From b1c246f643c24cec3d67b4ead33667fa202084c2 Mon Sep 17 00:00:00 2001
From: filipapietrat <filipapietrat@gmail.com>
Date: Wed, 22 Nov 2023 13:27:43 +0000
Subject: [PATCH] [lab-sql-9]Filipa T

---
 .../[lab-sql-9]Filipa T-checkpoint.ipynb      | 546 ++++++++++++++++++
 [lab-sql-9]Filipa T.ipynb                     | 546 ++++++++++++++++++
 [lab-sql-9]Filipa T.sql                       |  56 ++
 3 files changed, 1148 insertions(+)
 create mode 100644 .ipynb_checkpoints/[lab-sql-9]Filipa T-checkpoint.ipynb
 create mode 100644 [lab-sql-9]Filipa T.ipynb
 create mode 100644 [lab-sql-9]Filipa T.sql

diff --git a/.ipynb_checkpoints/[lab-sql-9]Filipa T-checkpoint.ipynb b/.ipynb_checkpoints/[lab-sql-9]Filipa T-checkpoint.ipynb
new file mode 100644
index 0000000..07e4688
--- /dev/null
+++ b/.ipynb_checkpoints/[lab-sql-9]Filipa T-checkpoint.ipynb	
@@ -0,0 +1,546 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "3367cdce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "c6575e39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create a Python connection with SQL database and retrieve the results of the last two queries (also mentioned below) as dataframes:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "1264f188",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a503596d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check the number of rentals for each customer for May"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "f5d4568b",
+   "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>customer_id</th>\n",
+       "      <th>rental_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>269</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>239</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>431</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>351</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>136</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rental_count\n",
+       "0            197             8\n",
+       "1            506             7\n",
+       "2            109             7\n",
+       "3            269             6\n",
+       "4            239             6\n",
+       "..           ...           ...\n",
+       "515          431             1\n",
+       "516          351             1\n",
+       "517           10             1\n",
+       "518          136             1\n",
+       "519           61             1\n",
+       "\n",
+       "[520 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+    "engine = create_engine(connection_string)\n",
+    "may_data = pd.read_sql_query('select customer_id, count(rental_id) as rental_count from sakila.rentals_may group by customer_id order by rental_count desc', engine)\n",
+    "may_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "f4242747",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check the number of rentals for each customer for June"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "6981e1a8",
+   "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>customer_id</th>\n",
+       "      <th>rental_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>454</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>329</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>295</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>561</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>425</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>412</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>335</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>126</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>590 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rental_count\n",
+       "0             31            11\n",
+       "1            454            10\n",
+       "2            329             9\n",
+       "3            295             9\n",
+       "4            561             9\n",
+       "..           ...           ...\n",
+       "585          425             1\n",
+       "586          412             1\n",
+       "587          335             1\n",
+       "588           22             1\n",
+       "589          126             1\n",
+       "\n",
+       "[590 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+    "engine = create_engine(connection_string)\n",
+    "june_data = pd.read_sql_query('select customer_id, count(rental_id) as rental_count from sakila.rentals_june group by customer_id order by rental_count desc', engine)\n",
+    "june_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "7e166361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a function that checks if customer borrowed more or less films in the month of June as compared to May."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "6da36a5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "may_data = pd.read_sql_query('select customer_id, count(rental_id) as rental_count from sakila.rentals_may group by customer_id order by rental_count desc', engine)\n",
+    "\n",
+    "# Renamaming the columns\n",
+    "may_data = may_data.rename(columns={'rental_count': 'rentals_may'})\n",
+    "\n",
+    "\n",
+    "june_data = pd.read_sql_query('select customer_id, count(rental_id) as rental_count from sakila.rentals_june group by customer_id order by rental_count desc', engine)\n",
+    "\n",
+    "# Renamaming the columns\n",
+    "june_data = june_data.rename(columns={'rental_count': 'rentals_june'})\n",
+    "\n",
+    "# Merging the dfs\n",
+    "final_data = pd.merge(may_data, june_data, on='customer_id', how='outer')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "fbd5a870",
+   "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>customer_id</th>\n",
+       "      <th>rentals_may</th>\n",
+       "      <th>rentals_june</th>\n",
