Statistical Thinking with Python (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "\n",
    "nohitter_times = pd.read_csv('mlb_nohitters.csv')\n",
    "\n",
    "# Seed random number generator\n",
    "np.random.seed(42)\n",
    "\n",
    "# Compute mean no-hitter time: tau\n",
    "tau = np.mean(nohitter_times['game_number'])\n",
    "\n",
    "# Draw out of an exponential distribution with parameter tau: inter_nohitter_time\n",
    "inter_nohitter_time = np.random.exponential(tau, 100000)\n",
    "\n",
    "# Plot the PDF and label axes\n",
    "_ = plt.hist(inter_nohitter_time,\n",
    "             bins=50, normed=True, histtype='step')\n",
    "_ = plt.xlabel('Games between no-hitters')\n",
    "_ = plt.ylabel('PDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "\n",
    "nohitter_times = pd.read_csv('mlb_nohitters.csv')\n",
    "nohitter_times = nohitter_times['game_number']\n",
    "\n",
    "tau = np.mean(nohitter_times)\n",
    "inter_nohitter_time = np.random.exponential(tau, 100000)\n",
    "\n",
    "def ecdf(data):\n",
    "    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n",
    "    # Number of data points: n\n",
    "    n = len(data)\n",
    "\n",
    "    # x-data for the ECDF: x\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y-data for the ECDF: y\n",
    "    y = np.arange(1, n + 1) / n\n",
    "\n",
    "    return x, y\n",
    "\n",
    "# Create an ECDF from real data: x, y\n",
    "x, y = ecdf(nohitter_times)\n",
    "\n",
    "# Create a CDF from theoretical samples: x_theor, y_theor\n",
    "x_theor, y_theor = ecdf(inter_nohitter_time)\n",
    "\n",
    "# Overlay the plots\n",
    "plt.plot(x_theor, y_theor)\n",
    "plt.plot(x, y, marker='.', color='yellow', linestyle='none')\n",
    "\n",
    "# Margins and axis labels\n",
    "plt.margins(0.02)\n",
    "plt.xlabel('Games between no-hitters')\n",
    "plt.ylabel('CDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#How is this parameter optimal?\n",
    "#Now sample out of an exponential distribution with  being twice as large as the optimal . Do it again for  half as large. Make CDFs of these samples and overlay them with your data. You can see that they do not reproduce the data as well. Thus, the  you computed from the mean inter-no-hitter times is optimal in that it best reproduces the data.\n",
    "\n",
    "# Plot the theoretical CDFs\n",
    "plt.plot(x_theor, y_theor)\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "plt.margins(0.02)\n",
    "plt.xlabel('Games between no-hitters')\n",
    "plt.ylabel('CDF')\n",
    "\n",
    "# Take samples with half tau: samples_half\n",
    "samples_half = np.random.exponential(tau/2, 10000)\n",
    "\n",
    "# Take samples with double tau: samples_double\n",
    "samples_double = np.random.exponential(2*tau, 10000)\n",
    "\n",
    "# Generate CDFs from these samples\n",
    "x_half, y_half = ecdf(samples_half)\n",
    "x_double, y_double = ecdf(samples_double)\n",
    "\n",
    "# Plot these CDFs as lines\n",
    "_ = plt.plot(x_half, y_half)\n",
    "_ = plt.plot(x_double, y_double)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How is this parameter optimal?\n",
    "#Now sample out of an exponential distribution with  being twice as large as the optimal . Do it again for  half as large. Make CDFs of these samples and overlay them with your data. You can see that they do not reproduce the data as well. Thus, the  you computed from the mean inter-no-hitter times is optimal in that it best reproduces the data.\n",
    "\n",
    "# Plot the theoretical CDFs\n",
    "plt.plot(x_theor, y_theor)\n",
    "plt.plot(x, y, marker='.', linestyle='none')\n",
    "plt.margins(0.02)\n",
    "plt.xlabel('Games between no-hitters')\n",
    "plt.ylabel('CDF')\n",
    "\n",
    "# Take samples with half tau: samples_half\n",
    "samples_half = np.random.exponential(tau/2, 10000)\n",
    "\n",
    "# Take samples with double tau: samples_double\n",
    "samples_double = np.random.exponential(2*tau, 10000)\n",
    "\n",
    "# Generate CDFs from these samples\n",
    "x_half, y_half = ecdf(samples_half)\n",
    "x_double, y_double = ecdf(samples_double)\n",
    "\n",
    "# Plot these CDFs as lines\n",
    "_ = plt.plot(x_half, y_half)\n",
    "_ = plt.plot(x_double, y_double)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Country ', 'Continent', 'female literacy', 'fertility', 'population'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.8041324026815345, 5.635092985976559e-38)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import scipy\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "\n",
    "dataset = pd.read_csv('female_literacy_fertility.csv')\n",
    "print(dataset.columns)\n",
    "# Plot the illiteracy rate versus fertility\n",
    "_ = plt.plot(dataset['female literacy'], dataset['fertility'], marker='.', linestyle='none')\n",
    "\n",
    "# Set the margins and label axes\n",
    "plt.margins(0.02)\n",
    "_ = plt.xlabel('percent literate')\n",
    "_ = plt.ylabel('fertility')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Show the Pearson correlation coefficient\n",
    "print(scipy.stats.pearsonr(dataset['female literacy'], dataset['fertility']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope = -0.049798548090634234 children per woman / percent literac\n",
      "intercept = 6.8679054196999765 children per woman\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import scipy\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "dataset = pd.read_csv('female_literacy_fertility.csv')\n",
    "\n",
    "literacy = dataset['female literacy']\n",
    "fertility = dataset['fertility']\n",
    "\n",
    "# Plot the illiteracy rate versus fertility\n",
    "_ = plt.plot(literacy, fertility, marker='.', linestyle='none')\n",
    "plt.margins(0.02)\n",
    "_ = plt.xlabel('percent literate')\n",
    "_ = plt.ylabel('fertility')\n",
    "\n",
    "# Perform a linear regression using np.polyfit(): a, b\n",
    "a, b = np.polyfit(literacy, fertility, 1)\n",
    "\n",
    "# Print the results to the screen\n",
    "print('slope =', a, 'children per woman / percent literac')\n",
    "print('intercept =', b, 'children per woman')\n",
    "\n",
    "# Make theoretical line to plot\n",
    "x = np.array([0, 100])\n",
    "y = a * x + b\n",
    "\n",
    "# Add regression line to your plot\n",
    "_ = plt.plot(x, y)\n",
    "\n",
    "# Draw the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Specify slopes to consider: a_vals\n",
    "a_vals = np.linspace(0, 0.1, 200)\n",
    "\n",
    "# Initialize sum of square of residuals: rss\n",
    "rss = np.empty_like(a_vals)\n",
    "\n",
    "# Compute sum of square of residuals for each value of a_vals\n",
    "for i, a in enumerate(a_vals):\n",
    "    rss[i] = np.sum((fertility - a * literacy - b)**2)\n",
    "\n",
    "# Plot the RSS\n",
    "plt.plot(a_vals, rss, marker='.')\n",
    "plt.xlabel('slope (children per woman / percent literate)')\n",
    "plt.ylabel('sum of square of residuals')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        x\n",
      "1     10.0\n",
      "2      8.0\n",
      "3     13.0\n",
      "4      9.0\n",
      "5     11.0\n",
      "6     14.0\n",
      "7      6.0\n",
      "8      4.0\n",
      "9     12.0\n",
