавливать разные библиотеки/зависимости
+#
+# 12) С этого момента надо работать в виртуальном окружении conda,
+# ты научился(-ась) выгружать зависимости и работать с окружением?
+#
+# Да
+
+# 13) Удалите папку VENV, она больше не нужна, мы же не разрабы, нам нужна только conda
+#
+# Удалил
diff --git a/quiz.ipynb b/quiz.ipynb
new file mode 100644
index 00000000..7c0c7136
--- /dev/null
+++ b/quiz.ipynb
@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36cfc750",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Ответы на квизы.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26c1e2bc",
+   "metadata": {},
+   "source": [
+    "11/08/25     \n",
+    "\n",
+    "Список вопросов к видео https://youtu.be/hW_7hodGxVU?si=tCEVs00xGt2q61eW (АЛГОРИТМ ПРИНЯТИЯ И ОТДАЧИ ДОМАШКИ):\n",
+    "       \n",
+    "(По желанию) В ответе подробно всё опишите и обязательно нужно указывать тайм код из видео где я это сказал, по желанию, дополнительно прикладываем скриншот из видео.\n",
+    "Если вы знаете ответы на вопросы из Вашего опыта, то таймкоды из видео не надо указывать и т.д.\n",
+    "\n",
+    "1)Как понять , что домашка пришла?\n",
+    "2) Как принять  домашку?\n",
+    "3) Зачем нужна кнопка history и какие функции появляются при нажатии правой кнопки мыши на коммит?\n",
+    "3.1) Где брать ссылку на коммит? куда её отправлять? \n",
+    "4) Что такое файл лога? \n",
+    "4.1) Когда  нужно его пушить?\n",
+    "5) Что такое интерпритатор? \n",
+    "6) Где можно выбрать интерпритатор?\n",
+    "7) Что такое модуль ? \n",
+    "8) Как создать и отправить коммит?\n",
+    "9) Как посмотреть что коммит точно отправлен и находится в github?\n",
+    "10)Какая команда показывает что код не прошёл проверки на ошибки? \n",
+    "10.1) Напишите список линтеров которые используются для проверки кода и дайте их краткую характеристику.\n",
+    "11) Как узнать какой именно линтер не прошёл проверку?\n",
+    "12) Линтер Pylint видит markdown?\n",
+    "13) Номер ячейки в терминале и номер ячейки в vs code может отличаться? в каком случае?\n",
+    "14) Где посмотреть номер ячейки в vscode?\n",
+    "15) В каком формате ipynb отправляется в гитхаб? причём здесь JSON?\n",
+    "16) Где посмотреть в какой ячейке ошибка?\n",
+    "17) Как запустить терминал ?\n",
+    "18) Что такое линтер ?\n",
+    "19) В какой сайт нужно вставлять код ошибки если ошибка связана с pylint?\n",
+    "20) Секция pydocstyle в большинстве случае автоматический закрывается после исправления ошибок в каком линтере?\n",
+    "21) Что такое описание модуля? Оно должно отражать информацию о том что находится в модуле?\n",
+    "21) С какой  git команды начинается утро программиста?\n",
+    "22) После внесения изменений в файлах, кнопка open in vs code пропадает в кошке, как по другому открыть vs code из кошки?\n",
+    "23) Что такое stash?  \n",
+    "  Общее объяснение концепции.\n",
+    "23.1) Как сохранить стэш?\n",
+    "  git командa(подсказка: https://t.me/c/1937296927/3602/19531): \n",
+    "\n",
+    "  Кнопка в vs code:\n",
+    "\n",
+    "23.2) Как восстановить стэш(подсказка: https://t.me/c/1937296927/3602/25747)?:\n",
+    "\n",
+    "      git команда(подсказка: https://t.me/c/1937296927/3602/19531)?:\n",
+    "\n",
+    "23.3) Различие между стэшем и коммитом.  \n",
+    "  Когда лучше сохранить изменения в стэше, а когда коммитить.\n",
+    "23.4) Как просмотреть список сохраненных стэшей?  \n",
+    "  git команда (подсказка: https://t.me/c/1937296927/3602/19531):\n",
+    "\n",
+    "23.5) Как удалить стэш?  \n",
+    "  Команды для удаления отдельных стэшей или всех сразу.\n",
+    "  git команда (подсказка: https://t.me/c/1937296927/3602/19531):\n",
+    "\n",
+    "23.6) Практические примеры использования стэша.  \n",
+    "  Краткие сценарии, где стэш помогает\n",
+    "\n",
+    "24) Где посмотреть что есть конфликт в файлах? \n",
+    "24.1) Когда он появляется?\n",
+    "25) Как решить конфликт в файлах?\n",
+    "26) Напишиие правильное утверждение\n",
+    "-Зелёное то что пришло с гитхаба и синее локальные изменения или синее то что пришло с гитхаба и зелёное это локальные изменения \n",
+    "27) Если мы работаем в одном файле, можно ли принять pull после того как вы спрячете в стэш свои изменения? \n",
