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

@@ -414,7 +414,7 @@
  ],
  "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"
  ]
  },
  {
@@ -473,7 +473,7 @@
  "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)"
  ]
  },
  {
@@ -503,7 +503,7 @@
  ],
  "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]);"
@@ -548,7 +548,7 @@
  ],
  "source": [
  "# Solution\n",
- "sum(np.random.binomial(20,0.3,10000) >= 5)/10000"
+ "sum(np.random.binomial(20, 0.3, 10000) >= 5)/10000"
  ]
  },
  {
@@ -603,7 +603,7 @@
  ],
  "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);"
  ]
  },
@@ -685,7 +685,7 @@
  ],
  "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)"
@@ -709,8 +709,8 @@
  ],
  "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"
@@ -789,8 +789,8 @@
  "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"
  ]
  },
@@ -820,8 +820,8 @@
  "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"
  ]
@@ -983,7 +983,7 @@
  "# 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"
  ]
  },
@@ -994,9 +994,9 @@
  "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",
@@ -362,7 +363,7 @@
  "# 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))"
  ]
  },
  {