      "10     7.0\n",
      "11     5.0\n",
      "Name: , dtype: object\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"float\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-fd35bf3b4681>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;31m# Perform linear regression: a, b\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;31m# Print the slope and intercept\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\lib\\polynomial.py\u001b[0m in \u001b[0;36mpolyfit\u001b[1;34m(x, y, deg, rcond, full, w, cov)\u001b[0m\n\u001b[0;32m    587\u001b[0m     \"\"\"\n\u001b[0;32m    588\u001b[0m     \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeg\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 589\u001b[1;33m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    590\u001b[0m     \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"float\") to str"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import scipy\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "df = pd.read_csv('anscombe.csv')\n",
    "df.reset_index(drop=True)\n",
    "df.columns = [''] * len(df.columns)\n",
    "df = df.iloc[:, 0:2]\n",
    "x = df.iloc[:, 0]\n",
    "print(x)\n",
    "\n",
    "\n",
    "# Perform linear regression: a, b\n",
    "a, b = np.polyfit(x, y, 1)\n",
    "\n",
    "# Print the slope and intercept\n",
    "print(a, b)\n",
    "\n",
    "# Generate theoretical x and y data: x_theor, y_theor\n",
    "x_theor = np.array([3, 15])\n",
    "y_theor = a * x_theor + b\n",
    "\n",
    "# Plot the Anscombe data and theoretical line\n",
    "_ = plt.plot(x, y, marker='.', linestyle='none')\n",
    "_ = plt.plot(x_theor, y_theor)\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Iterate through x,y pairs\n",
    "for x, y in zip(anscombe_x, anscombe_y):\n",
    "    # Compute the slope and intercept: a, b\n",
    "    a, b = np.polyfit(x, y, 1)\n",
    "\n",
    "    # Print the result\n",
    "    print('slope:', a, 'intercept:', b)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import scipy\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "df = pd.read_csv('sheffield_weather_station.csv', delimiter=';')\n",
    "rain = df['rain']\n",
    "\n",
    "for _ in range(50):\n",
    "    # Generate bootstrap sample: bs_sample\n",
    "    bs_sample = np.random.choice(rain, size=len(rain))\n",
    "\n",
    "    # Compute and plot ECDF from bootstrap sample\n",
    "    x, y = ecdf(bs_sample)\n",
    "    _ = plt.plot(x, y, marker='.', linestyle='none',\n",
    "                 color='red', alpha=0.1)\n",
    "\n",
    "\n",
    "# Compute and plot ECDF from original data\n",
    "x, y = ecdf(rain)\n",
    "_ = plt.plot(x, y, marker='.', color='blue', alpha = .2)\n",
    "\n",
    "# Make margins and label axes\n",
    "plt.margins(0.02)\n",
    "_ = plt.xlabel('yearly rainfall (mm)')\n",
    "_ = plt.ylabel('ECDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9488593574676786\n",
      "0.9455698817507284\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[64.9343789  68.60341292]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% confidence interval = [ 89183.4662415  103967.06972789] games\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import scipy\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "df = pd.read_csv('sheffield_weather_station.csv', delimiter=';')\n",
    "rainfall = df['rain']\n",
    "\n",
    "def bootstrap_replicate_1d(data, func):\n",
    "    \"\"\" Output the mean for a given distribution through a boostrapped sample. \"\"\"\n",
    "    boostrap_sample = np.random.choice(data, len(data))\n",
    "    \n",
    "    return func(boostrap_sample)\n",
    "\n",
    "\n",
    "def draw_bs_reps(data, func, size=1):\n",
    "    \"\"\"Draw bootstrap replicates.\"\"\"\n",
    "\n",
    "    # Initialize array of replicates: bs_replicates\n",
    "    bs_replicates = np.empty(size)\n",
    "\n",
    "    # Generate replicates (not only the estimator)\n",
    "    for i in range(size):\n",
    "        bs_replicates[i] = bootstrap_replicate_1d(data, func)\n",
    "\n",
    "    return bs_replicates\n",
    "\n",
    "\n",
    "# Take 10,000 bootstrap replicates of the mean: bs_replicates\n",
    "bs_replicates = draw_bs_reps(rainfall, np.mean, size = 10000)\n",
    "\n",
    "# Compute and print SEM (standard error of the mean)\n",
    "sem = np.std(rainfall) / np.sqrt(len(rainfall))\n",
    "print(sem)\n",
    "\n",
    "# Compute and print standard deviation of bootstrap replicates\n",
    "bs_std = np.std(bs_replicates)\n",
    "print(bs_std)\n",
    "\n",
    "# Make a histogram of the results\n",
    "_ = plt.hist(bs_replicates, bins=80, normed=True)\n",
    "_ = plt.xlabel('mean annual rainfall (mm)')\n",
    "_ = plt.ylabel('PDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# print 2.5, 97.5 confidence interval boundaries values\n",
    "print(np.percentile(bs_replicates, [2.5, 97.5]))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Replace estimator by the variance\n",
    "# Generate 10,000 bootstrap replicates of the variance: bs_replicates\n",
    "bs_replicates = draw_bs_reps(rainfall, np.var, size = 10000)\n",
    "\n",
    "# Put the variance in units of square centimeters (better lisibility)\n",
    "bs_replicates = bs_replicates / 100\n",
    "\n",
    "# Make a histogram of the results\n",
    "_ = plt.hist(bs_replicates, normed=True, bins=50)\n",
    "_ = plt.xlabel('variance of annual rainfall (sq. cm)')\n",
    "_ = plt.ylabel('PDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# This is not normally distributed, as it has a longer tail to the right. \n",
    "# Note that you can also compute a confidence interval on the variance, or any other statistic, using np.percentile() with your bootstrap replicates.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Draw bootstrap replicates of the mean no-hitter time (equal to tau): bs_replicates\n",
    "bs_replicates = draw_bs_reps(nohitter_times, np.mean, size=10000)\n",
    "\n",
    "# Compute the 95% confidence interval: conf_int\n",
    "conf_int = np.percentile(bs_replicates, [2.5, 97.5])\n",
    "\n",
    "# Print the confidence interval\n",
    "print('95% confidence interval =', conf_int, 'games')\n",
    "\n",
    "# Plot the histogram of the replicates\n",
    "_ = plt.hist(bs_replicates, bins=50, normed=True)\n",
    "_ = plt.xlabel(r'$\\tau$ (games)')\n",
    "_ = plt.ylabel('PDF')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.05535127 -0.04412664]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import scipy\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "df = pd.read_csv('sheffield_weather_station.csv', delimiter=';')\n",
    "rainfall = df['rain']\n",
    "\n",
    "def bootstrap_replicate_1d(data, func):\n",
    "    \"\"\" Output the mean for a given distribution through a boostrapped sample. \"\"\"\n",
    "    boostrap_sample = np.random.choice(data, len(data))\n",
    "    \n",
    "    return func(boostrap_sample)\n",
    "\n",
    "def draw_bs_reps(data, func, size=1):\n",
    "    \"\"\"Draw bootstrap replicates.\"\"\"\n",
    "\n",
    "    # Initialize array of replicates: bs_replicates\n",
    "    bs_replicates = np.empty(size)\n",
    "\n",
    "    # Generate replicates (not only the estimator)\n",
    "    for i in range(size):\n",