+    "27.1) Что может произойти когда stash восстановите после принятия pull?\n",
+    "28) Сколько способов решения конфликтов было показано в видео? Напишите ЧИСЛО и укажите их способы.\n",
+    "29) Что делает кнопка complete merge?\n",
+    "30) В какой чат нужно писать если остались вопросы?\n",
+    "31) Что такое FORK? Зачем его делают? \n",
+    "32) Как скачать форкнутый репозиторий на локальный компьютер?\n",
+    "33) С какой вероятностью ваши ошибки были уже решены? и кто их решил?\n",
+    "34) Как создать файл в vs code?\n",
+    "35) Файл лога нужно заполнять в конце каждого урока?\n",
+    "==================\n",
+    "\n",
+    "Дополнительные вопросы:\n",
+    "1)Какая команда конвертирует файл в py из ipynb? \n",
+    "подсказка https://t.me/c/1937296927/1/26527 \n",
+    "2) Что такое пакетный менеджер? Вы пользуетесь пакетным менеджером conda или pip? Какой лучше использовать для дата сайнс?\n",
+    "3) Почему расширение py лучше чем ipynb?\n",
+    "4)Что такое pep8? \n",
+    "подсказка:https://peps.python.org/pep-0008/\n",
+    "4.1) линтеры проверяют на соблюдение pep8?\n",
+    "4.2) Какая нотация используется для создания переменных? \n",
+    "ответ на 85-95 страницы https://t.me/c/1937296927/1/16676\n",
+    "4.3) Может ли переменная состоять из одной буквы например андерскор  \"_\" ?\n",
+    "4.4) Зачем и где мы используем андерскор _ \n",
+    "4.5) По PEP8 допустима переменная в одну букву?\n",
+    "ответ на 85-95 страницы https://t.me/c/1937296927/1/16676  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "372df015",
+   "metadata": {},
+   "source": [
+    "1) 0:11 - 0:18 В чате Homework сообщение с ником и написано прими пул\n",
+    "2) 0:20 - 0:47  Зайти в Github desktop, нажать на кнопку рядоом с веткой main, заходим в history и самый первый коммит\n",
+    "3) 0:35 - 0:40 В этой кнопке можно посмотреть историю коммитов, может есть что-то интересное\n",
+    "3.1) Ссылку брать из репозитория на вкалдке Commits, и скопироватьь URL нужного коммита \n",
+    "4) 1:56 - 2:10 Файл лога нужен для логирования уроков , записывать туда что сделал и т.п.\n",
+    "4.1) В конце каждого урока\n",
+    "5) 2:46 - 2:51 программа которая читает и запускает код\n",
+    "6) 2:43  -  2:46 Нажимаем на запуск ячейки, потом в  окне выбираем интерпретатор(или над ячейкой можно нажать и откроется такое же окно с выбором)\n",
+    "7) Модуль - это инструменты для решения задач по DS\n",
+    "8) 3:12 - 3:40 Заходим в github desktop, во вкалдке changes будут наши файлы в формате json, пишем название коммита и нажимаем commit to main потом  push origin\n",
+    "9) 3:46 - 3:50  В history нужно будет найти наш коммит\n",
+    "10) 5:00 - 5:08  Нужно нажать на commit to main и если ошибки в коде то у нас вылетет окно с ошибкой Error и будет написано failed там где произошла ошибка\n",
+    "10.1) flake8: проверка стиля/ошибок; игнорируется ряд правил, pylint: анализирует качества кода, mypy: статистическая проверка, pydocstyle: проверяет стиль  документации \n",
+    "11) 6:08 - 6:21 Если в терминале на против линтера горит красным Failed\n",
+    "12) Нет , не видит \n",
+    "13) 6:54 - 7:09 Номера в ячейках могут отличаться в том случае, если в vscode ячейка стоит после markdown\n",
+    "14) в Vscode внизу справо\n",
+    "15) 3:20 - 3:25 В формате json так как ipynb отображается в формате json \n",
+    "16) 5:36 - 5:40 В окне  Error  будет укзана ячейка cell_1  \n",
+    "17) 5:54 - 5:59 Сверху есть кнопка Terminal -> New Terminal\n",
+    "18) 6:14 - 6:18 Линтер программ которая проверяет файл на корректность\n",
+    "19) 8:00 - 8:22 На сайте pylint надо вбить ошибку\n",
+    "20) 10:04 - 10:16 Убираются ошибки автоматичсеки когда мы закрыли все ошибки в линтере pylint\n",
+    "21) 9:25 - 9:29 Описание модуля - это какая информация у нас в модуле\n",
+    "22) 14:15 - 14:26 Нажимаем в левом верхем углу на Current repository, нажимае правой кнопкой мыши, и там будет кнопка open in vscode\n",
+    "23) Стэш это буфер куда мы временно прячем файлы, чтобы принять pull\n",
+    "23.1) git stash save \"NAME_STASH\"\n",
+    "23.2) git stash apply \"NUMBER_STASH\"\n",