+       "      <th>result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>It's a tie!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>June Wins!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>June Wins!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>269</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>June Wins!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>239</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>June Wins!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>63</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>598</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>487</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>555</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>335</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>598 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rentals_may  rentals_june       result\n",
+       "0            197          8.0           8.0  It's a tie!\n",
+       "1            506          7.0           5.0   June Wins!\n",
+       "2            109          7.0           5.0   June Wins!\n",
+       "3            269          6.0           3.0   June Wins!\n",
+       "4            239          6.0           5.0   June Wins!\n",
+       "..           ...          ...           ...          ...\n",
+       "593           63          NaN           1.0    nan alert\n",
+       "594          598          NaN           1.0    nan alert\n",
+       "595          487          NaN           1.0    nan alert\n",
+       "596          555          NaN           1.0    nan alert\n",
+       "597          335          NaN           1.0    nan alert\n",
+       "\n",
+       "[598 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for index, row in final_data.iterrows():\n",
+    "    may_rentals = row['rentals_may']\n",
+    "    june_rentals = row['rentals_june']\n",
+    "\n",
+    "    if pd.isna(may_rentals):\n",
+    "        final_data.loc[index, 'result'] = 'nan alert'\n",
+    "    elif may_rentals > june_rentals:\n",
+    "        final_data.loc[index, 'result'] = 'June Wins!'\n",
+    "    elif may_rentals < june_rentals:\n",
+    "        final_data.loc[index, 'result'] = 'May Wins!'\n",
+    "    else:\n",
+    "        final_data.loc[index, 'result'] = 'It\\'s a tie!'\n",
+    "\n",
+    "final_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "fb77a681",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([\"It's a tie!\", 'June Wins!', 'May Wins!', 'nan alert'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_data['result'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e10ad1ac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/[lab-sql-9]Filipa T.ipynb b/[lab-sql-9]Filipa T.ipynb
new file mode 100644
index 0000000..07e4688
--- /dev/null
+++ b/[lab-sql-9]Filipa T.ipynb	
@@ -0,0 +1,546 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "3367cdce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "c6575e39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create a Python connection with SQL database and retrieve the results of the last two queries (also mentioned below) as dataframes:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "1264f188",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a503596d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check the number of rentals for each customer for May"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "f5d4568b",
+   "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>customer_id</th>\n",
+       "      <th>rental_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>269</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>239</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>431</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>351</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>136</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rental_count\n",
+       "0            197             8\n",
+       "1            506             7\n",
+       "2            109             7\n",
+       "3            269             6\n",
+       "4            239             6\n",
+       "..           ...           ...\n",
+       "515          431             1\n",
+       "516          351             1\n",
+       "517           10             1\n",
+       "518          136             1\n",
+       "519           61             1\n",
+       "\n",
+       "[520 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+    "engine = create_engine(connection_string)\n",
+    "may_data = pd.read_sql_query('select customer_id, count(rental_id) as rental_count from sakila.rentals_may group by customer_id order by rental_count desc', engine)\n",
+    "may_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "f4242747",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check the number of rentals for each customer for June"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "6981e1a8",
+   "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>customer_id</th>\n",
+       "      <th>rental_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>454</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>329</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>295</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>561</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>425</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>412</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>335</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>126</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>590 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rental_count\n",
+       "0             31            11\n",
+       "1            454            10\n",
+       "2            329             9\n",
+       "3            295             9\n",
+       "4            561             9\n",
+       "..           ...           ...\n",
+       "585          425             1\n",
+       "586          412             1\n",
+       "587          335             1\n",
+       "588           22             1\n",
+       "589          126             1\n",
+       "\n",
+       "[590 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+    "engine = create_engine(connection_string)\n",
+    "june_data = pd.read_sql_query('select customer_id, count(rental_id) as rental_count from sakila.rentals_june group by customer_id order by rental_count desc', engine)\n",