    "        bs_replicates[i] = bootstrap_replicate_1d(data, func)\n",
    "\n",
    "    return bs_replicates\n",
    "\n",
    "def draw_bs_pairs_linreg(x, y, size=1):\n",
    "    \"\"\"Perform pairs bootstrap for linear regression.\"\"\"\n",
    "\n",
    "    # Set up array of indices to sample from: inds\n",
    "    inds = np.arange(0, len(x))\n",
    "\n",
    "    # Initialize replicates: bs_slope_reps, bs_intercept_reps\n",
    "    bs_slope_reps = np.empty(size)\n",
    "    bs_intercept_reps = np.empty(size)\n",
    "\n",
    "    # Generate replicates\n",
    "    for i in range(size):\n",
    "        bs_inds = np.random.choice(inds, size=len(inds))\n",
    "        bs_x, bs_y = x[bs_inds], y[bs_inds]\n",
    "        bs_slope_reps[i], bs_intercept_reps[i] = np.polyfit(bs_x, bs_y, 1)\n",
    "\n",
    "    return bs_slope_reps, bs_intercept_reps\n",
    "\n",
    "\n",
    "# Generate replicates of slope and intercept using pairs bootstrap\n",
    "bs_slope_reps, bs_intercept_reps = draw_bs_pairs_linreg(literacy, fertility, 1000)\n",
    "\n",
    "# Compute and print 95% CI for slope\n",
    "print(np.percentile(bs_slope_reps, [2.5, 97.5]))\n",
    "\n",
    "# Plot the histogram\n",
    "_ = plt.hist(bs_slope_reps, bins=50, normed=True, color='red')\n",
    "_ = plt.xlabel('slope')\n",
    "_ = plt.ylabel('PDF')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Generate array of x-values for bootstrap lines: x\n",
    "x = np.array([0, 100])\n",
    "\n",
    "# Plot the bootstrap lines\n",
    "for i in range(100):\n",
    "    _ = plt.plot(x, \n",
    "                 bs_slope_reps[i] * x + bs_intercept_reps[i],\n",
    "                 linewidth=0.5, alpha=0.2, color='red')\n",
    "\n",
    "# Plot the data\n",
    "_ = plt.plot(literacy, fertility, marker = \".\", linestyle = 'none')\n",
    "\n",
    "# Label axes, set the margins, and show the plot\n",
    "_ = plt.xlabel('illiteracy')\n",
    "_ = plt.ylabel('fertility')\n",
    "\n",
    "plt.margins(0.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "II     20\n",
      "IV     20\n",
      "III    20\n",
      "I      20\n",
      "Name: ID, dtype: int64\n",
      "p-value = 0.0\n",
      "p-value = 1.0\n",
      "p-value = 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nNice work! You got a similar result as when you did the permutation test. \\nNonetheless, remember that it is important to carefully think about what question you want to ask. \\nAre you only interested in the mean impact force, or in the distribution of impact forces?\\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os, sys\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import scipy\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "df = pd.read_csv('sheffield_weather_station.csv', delimiter=';')\n",
    "\n",
    "rainfall_june = df[df['mm'] == 6]\n",
    "rainfall_november = df[df['mm'] == 11]\n",
    "rain_june = rainfall_june['rain']\n",
    "rain_november = rainfall_november['rain']\n",
    "\n",
    "def ecdf(data):\n",
    "    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n",
    "    # Number of data points: n\n",
    "    n = len(data)\n",
    "\n",
    "    # x-data for the ECDF: x\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y-data for the ECDF: y\n",
    "    y = np.arange(1, n + 1) / n\n",
    "\n",
    "    return x, y\n",
    "\n",
    "def permutation_sample(data1, data2):\n",
    "    \"\"\"Generate a permutation sample from two data sets.\"\"\"\n",
    "\n",
    "    # Concatenate the data sets: data\n",
    "    data = np.concatenate([data1, data2])\n",
    "\n",
    "    # Permute the concatenated array: permuted_data\n",
    "    permuted_data = np.random.permutation(data)\n",
    "\n",
    "    # Split the permuted array into two: perm_sample_1, perm_sample_2\n",
    "    perm_sample_1 = permuted_data[:len(data1)]\n",
    "    perm_sample_2 = permuted_data[len(data1):]\n",
    "\n",
    "    return perm_sample_1, perm_sample_2\n",
    "\n",
    "for _ in range(50):\n",
    "    # Generate permutation samples\n",
    "    perm_sample_1, perm_sample_2 = permutation_sample(rain_june, rain_november)\n",
    "\n",
    "\n",
    "    # Compute ECDFs\n",
    "    x_1, y_1 = ecdf(perm_sample_1)\n",
    "    x_2, y_2 = ecdf(perm_sample_2)\n",
    "\n",
    "    # Plot ECDFs of permutation sample\n",
    "    _ = plt.plot(x_1, y_1, marker='.', linestyle='none',\n",
    "                 color='red', alpha=0.02)\n",
    "    _ = plt.plot(x_2, y_2, marker='.', linestyle='none',\n",
    "                 color='blue', alpha=0.02)\n",
    "\n",
    "# Create and plot ECDFs from original data\n",
    "x_1, y_1 = ecdf(rain_june)\n",
    "x_2, y_2 = ecdf(rain_november)\n",
    "_ = plt.plot(x_1, y_1, marker='.', linestyle='none', color='red')\n",
    "_ = plt.plot(x_2, y_2, marker='.', linestyle='none', color='blue')\n",
    "\n",
    "# Label axes, set margin, and show plot\n",
    "plt.margins(0.02)\n",
    "_ = plt.xlabel('monthly rainfall (mm)')\n",
    "_ = plt.ylabel('ECDF')\n",
    "plt.show()\n",
    "\n",
    "# Notice that the permutation samples ECDFs overlap and give a purple haze. \n",
    "# None of the ECDFs from the permutation samples overlap with the observed data, \n",
    "# suggesting that the hypothesis is not commensurate with the data. \n",
    "# June and November rainfall are not identically distributed.\n",
    "\n",
    "\n",
    "\n",
    "# Significance test, null hypothesis\n",
    "def draw_perm_reps(data_1, data_2, func, size=1):\n",
    "    \"\"\"Generate multiple permutation replicates.\"\"\"\n",
    "\n",
    "    # Initialize array of replicates: perm_replicates\n",
    "    perm_replicates = np.empty(size)\n",
    "\n",
    "    for i in range(size):\n",
    "        # Generate permutation sample\n",
    "        perm_sample_1, perm_sample_2 = permutation_sample(data_1, data_2)\n",
    "\n",
    "        # Compute the test statistic\n",
    "        perm_replicates[i] = func(perm_sample_1, perm_sample_2)\n",
    "\n",
    "    return perm_replicates\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "frog = pd.read_csv('frog_tongue.csv')\n",
    "\n",
    "# Make bee swarm plot\n",
    "_ = sns.swarmplot(x='ID', y='impact force (mN)', data= frog )\n",
    "\n",
    "# Label axes\n",
    "_ = plt.xlabel('frog')\n",
    "_ = plt.ylabel('impact force (N)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "#Eyeballing it, it does not look like they come from the same distribution. \n",
    "#Frog A, the adult, has three or four very hard strikes, and Frog B, the juvenile, has a couple weak ones. \n",
    "#However, it is possible that with only 20 samples it might be too difficult to tell if they have difference distributions, \n",
    "#so we should proceed with the hypothesis test.\n",
    "\n",
    "#The average strike force of Frog A was 0.71 Newtons (N), and that of Frog B was 0.42 N for a difference of 0.29 N. \n",
    "#It is possible the frogs strike with the same force and this observed difference was by chance. \n",
    "#You will compute the probability of getting at least a 0.29 N difference in mean strike force under\n",
    "#the hypothesis that the distributions of strike forces for the two frogs are identical. \n",