+    "23.3) Стэш - временное сохранение изменений(буфер) нужен чтобы отложить текущую задачу, коммит - сохранение изменений в репозитории эти изменения становятся частью истории проекта. Коммитить лучше когда завершена какая-то работа и мы хотим внести эти изменения в проект, сохранить изменения в стэше лучше когда нужно переключиться на другую задачу, не измения истроию проекта\n",
+    "23.4) git stash list: show all the stashes\n",
+    "23.5) git stash drop, git stash\n",
+    "23.6) Нужно срочно перерключиться на другую ветку main, чтобы исправить баг. Работа над несколькими задачами паралельно и нужно переключиться с одной на другую, сохранаяя контекст в текущей\n",
+    "24) 16:13 - 16:21 После восстановления файлов напротив имя файла появиться восклицательный знак\n",
+    "24.1) 16:00 - 16:13 Когда мы нажимаем Restore, то гитхаб не понимает какие изменения сохранились те который пришли из интеренета или локально(все это происходит если двое людей работают в одном файле)\n",
+    "25) 16:25 - 17:05 В файле открытом у нас будет текст выделенный либо зеленым, либо синим цветом, решить данный конфилкт можно нажатием на кнопку Accept Current Changes тогда примится текст выделенный зеленым(то что пришло с интернета), можно нажать кнопку Accept Incoming Changes тогда примится локальное изменения на ПК,но изменения с гитхаба(зеленый текст) не примутся, или нажать на кнопку Accept Both Changes тогда примутся все изменения, или руками устранить\n",
+    "26) Зелёное то что пришло с гитхаба и синее локальные изменения или синее то что пришло с гитхаба и зелёное это локальные изменения. Правильно - Зелёное то что пришло с гитхаба и синее локальные изменения\n",
+    "27) Да\n",
+    "28) 4 способа: 1) Нажать на Accept Current Changes 2) Нажать Accept Incoming Changes 3) Нажать на Accept Both Changes 4) Убрать изменения руками\n",
+    "29) 17:40 - 17:42 Эта кнопка подтвержедения того что мы убрали в изменениях, а что оставили\n",
+    "30) 18:30 - 18:32 Нужно писать в чат Help Me\n",
+    "31) 19:02 - 19:16 Нужно чтобы скопировать чужой репозиторий и сделать его личным. Чтобы делать там все что мы хотим\n",
+    "32) 19:25 - 19:54 Нужно зайти в Github desktop, слева нажать на current repository -> Add -> Выбираем нужный репозиторий из списка -> Clone\n",
+    "33) C высокой верятностью ошибки уже решены, их решили либо коллеги, либо преподаватель\n",
+    "34) Слева сверху нажать на кнопку File(Файл) -> Cоздать файл\n",
+    "35) 1:08 - 1:16 Да\n",
+    "==================\n",
+    "\n",
+    "Дополнительные вопросы:\n",
+    "1) jupyter nbconvert --to script (название нашего файла).ipynb\n",
+    "2) Пакетный менеджер - инструмент, который помогает устанавливать или обновлять библиотеки, удалять пакеты. И тем и тем. Для DS лучше использовать conda\n",
+    "3) py лучше чем ipynb из-за того, что  ipynb конвертируется в json в github desktop, а py - обычный файл текстовый, py быстрее чем ipynb\n",
+    "4) Pep8 - это руководство по стилю кода на Питоне\n",
+    "4.1) Да, линетры проверяют соблюдается ли PEP8\n",
+    "4.2) Змеиный регистр - snake_case(UPPER_SNAKE_CASE), верблююжий регистр - camelCase, схема Pasca - PascalCase\n",
+    "4.3) Технически - да, переменная может состоять из одной буквы или \"_\"\n",
+    "4.4) Мы можем его использовать в схеме регистра имен - Змеиный регистр(snake_case, с префиксами(is_have), можем использовать в циклах for, также можем использоват в качестве имени переменной\n",
+    "4.5) Нет, не допустимы, за исключением циклов, когда буква i или j используется в циклах for для перебора или использование x и y для декартовых координат"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40c494a4",
+   "metadata": {},
+   "source": [
+    "15/08/25\n",
+    "\n",
+    "Список вопросов к видео https://youtu.be/Si9MfV8uJ-0?si=JXHe-tsgOEwSTI5E (НАСТРОЙКА VSCODE, перенос строк, линтеры, работа с ячейками):\n",
+    "\n",
+    "(По желанию)В ответе подробно всё опишите и обязательно нужно указывать тайм код из видео где я это сказал, по желанию, дополнительно прикладываем скриншот из видео.\n",
+    "Если вы знаете ответы на вопросы из Вашего опыта, то таймкоды из видео не надо указывать и т.д.\n",
+    "\n",
+    "1. Как включить автосохранение данных в VSCODE?\n",
+    "2. Как настроить перенос строки? \n",
+    "3. Сколько символов по pep8 разрешено на строке?\n",
+    "4. Какие способы переноса строк показаны в видео:\n",
+    "\n",
+    "4.1 Строки с использованием обратного слэша (\\)\n",