+    "june_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "7e166361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a function that checks if customer borrowed more or less films in the month of June as compared to May."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "6da36a5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "may_data = pd.read_sql_query('select customer_id, count(rental_id) as rental_count from sakila.rentals_may group by customer_id order by rental_count desc', engine)\n",
+    "\n",
+    "# Renamaming the columns\n",
+    "may_data = may_data.rename(columns={'rental_count': 'rentals_may'})\n",
+    "\n",
+    "\n",
+    "june_data = pd.read_sql_query('select customer_id, count(rental_id) as rental_count from sakila.rentals_june group by customer_id order by rental_count desc', engine)\n",
+    "\n",
+    "# Renamaming the columns\n",
+    "june_data = june_data.rename(columns={'rental_count': 'rentals_june'})\n",
+    "\n",
+    "# Merging the dfs\n",
+    "final_data = pd.merge(may_data, june_data, on='customer_id', how='outer')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "fbd5a870",
+   "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>customer_id</th>\n",
+       "      <th>rentals_may</th>\n",
+       "      <th>rentals_june</th>\n",
+       "      <th>result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>It's a tie!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>June Wins!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>June Wins!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>269</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>June Wins!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>239</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>June Wins!</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>63</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>598</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>487</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>555</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>335</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>nan alert</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>598 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rentals_may  rentals_june       result\n",
+       "0            197          8.0           8.0  It's a tie!\n",
+       "1            506          7.0           5.0   June Wins!\n",
+       "2            109          7.0           5.0   June Wins!\n",
+       "3            269          6.0           3.0   June Wins!\n",
+       "4            239          6.0           5.0   June Wins!\n",
+       "..           ...          ...           ...          ...\n",
+       "593           63          NaN           1.0    nan alert\n",
+       "594          598          NaN           1.0    nan alert\n",
+       "595          487          NaN           1.0    nan alert\n",
+       "596          555          NaN           1.0    nan alert\n",
+       "597          335          NaN           1.0    nan alert\n",
+       "\n",
+       "[598 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for index, row in final_data.iterrows():\n",
+    "    may_rentals = row['rentals_may']\n",
+    "    june_rentals = row['rentals_june']\n",
+    "\n",
+    "    if pd.isna(may_rentals):\n",
+    "        final_data.loc[index, 'result'] = 'nan alert'\n",
+    "    elif may_rentals > june_rentals:\n",
+    "        final_data.loc[index, 'result'] = 'June Wins!'\n",
+    "    elif may_rentals < june_rentals:\n",
+    "        final_data.loc[index, 'result'] = 'May Wins!'\n",
+    "    else:\n",
+    "        final_data.loc[index, 'result'] = 'It\\'s a tie!'\n",
+    "\n",
+    "final_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "fb77a681",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([\"It's a tie!\", 'June Wins!', 'May Wins!', 'nan alert'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_data['result'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e10ad1ac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/[lab-sql-9]Filipa T.sql b/[lab-sql-9]Filipa T.sql
new file mode 100644
index 0000000..0d229d4
--- /dev/null
+++ b/[lab-sql-9]Filipa T.sql	
@@ -0,0 +1,56 @@
+-- In this lab we will find the customers who were active in consecutive months of May and June. Follow the steps to complete the analysis.
+-- Create a table rentals_may to store the data from rental table with information for the month of May.
+-- Insert values in the table rentals_may using the table rental, filtering values only for the month of May.
+
+create table sakila.rentals_may
+select *,
+date_format(rental_date, '%M') as rental_month
+from sakila.rental
+where (date_format(rental_date, '%M') = 'May')
+;
+
+select *
+from sakila.rentals_may
+;
+
+-- Create a table rentals_june to store the data from rental table with information for the month of June.
+-- Insert values in the table rentals_june using the table rental, filtering values only for the month of June.
+
+create table sakila.rentals_june
+select *,
+date_format(rental_date, '%M') as rental_month
+from sakila.rental
+where (date_format(rental_date, '%M') = 'June')
+;
+
+select *
+from sakila.rentals_june
+;
+
+-- Check the number of rentals for each customer for May.
+
+select customer_id, count(rental_id) as rental_count
+from sakila.rentals_may
+group by customer_id
+order by rental_count desc
+;
+
+-- Check the number of rentals for each customer for June.
+
+select customer_id, count(rental_id) as rental_count
+from sakila.rentals_june
+group by customer_id
+order by rental_count desc
+
+;
+-- Create a Python connection with SQL database and retrieve the results of the last two queries (also mentioned below) as dataframes:
+
+-- Check the number of rentals for each customer for May
+
+-- Check the number of rentals for each customer for June
+
+-- Hint: You can store the results from the two queries in two separate dataframes.
+
+-- Write a function that checks if customer borrowed more or less films in the month of June as compared to May.
+
+-- Hint: For this part, you can create a join between the two dataframes created before, using the merge function available for pandas dataframes. Here is a link to the documentation for the merge function.
\ No newline at end of file