    "#We use a permutation test with a test statistic of the difference of means to test this hypothesis.\n",
    "\n",
    "print(frog['ID'].value_counts())\n",
    "force_a = frog[frog['ID']=='I']\n",
    "force_a = force_a['impact force (mN)']\n",
    "force_b = frog[frog['ID']=='II']\n",
    "force_b = force_b['impact force (mN)']\n",
    "\n",
    "def diff_of_means(data_1, data_2):\n",
    "    \"\"\"Difference in means of two arrays.\"\"\"\n",
    "\n",
    "    # The difference of means of data_1, data_2: diff\n",
    "    diff = np.mean(data_1) - np.mean(data_2)\n",
    "\n",
    "    return diff\n",
    "\n",
    "# Compute difference of mean impact force from experiment: empirical_diff_means\n",
    "empirical_diff_means = diff_of_means(force_a, force_b)\n",
    "\n",
    "# Draw 10,000 permutation replicates: perm_replicates\n",
    "perm_replicates = draw_perm_reps(force_a, force_b, diff_of_means, size=10000)\n",
    "\n",
    "# Compute p-value: p\n",
    "p = np.sum(perm_replicates >=empirical_diff_means) / len(perm_replicates)\n",
    "\n",
    "# Print the result\n",
    "print('p-value =', p)\n",
    "\n",
    "#The p-value tells you that there is about a 0.6% chance that you would get the difference\n",
    "#of means observed in the experiment if frogs were exactly the same. \n",
    "#A p-value below 0.01 is typically said to be \"statistically significant,\" but: \n",
    "#    warning! warning! warning! You have computed a p-value; it is a number. \n",
    "#    I encourage you not to distill it to a yes-or-no phrase. p = 0.006 and p = 0.000000006 are both\n",
    "#    said to be \"statistically significant,\" but they are definitely not the same!\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "A one-sample bootstrap hypothesis test\n",
    "Another juvenile frog was studied, Frog C, and you want to see if Frog B and Frog C have similar impact forces. \n",
    "Unfortunately, you do not have Frog C's impact forces available, but you know they have a mean of 0.55 N. \n",
    "Because you don't have the original data, you cannot do a permutation test, \n",
    "and you cannot assess the hypothesis that the forces from Frog B and Frog C come from the same distribution. \n",
    "You will therefore test another, less restrictive hypothesis: The mean strike force of Frog B is equal to that of Frog C.\n",
    "To set up the bootstrap hypothesis test, you will take the mean as our test statistic. Remember, \n",
    "your goal is to calculate the probability of getting a mean impact force less than or equal to what\n",
    "was observed for Frog B if the hypothesis that the true mean of Frog B's impact forces is equal to that\n",
    "of Frog C is true. You first translate all of the data of Frog B such that the mean is 0.55 N. \n",
    "This involves adding the mean force of Frog C and subtracting the mean force of Frog B from each\n",
    "measurement of Frog B. This leaves other properties of Frog B's distribution, such as the variance, unchanged.\n",
    "\"\"\"\n",
    "def bootstrap_replicate_1d(data, func):\n",
    "    \"\"\" Output the mean for a given distribution through a boostrapped sample. \"\"\"\n",
    "    boostrap_sample = np.random.choice(data, len(data))\n",
    "    \n",
    "    return func(boostrap_sample)\n",
    "\n",
    "def draw_bs_reps(data, func, size=1):\n",
    "    \"\"\"Draw bootstrap replicates.\"\"\"\n",
    "\n",
    "    # Initialize array of replicates: bs_replicates\n",
    "    bs_replicates = np.empty(size)\n",
    "\n",
    "    # Generate replicates (not only the estimator)\n",
    "    for i in range(size):\n",
    "        bs_replicates[i] = bootstrap_replicate_1d(data, func)\n",
    "\n",
    "    return bs_replicates\n",
    "\n",
    "# Make an array of translated impact forces: translated_force_b\n",
    "translated_force_b = force_b - np.mean(force_b) + 0.55\n",
    "\n",
    "# Take bootstrap replicates of Frog B's translated impact forces: bs_replicates\n",
    "bs_replicates = draw_bs_reps(translated_force_b, np.mean, 10000)\n",
    "\n",
    "# Compute fraction of replicates that are less than the observed Frog B force: p\n",
    "p = np.sum(bs_replicates <= np.mean(force_b)) / 10000\n",
    "\n",
    "# Print the p-value\n",
    "print('p-value =', p)\n",
    "\n",
    "# The low p-value suggests that the null hypothesis that Frog B and Frog C have the same mean impact force is false.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "A two-sample bootstrap hypothesis test for difference of means\n",
    "We now want to test the hypothesis that Frog A and Frog B have the same mean impact force, \n",
    "but not necessarily the same distribution, which is also impossible with a permutation test.\n",
    "To do the two-sample bootstrap test, we shift both arrays to have the same mean, \n",
    "since we are simulating the hypothesis that their means are, in fact, equal. \n",
    "We then draw bootstrap samples out of the shifted arrays and compute the difference in means. \n",
    "This constitutes a bootstrap replicate, and we generate many of them. \n",
    "The p-value is the fraction of replicates with a difference in means greater than or equal to what was observed.\n",
    "The objects forces_concat and empirical_diff_means are already in your namespace.\n",
    "\"\"\"\n",
    "forces_concat = frog['impact force (mN)']\n",
    "# Compute mean of all forces: mean_force\n",
    "mean_force = np.mean(forces_concat)\n",
    "\n",
    "# Generate shifted arrays\n",
    "force_a_shifted = force_a - np.mean(force_a) + mean_force\n",
    "force_b_shifted = force_b - np.mean(force_b) + mean_force\n",
    "\n",
    "# Compute 10,000 bootstrap replicates from shifted arrays\n",
    "bs_replicates_a = draw_bs_reps(force_a_shifted, np.mean, size=10000)\n",
    "bs_replicates_b = draw_bs_reps(force_b_shifted, np.mean, size=10000)\n",
    "\n",
    "# Get replicates of difference of means: bs_replicates\n",
    "bs_replicates = bs_replicates_a - bs_replicates_b\n",
    "\n",
    "# Compute and print p-value: p\n",
    "p = np.sum(bs_replicates >= empirical_diff_means) / len(bs_replicates)\n",
    "print('p-value =', p)\n",
    "\n",
    "\"\"\"\n",
    "Nice work! You got a similar result as when you did the permutation test. \n",
    "Nonetheless, remember that it is important to carefully think about what question you want to ask. \n",
    "Are you only interested in the mean impact force, or in the distribution of impact forces?\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value = 0.0001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nWhich of the following situations involving testing by a web-based company has an equivalent \\nset up for an A/B test as the one you just did with the Civil Rights Act of 1964?\\n'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Statistical test is all about what we want to verify :\n",
    "Identifiez votre application : est-ce que vous voulez savoir si vos variables sont corrélées ? \n",
    "Ou savoir si les groupes d’individus dans votre expérience sont vraiment différents ? \n",
    "Ou encore savoir quelle variable influence les résultats plus que l’autres ?\n",
    "\n",
    "Ensuite, les distributions à tester sont-elles catégoriques ? numériques ?\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Construct arrays of data: dems, reps\n",