+    "\n",
+    "string_continued = \"This is a long string that we want to \" \\\n",
+    "                   \"split across multiple lines.\"\n",
+    "print(string_continued)\n",
+    "\n",
+    "4.2 Тройные кавычки (''' или \"\"\") \n",
+    "\n",
+    "multi_line_string = \"\"\"This is a string that spans\n",
+    "multiple lines. You can write freely\n",
+    "and it will keep the line breaks.\"\"\"\n",
+    "print(multi_line_string)\n",
+    "\n",
+    "4.3 Создание списка строк и объединение с помощью join\n",
+    "\n",
+    "strings = [\n",
+    "    \"This is the first line.\",\n",
+    "    \"This is the second line.\",\n",
+    "    \"This is the third line.\"\n",
+    "]\n",
+    "result = \"\\n\".join(strings)  # Используем перенос строк '\\n'\n",
+    "print(result)\n",
+    "\n",
+    "4.4 Использование круглых скобок для продолжения строки\n",
+    "long_string = (\n",
+    "    \"This is a very long string that I would like to \"\n",
+    "    \"continue on the next line.\"\n",
+    ")\n",
+    "print(long_string)\n",
+    "\n",
+    "4.5 Форматированные строки (f-строки) с использованием скобок\n",
+    "letter_a = 5\n",
+    "letter_b = 6\n",
+    "product_ab = letter_a * letter_b\n",
+    "\n",
+    "message = (\n",
+    "    f\"when {letter_a} is multiplied by {letter_b}, \"\n",
+    "    f\"the result is {product_ab}\"\n",
+    ")\n",
+    "print(message)\n",
+    "\n",
+    "4.6 Сложение строк с помощью +\n",
+    "\n",
+    "string_part1 = \"This is the first part, \"\n",
+    "string_part2 = \"and this is the second part.\"\n",
+    "full_string = string_part1 + string_part2\n",
+    "print(full_string)\n",
+    "\n",
+    "5. Проверка на ошибки c помощью кнопки problems, где она находится?\n",
+    "6. Где в vscode находится клиент гита? как в нём отправить коммит? как принять домашку?\n",
+    "7. Что такое GIT? он локальный? В нём можно посмотреть историю изменений файлов и вернуться к любому коммиту?\n",
+    "8. Как вставить картинку в маркдаун? \n",
+    "9. Где посмотреть длину строки в vs code?\n",
+    "10. Как поменять тип ячейки с питона на маркдаун?\n",
+    "11. Как запустить сразу все ячейки в юпитере?\n",
+    "12. Как изменить размер картинки в юпитере? Нужно для этого знать HTML?\n",
+    "13. Какой хоткей чтобы запустить ячейку с смещением на следующую?\n",
+    "14. Как включить отображение номеров строк в юпитере(Cell line numbers)?\n",
+    "15. Что такое \"Go To\" чем это полезно? Как перейти сразу на ошибочную ячейку?\n",
+    "16. Как очистить вывод ячеек которые уже запущены?\n",
+    "17. Как работать одновременно в нескольких файлах в VSCODE? Что такое SPLIT?\n",
+    "18. Каким сочетанием убирается левый сайдбар?\n",
+    "19. Кнопка два листочка это наши локальные файлы?\n",
+    "20. Какая ошибка появилась в трассировке при запуске всех ячеек DICT или LIST?\n",
+    "21. Вы ознакомились с https://t.me/c/1937296927/832/19307? и ttps://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet?\n",
+    "22. Что такое валидация?\n",
+    "23. Что такое трассировка ошибки?\n",
+    "24. Что значит отвалился интерпритатор?\n",
+    "\n",
+    "Отвечаете на вопросы в вашем редакторе кода.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eb001db",
+   "metadata": {},
+   "source": [
+    "1) 0:10 - 0:13 Слева сверху нажимаем File -> AutoSave\n",
+    "2) 0:15 - 0:42 Слева сверху нажимаем File -> Preferences -> Settings, пишем в строке wrap, справа нажимаем на off и выставляем wordWrapcolumn и ниже ставим 79 символов\n",
+    "3) 0:22 - 0:25 не больше 79 символов на строку\n",
+    "4) В видео показаны способы:\n",
+    "4.6(2:07 - 2:40, 4:10-4:22)\n",
+    "5) 4:32 - 4:50 От кнопки Terminal слева в самом начале\n",
+    "6) 6:20 - 6:46  Слева кнопка,где три кружочка туда надо нажать и там будет кнопка commits, там и можно отдавать коммиты или принимать, в меню где мы отдаем коммиты, чтобы принять д/з нужно нажать на три точки и кнопка pull\n",
+    "7) 7:10 - 7:33 Git - это контроль версий, да он локальный, можно посмотреть историю файлов и вернуться к коммиту любому\n",
+    "8) 7:55 - 8:03 Просто нужно скопировать картинку и вставить в ячейку\n",
+    "9) 1:10 - 1:25 Нужно выделить нужную нам строку и снизу будет показана длина строки(пример: Ln 1, Col 1(72 selected))\n",
