PEP8ify your life, get on down.

https://www.youtube.com/watch?v=WKnr9sWivsI

Author: Hugo Bowne-Anderson

notebooks/01-Instructor-Probability_a_simulated_introduction.ipynb

@@ -269,7 +269,7 @@
    "outputs": [],
    "source": [
     "n_samples = 10000\n",
-    "sum(np.random.choice(lengths,n_samples, replace=True) > 10)/n_samples"
+    "sum(np.random.choice(lengths, n_samples, replace=True) > 10)/n_samples"
    ]
   },
   {
@@ -317,7 +317,7 @@
     "np.random.seed(seed=16071982)\n",
     "\n",
     "# Simulate one run of flipping the biased coin 10 times\n",
-    "np.random.binomial(10,0.7)"
+    "np.random.binomial(10, 0.7)"
    ]
   },
   {
@@ -336,7 +336,7 @@
    "outputs": [],
    "source": [
     "# Simulate 1,000 run of flipping the biased coin 10 times\n",
-    "x = np.random.binomial(10,0.3,10000)\n",
+    "x = np.random.binomial(10, 0.3, 10000)\n",
     "\n",
     "# Plot normalized histogram of results\n",
     "plt.hist(x, density=True, bins=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);"
@@ -370,7 +370,7 @@
    "outputs": [],
    "source": [
     "# Solution\n",
-    "sum(np.random.binomial(20,0.3,10000) >= 5)/10000"
+    "sum(np.random.binomial(20, 0.3, 10000) >= 5)/10000"
    ]
   },
   {
@@ -403,7 +403,7 @@
    "outputs": [],
    "source": [
     "# Plot histogram \n",
-    "x = np.random.binomial(10,0.5,10000)\n",
+    "x = np.random.binomial(10, 0.5, 10000)\n",
     "plt.hist(x, density=True);"
    ]
   },
@@ -465,7 +465,7 @@
    "outputs": [],
    "source": [
     "# Solution: Calculate P(A,B)\n",
-    "x_0 = np.random.binomial(2,0.5,10000)\n",
+    "x_0 = np.random.binomial(2, 0.5, 10000)\n",
     "p_ab = sum(x_0==2)/len(x_0)\n",
     "plt.hist(x_0);\n",
     "print(p_ab)"
@@ -478,8 +478,8 @@
    "outputs": [],
    "source": [
     "# Solution: Calculate P(A)P(B)\n",
-    "x_1 = np.random.binomial(1,0.5,10000)\n",
-    "x_2 = np.random.binomial(1,0.5,10000)\n",
+    "x_1 = np.random.binomial(1, 0.5, 10000)\n",
+    "x_2 = np.random.binomial(1, 0.5, 10000)\n",
     "p_a = sum(x_1 == 1)/len(x_1)\n",
     "p_b = sum(x_2 == 1)/len(x_2)\n",
     "p_a*p_b"
@@ -536,8 +536,8 @@
    "source": [
     "# Calculate P(A)P(B) using resampling methods\n",
     "n_samples = 100000\n",
-    "p_a = sum(np.random.choice(lengths,n_samples, replace=True) > 10)/n_samples\n",
-    "p_b = sum(np.random.choice(lengths,n_samples, replace=True) > 10)/n_samples\n",
+    "p_a = sum(np.random.choice(lengths, n_samples, replace=True) > 10)/n_samples\n",
+    "p_b = sum(np.random.choice(lengths, n_samples, replace=True) > 10)/n_samples\n",
     "p_a*p_b"
    ]
   },
@@ -556,8 +556,8 @@
    "source": [
     "# Calculate P(A,B) using resampling methods\n",
     "n_samples = 100000\n",
-    "samples = np.random.choice(lengths,(n_samples,2), replace=True)\n",
-    "_ = samples > (10,10)\n",
+    "samples = np.random.choice(lengths, (n_samples,2), replace=True)\n",
+    "_ = samples > (10, 10)\n",
     "p_ab = sum(np.prod(_, axis=1))/n_samples\n",
     "p_ab"
    ]
@@ -686,7 +686,7 @@
     "# Take 10,000 subjects\n",
     "n = 100000\n",
     "# Sample for number of users, non-users\n",
-    "users = np.random.binomial(n,0.005,1) \n",
+    "users = np.random.binomial(n, 0.005, 1) \n",
     "non_users = n - users"
    ]
   },
@@ -697,9 +697,9 @@
    "outputs": [],
    "source": [
     "# How many of these users tested +ve ?\n",
-    "u_pos = np.random.binomial(users,0.99)\n",
+    "u_pos = np.random.binomial(users, 0.99)\n",
     "# How many of these non-users tested +ve ?\n",
-    "non_pos = np.random.binomial(non_users,0.01)"
+    "non_pos = np.random.binomial(non_users, 0.01)"
    ]
   },
   {

notebooks/02-Instructor-Parameter_estimation_hypothesis_testing.ipynb

@@ -137,8 +137,9 @@
     "    n_successes = np.random.binomial(N, p)\n",
     "    # X-axis for PDF\n",
     "    x = np.linspace(0, 1, 100)\n",
-    "    #prior\n",
+    "    # Prior\n",
     "    prior = 1\n",
+    "    # Posterior\n",
     "    posterior = x**n_successes*(1-x)**(N-n_successes)*prior\n",
     "    posterior /= np.max(posterior)  # so that peak always at 1\n",
     "    plt.plot(x, posterior)\n",
@@ -321,7 +322,7 @@
     "# click-through rates\n",
     "p_a = 0.15\n",
     "N = 150\n",
-    "n_successes_a = np.sum(np.random.binomial(N,p_a))"
+    "n_successes_a = np.sum(np.random.binomial(N, p_a))"
    ]
   },
   {