    "dems = np.array([True] * 153 + [False] * 91)\n",
    "reps = np.array([True] * 136 + [False] * 35)\n",
    "\n",
    "def permutation_sample(data1, data2):\n",
    "    \"\"\"Generate a permutation sample from two data sets.\"\"\"\n",
    "\n",
    "    # Concatenate the data sets: data\n",
    "    data = np.concatenate([data1, data2])\n",
    "\n",
    "    # Permute the concatenated array: permuted_data\n",
    "    permuted_data = np.random.permutation(data)\n",
    "\n",
    "    # Split the permuted array into two: perm_sample_1, perm_sample_2\n",
    "    perm_sample_1 = permuted_data[:len(data1)]\n",
    "    perm_sample_2 = permuted_data[len(data1):]\n",
    "\n",
    "    return perm_sample_1, perm_sample_2\n",
    "\n",
    "def draw_perm_reps(data_1, data_2, func, size=1):\n",
    "    \"\"\"Generate multiple permutation replicates.\"\"\"\n",
    "\n",
    "    # Initialize array of replicates: perm_replicates\n",
    "    perm_replicates = np.empty(size)\n",
    "\n",
    "    for i in range(size):\n",
    "        # Generate permutation sample\n",
    "        perm_sample_1, perm_sample_2 = permutation_sample(data_1, data_2)\n",
    "\n",
    "        # Compute the test statistic\n",
    "        perm_replicates[i] = func(perm_sample_1, perm_sample_2)\n",
    "\n",
    "    return perm_replicates\n",
    "\n",
    "def frac_yea_dems(dems, reps):\n",
    "    \"\"\"Compute fraction of Democrat yea votes.\"\"\"\n",
    "    frac = np.sum(dems) / len(dems)\n",
    "    return frac\n",
    "\n",
    "# Acquire permutation samples: perm_replicates\n",
    "perm_replicates = draw_perm_reps(dems, reps, frac_yea_dems, 10000)\n",
    "\n",
    "# Compute and print p-value: p\n",
    "p = np.sum(perm_replicates <= 153/244) / len(perm_replicates)\n",
    "print('p-value =', p)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "This small p-value suggests that party identity had a lot to do with the voting. \n",
    "Importantly, the South had a higher fraction of Democrat representatives, and consequently also a more racist bias.\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "Which of the following situations involving testing by a web-based company has an equivalent \n",
    "set up for an A/B test as the one you just did with the Civil Rights Act of 1964?\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nht_dead' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-2f8aa9b5b433>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Compute the observed difference in mean inter-no-hitter times: nht_diff_obs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnht_diff_obs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdiff_of_means\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnht_dead\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnht_live\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# Acquire 10,000 permutation replicates of difference in mean no-hitter time: perm_replicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mperm_replicates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdraw_perm_reps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnht_dead\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnht_live\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiff_of_means\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'nht_dead' is not defined"
     ]
    }
   ],
   "source": [
    "# Compute the observed difference in mean inter-no-hitter times: nht_diff_obs\n",
    "nht_diff_obs = diff_of_means(nht_dead, nht_live)\n",
    "\n",
    "# Acquire 10,000 permutation replicates of difference in mean no-hitter time: perm_replicates\n",
    "perm_replicates = draw_perm_reps(nht_dead, nht_live, diff_of_means,10000)\n",
    "\n",
    "\n",
    "# Compute and print the p-value: p\n",
    "p = np.sum(perm_replicates <= nht_diff_obs) / len(perm_replicates)\n",
    "print('p-val =', p)\n",
    "\n",
    "\"\"\"\n",
    "Your p-value is 0.0001, which means that only one out of your 10,000 replicates had a result as \n",
    "extreme as the actual difference between the dead ball and live ball eras. \n",
    "This suggests strong statistical significance. Watch out, though, you could very well \n",
    "have gotten zero replicates that were as extreme as the observed value. \n",
    "This just means that the p-value is quite small, almost certainly smaller than 0.001.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-val = 1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n/!\\\\ Reversed variables for example : true p-value = 0.0000\\nYou got a p-value of zero. In hacker statistics, this means that your p-value is very low, \\nsince you never got a single replicate in the 10,000 you took that had a Pearson correlation \\ngreater than the observed one. You could try increasing the number of replicates you take to \\ncontinue to move the upper bound on your p-value lower and lower.\\n'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "The observed correlation between female illiteracy and fertility in the data set of 162 countries \n",
    "may just be by chance; the fertility of a given country may actually be totally independent of its illiteracy. \n",
    "You will test this null hypothesis in the next exercise.\n",
    "\n",
    "To do the test, you need to simulate the data assuming the null hypothesis is true. \n",
    "Of the following choices, which is the best way to do it?\n",
    "\n",
    "Do a permutation test: Permute the illiteracy values but leave the fertility values fixed to\n",
    "generate a new set of (illiteracy, fertility) data.\n",
    "this exactly simulates the null hypothesis and does so more efficiently than the last option. \n",
    "It is exact because it uses all data and eliminates any correlation because which illiteracy value pairs\n",
    "to which fertility value is shuffled.\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, sys\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "dataset = pd.read_csv('female_literacy_fertility.csv')\n",
    "\n",
    "literacy = dataset['female literacy']\n",
    "fertility = dataset['fertility']\n",
    "\n",
    "def pearson_r(x, y):\n",
    "    \"\"\"Compute Pearson correlation coefficient between two arrays.\"\"\"\n",
    "    # Compute correlation matrix: corr_mat\n",
    "    corr_mat = np.corrcoef(x, y)\n",
    "\n",
    "    # Return entry [0,1]\n",
    "    return corr_mat[0,1]\n",
    "\n",
    "# Compute observed correlation: r_obs\n",
    "r_obs = pearson_r(literacy, fertility)\n",
    "\n",
    "# Initialize permutation replicates: perm_replicates\n",
    "perm_replicates = np.empty(20000)\n",
    "\n",
    "# Draw replicates\n",
    "for i in range(0, 20000):\n",
    "    # Permute illiteracy measurments: illiteracy_permuted\n",
    "    literacy_permuted = np.random.permutation(literacy)\n",
    "\n",
    "    # Compute Pearson correlation\n",
    "    perm_replicates[i] = pearson_r(literacy_permuted, fertility)\n",
    "\n",
    "# Compute p-value: p\n",
    "p = np.sum(perm_replicates >= r_obs) / len(perm_replicates)\n",
    "print('p-val =', p)\n",
    "\n",
    "\"\"\"\n",
    "/!\\ Reversed variables for example : true p-value = 0.0000\n",