+    "10) 7:41 - 7:47 Справо снизу у ячейки нужно нажать на python и в окне выбрать markdown\n",
+    "11) 8:27 - 8:30 Сверху возле кнопки \"+Code\" нужно нажать на кнопку Run All\n",
+    "12) 8:08 - 8:22 Нужно картинку вставить в тег img. Весь HTML - нет, нужно только выучить как работать с тегом img\n",
+    "13) 8:38 - 8:44 Shift+Enter\n",
+    "14) 8:57 - 9:18 В ячейке справо нужно нажать на три точки и нажать на кнопку Show Cell Lines Numbers\n",
+    "15) 9:39 - 9:50 Кнопка чтобы переместитьтся к ячейке, где произошла ошибка. Чтобы перейти на ошибочную ячейку нужно нажать на кнопку Go To\n",
+    "16) 10:42 - 10:51 Нужно сверху нажать на кнопку Clear All Outputs\n",
+    "17) Чтобы работать одновременно в нескольких файлах, нужно нажать справо сверху на кнопку \"Split Editor\". Split - это разделение(возможность) работать в нескольких файлах одновременно\n",
+    "18) Ctlr+B\n",
+    "19) Да\n",
+    "20) 9:24 - 9:30 Появилась ошибка DICT\n",
+    "21) Да, просмотрел и ознакомился\n",
+    "22) 9:50 - 9:54 Проверка на то как написан код\n",
+    "23) 10:00 - 10:08 Визуал ошибки(текст что как неправильно)\n",
+    "24) 10:22 - 10:27 Место, где появилась ошибка\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1e3ef0a",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4862d71d",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/quiz.py b/quiz.py
new file mode 100644
index 00000000..bc8d6413
--- /dev/null
+++ b/quiz.py
@@ -0,0 +1,263 @@
+"""Ответы на квизы."""
+
+# 11/08/25
+#
+# Список вопросов к видео https://youtu.be/hW_7hodGxVU?si=tCEVs00xGt2q61eW (АЛГОРИТМ ПРИНЯТИЯ И ОТДАЧИ ДОМАШКИ):
+#
+# (По желанию) В ответе подробно всё опишите и обязательно нужно указывать тайм код из видео где я это сказал, по желанию, дополнительно прикладываем скриншот из видео.
+# Если вы знаете ответы на вопросы из Вашего опыта, то таймкоды из видео не надо указывать и т.д.
+#
+# 1) Как понять , что домашка пришла?
+# 2) Как принять  домашку?
+# 3) Зачем нужна кнопка history и какие функции появляются при нажатии правой кнопки мыши на коммит?
+# 3.1) Где брать ссылку на коммит? куда её отправлять?
+# 4) Что такое файл лога?
+# 4.1) Когда нужно его пушить?
+# 5) Что такое интерпритатор?
+# 6) Где можно выбрать интерпритатор?
+# 7) Что такое модуль ?
+# 8) Как создать и отправить коммит?
+# 9) Как посмотреть что коммит точно отправлен и находится в github?
+# 10) Какая команда показывает что код не прошёл проверки на ошибки?
+# 10.1) Напишите список линтеров которые используются для проверки кода и дайте их краткую характеристику.
+# 11) Как узнать какой именно линтер не прошёл проверку?
+# 12) Линтер Pylint видит markdown?
+# 13) Номер ячейки в терминале и номер ячейки в vs code может отличаться? в каком случае?
+# 14) Где посмотреть номер ячейки в vscode?
+# 15) В каком формате ipynb отправляется в гитхаб? причём здесь JSON?
+# 16) Где посмотреть в какой ячейке ошибка?
+# 17) Как запустить терминал ?
+# 18) Что такое линтер ?
+# 19) В какой сайт нужно вставлять код ошибки если ошибка связана с pylint?
+# 20) Секция pydocstyle в большинстве случае автоматический закрывается после исправления ошибок в каком линтере?
+# 21) Что такое описание модуля? Оно должно отражать информацию о том что находится в модуле?
+# 21) С какой  git команды начинается утро программиста?
+# 22) После внесения изменений в файлах, кнопка open in vs code пропадает в кошке, как по другому открыть vs code из кошки?
+# 23) Что такое stash?
+#   Общее объяснение концепции.
+# 23.1) Как сохранить стэш?
+#   git командa(подсказка: https://t.me/c/1937296927/3602/19531):
+#
+#   Кнопка в vs code:
+#
+# 23.2) Как восстановить стэш(подсказка: https://t.me/c/1937296927/3602/25747)?:
+#
+#       git команда(подсказка: https://t.me/c/1937296927/3602/19531)?:
+#
+# 23.3) Различие между стэшем и коммитом.
+#   Когда лучше сохранить изменения в стэше, а когда коммитить.
+# 23.4) Как просмотреть список сохраненных стэшей?
+#   git команда (подсказка: https://t.me/c/1937296927/3602/19531):
+#
+# 23.5) Как удалить стэш?
+#   Команды для удаления отдельных стэшей или всех сразу.
+#   git команда (подсказка: https://t.me/c/1937296927/3602/19531):
+#
+# 23.6) Практические примеры использования стэша.
+#   Краткие сценарии, где стэш помогает
+#
+# 24) Где посмотреть что есть конфликт в файлах?