    "You got a p-value of zero. In hacker statistics, this means that your p-value is very low, \n",
    "since you never got a single replicate in the 10,000 you took that had a Pearson correlation \n",
    "greater than the observed one. You could try increasing the number of replicates you take to \n",
    "continue to move the upper bound on your p-value lower and lower.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value = 0.0001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nNice work! The p-value is small, most likely less than 0.0001, since you never saw a bootstrap \\nreplicated with a difference of means at least as extreme as what was observed. In fact, when I \\ndid the calculation with 10 million replicates, I got a p-value of 2e-05.\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Do neonicotinoid insecticides have unintended consequences?\n",
    "As a final exercise in hypothesis testing before we put everything together in our case study in the next chapter, \n",
    "you will investigate the effects of neonicotinoid insecticides on bee reproduction. \n",
    "These insecticides are very widely used in the United States to combat aphids and other pests that damage plants.\n",
    "\n",
    "In a recent study, Straub, et al. (Proc. Roy. Soc. B, 2016) investigated the effects of neonicotinoids \n",
    "on the sperm of pollinating bees. In this and the next exercise, you will study how the pesticide treatment \n",
    "affected the count of live sperm per half milliliter of semen.\n",
    "\n",
    "First, we will do EDA, as usual. Plot ECDFs of the alive sperm count for untreated bees \n",
    "(stored in the Numpy array control) and bees treated with pesticide (stored in the Numpy array treated).\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, sys\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "df = pd.read_csv('bee_sperm.csv')\n",
    "\n",
    "control = df[df['Treatment'] == 'Control']\n",
    "treated = df[df['Treatment'] == 'Pesticide']\n",
    "\n",
    "control = control['Alive Sperm Millions']\n",
    "treated = treated['Alive Sperm Millions']\n",
    "\n",
    "\n",
    "def ecdf(data):\n",
    "    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n",
    "    # Number of data points: n\n",
    "    n = len(data)\n",
    "\n",
    "    # x-data for the ECDF: x\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y-data for the ECDF: y\n",
    "    y = np.arange(1, n + 1) / n\n",
    "\n",
    "    return x, y\n",
    "\n",
    "# Compute x,y values for ECDFs\n",
    "x_control, y_control = ecdf(control)\n",
    "x_treated, y_treated = ecdf(treated)\n",
    "\n",
    "# Plot the ECDFs\n",
    "plt.plot(x_control, y_control, marker='.', linestyle='none')\n",
    "plt.plot(x_treated, y_treated, marker='.', linestyle='none')\n",
    "\n",
    "# Set the marginsa\n",
    "plt.margins(0.02)\n",
    "\n",
    "# Add a legend\n",
    "plt.legend(('control', 'treated'), loc='lower right')\n",
    "\n",
    "# Label axes and show plot\n",
    "plt.xlabel('millions of alive sperm per mL')\n",
    "plt.ylabel('ECDF')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Bootstrap hypothesis test on bee sperm counts\n",
    "Now, you will test the following hypothesis: On average, male bees treated with neonicotinoid \n",
    "insecticide have the same number of active sperm per milliliter of semen than do untreated male bees. \n",
    "You will use the difference of means as your test statistic.\n",
    "\n",
    "For your reference, the call signature for the draw_bs_reps() function you wrote in chapter 2 is \n",
    "draw_bs_reps(data, func, size=1).\n",
    "\"\"\"\n",
    "\n",
    "def diff_of_means(data_1, data_2):\n",
    "    \"\"\"Difference in means of two arrays.\"\"\"\n",
    "\n",
    "    # The difference of means of data_1, data_2: diff\n",
    "    diff = np.mean(data_1) - np.mean(data_2)\n",
    "\n",
    "    return diff\n",
    "\n",
    "def bootstrap_replicate_1d(data, func):\n",
    "    \"\"\" Output the mean for a given distribution through a boostrapped sample. \"\"\"\n",
    "    boostrap_sample = np.random.choice(data, len(data))\n",
    "    \n",
    "    return func(boostrap_sample)\n",
    "\n",
    "def draw_bs_reps(data, func, size=1):\n",
    "    \"\"\"Draw bootstrap replicates.\"\"\"\n",
    "\n",
    "    # Initialize array of replicates: bs_replicates\n",
    "    bs_replicates = np.empty(size)\n",
    "\n",
    "    # Generate replicates (not only the estimator)\n",
    "    for i in range(size):\n",
    "        bs_replicates[i] = bootstrap_replicate_1d(data, func)\n",
    "\n",
    "    return bs_replicates\n",
    "\n",
    "# Compute the difference in mean sperm count: diff_means\n",
    "diff_means = diff_of_means(control, treated)\n",
    "\n",
    "# Compute mean of pooled data: mean_count\n",
    "mean_count = np.concatenate([control, treated]).mean()\n",
    "\n",
    "# Generate shifted data sets\n",
    "control_shifted = control - np.mean(control) + mean_count\n",
    "treated_shifted = treated - np.mean(treated) + mean_count\n",
    "\n",
    "# Generate bootstrap replicates\n",
    "bs_reps_control = draw_bs_reps(control_shifted, np.mean, size=10000)\n",
    "bs_reps_treated = draw_bs_reps(treated_shifted, np.mean, size=10000)\n",
    "\n",
    "# Get replicates of difference of means: bs_replicates\n",
    "bs_replicates = bs_reps_control - bs_reps_treated\n",
    "\n",
    "# Compute and print p-value: p\n",
    "p = np.sum(bs_replicates >= np.mean(control) - np.mean(treated)) \\\n",
    "            / len(bs_replicates)\n",
    "\n",
    "print('p-value =', p)\n",
    "\n",
    "\"\"\"\n",
    "Nice work! The p-value is small, most likely less than 0.0001, since you never saw a bootstrap \n",
    "replicated with a difference of means at least as extreme as what was observed. In fact, when I \n",
    "did the calculation with 10 million replicates, I got a p-value of 2e-05.\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   band species  beak_length  beak_depth  year\n",
      "0     2  fortis          9.4         8.0  1975\n",
      "1     9  fortis          9.2         8.3  1975\n",
      "2    12  fortis          9.5         7.5  1975\n",
      "3    15  fortis          9.5         8.0  1975\n",
      "4   305  fortis         11.5         9.9  1975\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference of means = 0.22313275434242108 mm\n",
      "95% confidence interval = [-0.33939578 -0.10816106] mm\n",
      "p = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1975: slope = 0.0945968320892116 conf int = [0.05500088 0.13486652]\n",
      "1975: intercept = 8.053907980716424 conf int = [7.58387362 8.50865659]\n",
      "2012: slope = 0.27942148396112504 conf int = [0.23508067 0.31727908]\n",
      "2012: intercept = 5.548559204612315 conf int = [5.10380598 6.07401118]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1975: mean ratio = 1.2461609991094325 conf int = [1.22372193 1.27035091]\n",
      "2012: mean ratio = 1.3483617710027846 conf int = [1.32467353 1.37280547]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nIn the previous exercise, you computed the mean beak length to depth ratio with 99% confidence intervals \\nfor 1975 and for 2012. The results of that calculation are shown graphically in the plot accompanying this problem. \\nIn addition to these results, what would you say about the ratio of beak length to depth?\\n\\nIt is impossible to say if this is a real effect or just due to noise without computing a p-value. \\nLet me compute the p-value and get back to you.\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, sys\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "os.chdir('C:/Users/T0230575/source/repos/DataCamp__/Statistical Thinking with Python (2)/')\n",
    "\n",
    "df_one = pd.read_csv('finch_beaks_1975.csv')\n",
    "df_two = pd.read_csv('finch_beaks_2012.csv')\n",
    "\n",
    "cols = ['band', 'species','beak_length', 'beak_depth']\n",
    "df_two.columns = cols\n",
    "df_one.columns = cols\n",
    "\n",
    "df_one['year'] = '1975'\n",
    "df_two['year'] = '2012'\n",
    "\n",
    "frames = [df_one, df_two]\n",
    "df = pd.concat(frames)\n",
    "print(df.head())\n",
    "\n",
    "# Create bee swarm plot\n",
    "_ = sns.swarmplot(x='year', y='beak_depth', data=df)\n",
    "\n",
    "# Label the axes\n",
    "_ = plt.xlabel('year')\n",
    "_ = plt.ylabel('beak depth (mm)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\"\"\"\n",
    "It is kind of hard to see if there is a clear difference between the 1975 and 2012 data set. \n",
    "Eyeballing it, it appears as though the mean of the 2012 data set might be slightly higher, \n",
    "and it might have a bigger variance.\n",
    "\n",
    "While bee swarm plots are useful, we found that ECDFs are often even better when doing EDA. \n",
    "Plot the ECDFs for the 1975 and 2012 beak depth measurements on the same plot.\n",
    "\"\"\"\n",
    "\n",
    "def ecdf(data):\n",
    "    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n",
    "    # Number of data points: n\n",
    "    n = len(data)\n",
    "\n",
    "    # x-data for the ECDF: x\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y-data for the ECDF: y\n",
    "    y = np.arange(1, n + 1) / n\n",
    "\n",
    "    return x, y\n",
    "\n",
    "bd_1975 = df_one['beak_depth']\n",
    "bd_2012 = df_two['beak_depth']\n",
    "\n",
    "# Compute ECDFs\n",
    "x_1975, y_1975 = ecdf(bd_1975)\n",
    "x_2012, y_2012 = ecdf(bd_2012)\n",
    "\n",
    "# Plot the ECDFs\n",
    "_ = plt.plot(x_1975, y_1975, color=\"red\", marker='.', linestyle='none')\n",
    "_ = plt.plot(x_2012, y_2012, color=\"blue\", marker='.', linestyle='none')\n",
    "\n",
    "# Set margins\n",
    "plt.margins(0.02)\n",
    "\n",
    "# Add axis labels and legend\n",
    "_ = plt.xlabel('beak depth (mm)')\n",
    "_ = plt.ylabel('ECDF')\n",
    "_ = plt.legend(('1975', '2012'), loc='lower right')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\"\"\"\n",
    "The differences are much clearer in the ECDF. \n",
    "The mean is larger in the 2012 data, and the variance does appear larger as well.\n",
    "\n",
    "Parameter estimates of beak depths\n",
    "Estimate the difference of the mean beak depth of the G. scandens samples from 1975 and 2012\n",
    "and report a 95% confidence interval.\n",
    "\"\"\"\n",
    "def bootstrap_replicate_1d(data, func):\n",
    "    \"\"\" Output the mean for a given distribution through a boostrapped sample. \"\"\"\n",
    "    boostrap_sample = np.random.choice(data, len(data))\n",
    "    \n",
    "    return func(boostrap_sample)\n",
    "\n",
    "def diff_of_means(data1, data2):\n",
    "    \n",
    "    diff = np.mean(data1) - np.mean(data2)\n",
    "    \n",
    "    return diff\n",
    "\n",
    "def draw_bs_reps(data, func, size=1):\n",
    "    \"\"\"Draw bootstrap replicates.\"\"\"\n",
    "\n",
    "    # Initialize array of replicates: bs_replicates\n",
    "    bs_replicates = np.empty(size)\n",
    "\n",
    "    # Generate replicates (not only the estimator)\n",
    "    for i in range(size):\n",
    "        bs_replicates[i] = bootstrap_replicate_1d(data, func)\n",
    "\n",
    "    return bs_replicates\n",
    "\n",
    "\n",
    "# Compute the difference of the sample means: mean_diff\n",
    "mean_diff = diff_of_means(bd_1975, bd_2012)\n",
    "\n",
    "# Get bootstrap replicates of means\n",
    "bs_replicates_1975 = draw_bs_reps(bd_1975, np.mean, 10000)\n",
    "bs_replicates_2012 = draw_bs_reps(bd_2012, np.mean, 10000)\n",
    "\n",
    "# Compute samples of difference of means: bs_diff_replicates\n",
    "bs_diff_replicates = bs_replicates_2012 - bs_replicates_1975\n",
    "\n",
    "# Compute 95% confidence interval: conf_int\n",
    "conf_int = np.percentile(bs_diff_replicates, [2.5, 97.5])\n",
    "\n",
    "# Print the results\n",
    "print('difference of means =', mean_diff, 'mm')\n",
    "print('95% confidence interval =', conf_int, 'mm')\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Hypothesis test: Are beaks deeper in 2012?\n",
    "Your plot of the ECDF and determination of the confidence interval make it pretty clear \n",
    "that the beaks of G. scandens on Daphne Major have gotten deeper. But is it possible that \n",
    "this effect is just due to random chance? In other words, what is the probability that we \n",
    "would get the observed difference in mean beak depth if the means were the same?\n",
    "\n",
    "Be careful! The hypothesis we are testing is not that the beak depths come from the same distribution. \n",
    "For that we could use a permutation test. The hypothesis is that the means are equal. \n",
    "To perform this hypothesis test, we need to shift the two data sets so that they have the same mean and\n",
    "then use bootstrap sampling to compute the difference of means.\n",
    "\"\"\"\n",
    "\n",
    "# Compute mean of combined data set: combined_mean\n",
    "combined_mean = np.mean(np.concatenate((bd_1975, bd_2012)))\n",
    "\n",
    "# Shift the samples\n",
    "bd_1975_shifted = bd_1975 - np.mean(bd_1975) + combined_mean\n",
    "bd_2012_shifted = bd_2012 - np.mean(bd_2012) + combined_mean\n",
    "\n",
    "# Get bootstrap replicates of shifted data sets\n",
    "bs_replicates_1975 = draw_bs_reps(bd_1975_shifted, np.mean, 10000)\n",
    "bs_replicates_2012 = draw_bs_reps(bd_2012_shifted, np.mean, 10000)\n",
    "\n",
    "# Compute replicates of difference of means: bs_diff_replicates\n",
    "bs_diff_replicates = bs_replicates_2012 - bs_replicates_1975\n",
    "\n",
    "# Compute the p-value\n",
    "p = np.sum(bs_diff_replicates >= mean_diff) / len(bs_diff_replicates)\n",
    "\n",
    "# Print p-value\n",
    "print('p =', p)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "We get a p-value of 0.0034, which suggests that there is a statistically significant difference. \n",
    "But remember: it is very important to know how different they are! In the previous exercise, \n",
    "you got a difference of 0.2 mm between the means. You should combine this with the statistical significance. \n",
    "Changing by 0.2 mm in 37 years is substantial by evolutionary standards. \n",
    "If it kept changing at that rate, the beak depth would double in only 400 years.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "bl_1975 = df_one['beak_length']\n",
    "bl_2012 = df_two['beak_length']\n",
    "\n",
    "# Make scatter plot of 1975 data\n",
    "_ = plt.plot(bl_1975, bd_1975, marker='.',\n",