+# 24.1) Когда он появляется?
+# 25) Как решить конфликт в файлах?
+# 26) Напишиие правильное утверждение
+# -Зелёное то что пришло с гитхаба и синее локальные изменения или синее то что пришло с гитхаба и зелёное это локальные изменения
+# 27) Если мы работаем в одном файле, можно ли принять pull после того как вы спрячете в стэш свои изменения?
+# 27.1) Что может произойти когда stash восстановите после принятия pull?
+# 28) Сколько способов решения конфликтов было показано в видео? Напишите ЧИСЛО и укажите их способы.
+# 29) Что делает кнопка complete merge?
+# 30) В какой чат нужно писать если остались вопросы?
+# 31) Что такое FORK? Зачем его делают?
+# 32) Как скачать форкнутый репозиторий на локальный компьютер?
+# 33) С какой вероятностью ваши ошибки были уже решены? и кто их решил?
+# 34) Как создать файл в vs code?
+# 35) Файл лога нужно заполнять в конце каждого урока?
+# ==================
+#
+# Дополнительные вопросы:
+# 1)Какая команда конвертирует файл в py из ipynb?
+# подсказка https://t.me/c/1937296927/1/26527
+# 2) Что такое пакетный менеджер? Вы пользуетесь пакетным менеджером conda или pip? Какой лучше использовать для дата сайнс?
+# 3) Почему расширение py лучше чем ipynb?
+# 4) Что такое pep8?
+# подсказка:https://peps.python.org/pep-0008/
+# 4.1) линтеры проверяют на соблюдение pep8?
+# 4.2) Какая нотация используется для создания переменных?
+# ответ на 85-95 страницы https://t.me/c/1937296927/1/16676
+# 4.3) Может ли переменная состоять из одной буквы например андерскор  "_" ?
+# 4.4) Зачем и где мы используем андерскор _
+# 4.5) По PEP8 допустима переменная в одну букву?
+# ответ на 85-95 страницы https://t.me/c/1937296927/1/16676
+#
+
+# 1) 0:11 - 0:18 В чате Homework сообщение с ником и написано прими пул
+# 2) 0:20 - 0:47  Зайти в Github desktop, нажать на кнопку рядоом с веткой main, заходим в history и самый первый коммит
+# 3) 0:35 - 0:40 В этой кнопке можно посмотреть историю коммитов, может есть что-то интересное
+# 3.1) Ссылку брать из репозитория на вкалдке Commits, и скопироватьь URL нужного коммита
+# 4) 1:56 - 2:10 Файл лога нужен для логирования уроков , записывать туда что сделал и т.п.
+# 4.1) В конце каждого урока
+# 5) 2:46 - 2:51 программа которая читает и запускает код
+# 6) 2:43  -  2:46 Нажимаем на запуск ячейки, потом в  окне выбираем интерпретатор(или над ячейкой можно нажать и откроется такое же окно с выбором)
+# 7) Модуль - это инструменты для решения задач по DS
+# 8) 3:12 - 3:40 Заходим в github desktop, во вкалдке changes будут наши файлы в формате json, пишем название коммита и нажимаем commit to main потом  push origin
+# 9) 3:46 - 3:50  В history нужно будет найти наш коммит
+# 10) 5:00 - 5:08  Нужно нажать на commit to main и если ошибки в коде то у нас вылетет окно с ошибкой Error и будет написано failed там где произошла ошибка
+# 10.1) flake8: проверка стиля/ошибок; игнорируется ряд правил, pylint: анализирует качества кода, mypy: статистическая проверка, pydocstyle: проверяет стиль  документации
+# 11) 6:08 - 6:21 Если в терминале на против линтера горит красным Failed
+# 12) Нет , не видит
+# 13) 6:54 - 7:09 Номера в ячейках могут отличаться в том случае, если в vscode ячейка стоит после markdown
+# 14) в Vscode внизу справо
+# 15) 3:20 - 3:25 В формате json так как ipynb отображается в формате json
+# 16) 5:36 - 5:40 В окне  Error  будет укзана ячейка cell_1
+# 17) 5:54 - 5:59 Сверху есть кнопка Terminal -> New Terminal
+# 18) 6:14 - 6:18 Линтер программ которая проверяет файл на корректность
+# 19) 8:00 - 8:22 На сайте pylint надо вбить ошибку
+# 20) 10:04 - 10:16 Убираются ошибки автоматичсеки когда мы закрыли все ошибки в линтере pylint
+# 21) 9:25 - 9:29 Описание модуля - это какая информация у нас в модуле
+# 22) 14:15 - 14:26 Нажимаем в левом верхем углу на Current repository, нажимае правой кнопкой мыши, и там будет кнопка open in vscode
+# 23) Стэш это буфер куда мы временно прячем файлы, чтобы принять pull
+# 23.1) git stash save "NAME_STASH"
+# 23.2) git stash apply "NUMBER_STASH"
+# 23.3) Стэш - временное сохранение изменений(буфер) нужен чтобы отложить текущую задачу, коммит - сохранение изменений в репозитории эти изменения становятся частью истории проекта. Коммитить лучше когда завершена какая-то работа и мы хотим внести эти изменения в проект, сохранить изменения в стэше лучше когда нужно переключиться на другую задачу, не измения истроию проекта
+# 23.4) git stash list: show all the stashes
+# 23.5) git stash drop, git stash
+# 23.6) Нужно срочно перерключиться на другую ветку main, чтобы исправить баг. Работа над несколькими задачами паралельно и нужно переключиться с одной на другую, сохранаяя контекст в текущей
+# 24) 16:13 - 16:21 После восстановления файлов напротив имя файла появиться восклицательный знак
+# 24.1) 16:00 - 16:13 Когда мы нажимаем Restore, то гитхаб не понимает какие изменения сохранились те который пришли из интеренета или локально(все это происходит если двое людей работают в одном файле)
+# 25) 16:25 - 17:05 В файле открытом у нас будет текст выделенный либо зеленым, либо синим цветом, решить данный конфилкт можно нажатием на кнопку Accept Current Changes тогда примится текст выделенный зеленым(то что пришло с интернета), можно нажать кнопку Accept Incoming Changes тогда примится локальное изменения на ПК,но изменения с гитхаба(зеленый текст) не примутся, или нажать на кнопку Accept Both Changes тогда примутся все изменения, или руками устранить
+# 26) Зелёное то что пришло с гитхаба и синее локальные изменения или синее то что пришло с гитхаба и зелёное это локальные изменения. Правильно - Зелёное то что пришло с гитхаба и синее локальные изменения
+# 27) Да
+# 28) 4 способа: 1) Нажать на Accept Current Changes 2) Нажать Accept Incoming Changes 3) Нажать на Accept Both Changes 4) Убрать изменения руками
+# 29) 17:40 - 17:42 Эта кнопка подтвержедения того что мы убрали в изменениях, а что оставили
+# 30) 18:30 - 18:32 Нужно писать в чат Help Me
+# 31) 19:02 - 19:16 Нужно чтобы скопировать чужой репозиторий и сделать его личным. Чтобы делать там все что мы хотим
+# 32) 19:25 - 19:54 Нужно зайти в Github desktop, слева нажать на current repository -> Add -> Выбираем нужный репозиторий из списка -> Clone
+# 33) C высокой верятностью ошибки уже решены, их решили либо коллеги, либо преподаватель
+# 34) Слева сверху нажать на кнопку File(Файл) -> Cоздать файл
+# 35) 1:08 - 1:16 Да
+# ==================
+#
+# Дополнительные вопросы:
+# 1) jupyter nbconvert --to script (название нашего файла).ipynb
+# 2) Пакетный менеджер - инструмент, который помогает устанавливать или обновлять библиотеки, удалять пакеты. И тем и тем. Для DS лучше использовать conda
+# 3) py лучше чем ipynb из-за того, что  ipynb конвертируется в json в github desktop, а py - обычный файл текстовый, py быстрее чем ipynb
+# 4) Pep8 - это руководство по стилю кода на Питоне
+# 4.1) Да, линетры проверяют соблюдается ли PEP8
+# 4.2) Змеиный регистр - snake_case(UPPER_SNAKE_CASE), верблююжий регистр - camelCase, схема Pasca - PascalCase
+# 4.3) Технически - да, переменная может состоять из одной буквы или "_"
+# 4.4) Мы можем его использовать в схеме регистра имен - Змеиный регистр(snake_case, с префиксами(is_have), можем использовать в циклах for, также можем использоват в качестве имени переменной
+# 4.5) Нет, не допустимы, за исключением циклов, когда буква i или j используется в циклах for для перебора или использование x и y для декартовых координат
+
+# 15/08/25
+#
+# Список вопросов к видео https://youtu.be/Si9MfV8uJ-0?si=JXHe-tsgOEwSTI5E (НАСТРОЙКА VSCODE, перенос строк, линтеры, работа с ячейками):
+#
+# (По желанию)В ответе подробно всё опишите и обязательно нужно указывать тайм код из видео где я это сказал, по желанию, дополнительно прикладываем скриншот из видео.
+# Если вы знаете ответы на вопросы из Вашего опыта, то таймкоды из видео не надо указывать и т.д.
+#
+# 1. Как включить автосохранение данных в VSCODE?
+# 2. Как настроить перенос строки?
+# 3. Сколько символов по pep8 разрешено на строке?
+# 4. Какие способы переноса строк показаны в видео:
+#
+# 4.1 Строки с использованием обратного слэша (\)
+#
+# string_continued = "This is a long string that we want to " \
+#                    "split across multiple lines."
+# print(string_continued)
+#
+# 4.2 Тройные кавычки (''' или """)
+#
+# multi_line_string = """This is a string that spans
+# multiple lines. You can write freely
+# and it will keep the line breaks."""
+# print(multi_line_string)
+#
+# 4.3 Создание списка строк и объединение с помощью join
+#
+# strings = [
+#     "This is the first line.",
+#     "This is the second line.",
+#     "This is the third line."
+# ]
+# result = "\n".join(strings)  # Используем перенос строк '\n'
+# print(result)
+#
+# 4.4 Использование круглых скобок для продолжения строки
+# long_string = (
+#     "This is a very long string that I would like to "
+#     "continue on the next line."
+# )
+# print(long_string)
+#
+# 4.5 Форматированные строки (f-строки) с использованием скобок
+# letter_a = 5
+# letter_b = 6
+# product_ab = letter_a * letter_b
+#
+# message = (
+#     f"when {letter_a} is multiplied by {letter_b}, "
+#     f"the result is {product_ab}"
+# )
+# print(message)
+#
+# 4.6 Сложение строк с помощью +
+#
+# string_part1 = "This is the first part, "
+# string_part2 = "and this is the second part."
+# full_string = string_part1 + string_part2
+# print(full_string)
+#
+# 5. Проверка на ошибки c помощью кнопки problems, где она находится?
+# 6. Где в vscode находится клиент гита? как в нём отправить коммит? как принять домашку?
+# 7. Что такое GIT? он локальный? В нём можно посмотреть историю изменений файлов и вернуться к любому коммиту?
+# 8. Как вставить картинку в маркдаун?
+# 9. Где посмотреть длину строки в vs code?
+# 10. Как поменять тип ячейки с питона на маркдаун?
+# 11. Как запустить сразу все ячейки в юпитере?
+# 12. Как изменить размер картинки в юпитере? Нужно для этого знать HTML?
+# 13. Какой хоткей чтобы запустить ячейку с смещением на следующую?
+# 14. Как включить отображение номеров строк в юпитере(Cell line numbers)?
+# 15. Что такое "Go To" чем это полезно? Как перейти сразу на ошибочную ячейку?
+# 16. Как очистить вывод ячеек которые уже запущены?
+# 17. Как работать одновременно в нескольких файлах в VSCODE? Что такое SPLIT?
+# 18. Каким сочетанием убирается левый сайдбар?
+# 19. Кнопка два листочка это наши локальные файлы?
+# 20. Какая ошибка появилась в трассировке при запуске всех ячеек DICT или LIST?
+# 21. Вы ознакомились с https://t.me/c/1937296927/832/19307? и ttps://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet?
+# 22. Что такое валидация?
+# 23. Что такое трассировка ошибки?
+# 24. Что значит отвалился интерпритатор?
+#
+# Отвечаете на вопросы в вашем редакторе кода.
+#
+
+# 1) 0:10 - 0:13 Слева сверху нажимаем File -> AutoSave
+# 2) 0:15 - 0:42 Слева сверху нажимаем File -> Preferences -> Settings, пишем в строке wrap, справа нажимаем на off и выставляем wordWrapcolumn и ниже ставим 79 символов
+# 3) 0:22 - 0:25 не больше 79 символов на строку
+# 4) В видео показаны способы:
+# 4.6(2:07 - 2:40, 4:10-4:22)
+# 5) 4:32 - 4:50 От кнопки Terminal слева в самом начале
+# 6) 6:20 - 6:46  Слева кнопка,где три кружочка туда надо нажать и там будет кнопка commits, там и можно отдавать коммиты или принимать, в меню где мы отдаем коммиты, чтобы принять д/з нужно нажать на три точки и кнопка pull
+# 7) 7:10 - 7:33 Git - это контроль версий, да он локальный, можно посмотреть историю файлов и вернуться к коммиту любому
+# 8) 7:55 - 8:03 Просто нужно скопировать картинку и вставить в ячейку
+# 9) 1:10 - 1:25 Нужно выделить нужную нам строку и снизу будет показана длина строки(пример: Ln 1, Col 1(72 selected))
+# 10) 7:41 - 7:47 Справо снизу у ячейки нужно нажать на python и в окне выбрать markdown
+# 11) 8:27 - 8:30 Сверху возле кнопки "+Code" нужно нажать на кнопку Run All
+# 12) 8:08 - 8:22 Нужно картинку вставить в тег img. Весь HTML - нет, нужно только выучить как работать с тегом img
+# 13) 8:38 - 8:44 Shift+Enter
+# 14) 8:57 - 9:18 В ячейке справо нужно нажать на три точки и нажать на кнопку Show Cell Lines Numbers
+# 15) 9:39 - 9:50 Кнопка чтобы переместитьтся к ячейке, где произошла ошибка. Чтобы перейти на ошибочную ячейку нужно нажать на кнопку Go To
+# 16) 10:42 - 10:51 Нужно сверху нажать на кнопку Clear All Outputs
+# 17) Чтобы работать одновременно в нескольких файлах, нужно нажать справо сверху на кнопку "Split Editor". Split - это разделение(возможность) работать в нескольких файлах одновременно
+# 18) Ctlr+B
+# 19) Да
+# 20) 9:24 - 9:30 Появилась ошибка DICT
+# 21) Да, просмотрел и ознакомился
+# 22) 9:50 - 9:54 Проверка на то как написан код
+# 23) 10:00 - 10:08 Визуал ошибки(текст что как неправильно)
+# 24) 10:22 - 10:27 Место, где появилась ошибка
+#
+
+#
+
+#