    "             linestyle='None', color='yellow', alpha=.5)\n",
    "\n",
    "# Make scatter plot of 2012 data\n",
    "_ = plt.plot(bl_2012, bd_2012, marker='.',\n",
    "            linestyle='None', color=\"red\", alpha=.5)\n",
    "\n",
    "# Label axes and make legend\n",
    "_ = plt.xlabel('beak length (mm)')\n",
    "_ = plt.ylabel('beak depth (mm)')\n",
    "_ = plt.legend(('1975', '2012'), loc='upper left')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\"\"\"\n",
    "In looking at the plot, we see that beaks got deeper (the red points are higher up in the y-direction), \n",
    "but not really longer. If anything, they got a bit shorter, since the red dots are to the left of the blue dots. \n",
    "So, it does not look like the beaks kept the same shape; they became shorter and deeper\n",
    "\n",
    "Now let's say we want to see/compare the evolution of 2 distributions in the same time, let's say if they evolve towards\n",
    "the same dimension or not.\n",
    "\"\"\"\n",
    "\n",
    "def draw_bs_pairs_linreg(x, y, size=1):\n",
    "    \"\"\"Perform pairs bootstrap for linear regression.\"\"\"\n",
    "\n",
    "    # Set up array of indices to sample from: inds\n",
    "    inds = np.arange(0, len(x))\n",
    "\n",
    "    # Initialize replicates: bs_slope_reps, bs_intercept_reps\n",
    "    bs_slope_reps = np.empty(size)\n",
    "    bs_intercept_reps = np.empty(size)\n",
    "\n",
    "    # Generate replicates\n",
    "    for i in range(size):\n",
    "        bs_inds = np.random.choice(inds, size=len(inds))\n",
    "        bs_x, bs_y = x[bs_inds], y[bs_inds]\n",
    "        bs_slope_reps[i], bs_intercept_reps[i] = np.polyfit(bs_x, bs_y, 1)\n",
    "\n",
    "    return bs_slope_reps, bs_intercept_reps\n",
    "\n",
    "\n",
    "\n",
    "# Compute the linear regressions\n",
    "slope_1975, intercept_1975 = np.polyfit(bl_1975, bd_1975, 1)\n",
    "slope_2012, intercept_2012 = np.polyfit(bl_2012, bd_2012, 1)\n",
    "\n",
    "# Perform pairs bootstrap for the linear regressions\n",
    "bs_slope_reps_1975, bs_intercept_reps_1975 = \\\n",
    "        draw_bs_pairs_linreg(bl_1975, bd_1975, size=1000)\n",
    "bs_slope_reps_2012, bs_intercept_reps_2012 = \\\n",
    "        draw_bs_pairs_linreg(bl_2012, bd_2012, size=1000)\n",
    "\n",
    "# Compute confidence intervals of slopes\n",
    "slope_conf_int_1975 = np.percentile(bs_slope_reps_1975, [2.5, 97.5])\n",
    "slope_conf_int_2012 = np.percentile(bs_slope_reps_2012, [2.5, 97.5])\n",
    "intercept_conf_int_1975 = np.percentile(bs_intercept_reps_1975, [2.5, 97.5])\n",
    "intercept_conf_int_2012 = np.percentile(bs_intercept_reps_2012, [2.5, 97.5])\n",
    "\n",
    "\n",
    "# Print the results\n",
    "print('1975: slope =', slope_1975,\n",
    "      'conf int =', slope_conf_int_1975)\n",
    "print('1975: intercept =', intercept_1975,\n",
    "      'conf int =', intercept_conf_int_1975)\n",
    "print('2012: slope =', slope_2012,\n",
    "      'conf int =', slope_conf_int_2012)\n",
    "print('2012: intercept =', intercept_2012,\n",
    "      'conf int =', intercept_conf_int_2012)\n",
    "\n",
    "# It looks like they have the same slope, but different intercepts.\n",
    "\n",
    "# Make scatter plot of 1975 data\n",
    "_ = plt.plot(bl_1975, bd_1975, marker='.',\n",
    "             linestyle='none', color='blue', alpha=0.5)\n",
    "\n",
    "# Make scatter plot of 2012 data\n",
    "_ = plt.plot(bl_2012, bd_2012, marker='.',\n",
    "             linestyle='none', color='red', alpha=0.5)\n",
    "\n",
    "# Label axes and make legend\n",
    "_ = plt.xlabel('beak length (mm)')\n",
    "_ = plt.ylabel('beak depth (mm)')\n",
    "_ = plt.legend(('1975', '2012'), loc='upper left')\n",
    "\n",
    "# Generate x-values for bootstrap lines: x\n",
    "x = np.array([10, 17])\n",
    "\n",
    "# Plot the bootstrap lines\n",
    "for i in range(100):\n",
    "    plt.plot(x, bs_slope_reps_1975[i] * x + bs_intercept_reps_1975[i],\n",
    "             linewidth=0.5, alpha=0.2, color='blue')\n",
    "    plt.plot(x, bs_slope_reps_2012[i] * x + bs_intercept_reps_2012[i],\n",
    "             linewidth=0.5, alpha=0.2, color='red')\n",
    "\n",
    "# Draw the plot again\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The linear regressions showed interesting information about the beak geometry. \n",
    "The slope was the same in 1975 and 2012, suggesting that for every millimeter gained in beak length, \n",
    "the birds gained about half a millimeter in depth in both years. However, if we are interested in the shape \n",
    "of the beak, we want to compare the ratio of beak length to beak depth. Let's make that comparison.\n",
    "\"\"\"\n",
    "\n",
    "# Compute length-to-depth ratios\n",
    "ratio_1975 = bl_1975 / bd_1975\n",
    "ratio_2012 = bl_2012 / bd_2012\n",
    "\n",
    "# Compute means\n",
    "mean_ratio_1975 = np.mean(ratio_1975)\n",
    "mean_ratio_2012 = np.mean(ratio_2012)\n",
    "\n",
    "# Generate bootstrap replicates of the means\n",
    "bs_replicates_1975 = draw_bs_reps(ratio_1975, np.mean, size=10000)\n",
    "bs_replicates_2012 = draw_bs_reps(ratio_2012, np.mean, size=10000)\n",
    "\n",
    "# Compute the 99% confidence intervals\n",
    "conf_int_1975 = np.percentile(bs_replicates_1975, [0.5, 99.5])\n",
    "conf_int_2012 = np.percentile(bs_replicates_2012, [0.5, 99.5])\n",
    "\n",
    "# Print the results\n",
    "print('1975: mean ratio =', mean_ratio_1975,\n",
    "      'conf int =', conf_int_1975)\n",
    "print('2012: mean ratio =', mean_ratio_2012,\n",
    "      'conf int =', conf_int_2012)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "In the previous exercise, you computed the mean beak length to depth ratio with 99% confidence intervals \n",
    "for 1975 and for 2012. The results of that calculation are shown graphically in the plot accompanying this problem. \n",
    "In addition to these results, what would you say about the ratio of beak length to depth?\n",
    "\n",
    "It is impossible to say if this is a real effect or just due to noise without computing a p-value. \n",
    "Let me compute the p-value and get back to you.\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G. scandens: 0.4117063629401258 [0.26514825 0.53021634]\n",
      "G. fortis: 0.7283412395518487 [0.6710712  0.77780497]\n",
      "G. scandens: 0.5485340868685982 [0.35614013 0.75023704]\n",
      "G. fortis: 0.7229051911438156 [0.64494431 0.79685283]\n",
      "p-val = 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nYou get a p-value of zero, which means that none of the 10,000 permutation pairs replicates you drew\\nhad a heritability high enough to match that which was observed. This strongly suggests that beak depth\\nis heritable in G. scandens, just not as much as in G. fortis. If you like, you can plot a histogram of\\nthe heritability replicates to get a feel for how extreme of a value of heritability you might expect by chance.\\n'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "EDA of heritability\n",
    "The array bd_parent_scandens contains the average beak depth (in mm) of two parents of the species G. scandens. \n",
    "The array bd_offspring_scandens contains the average beak depth of the offspring of the respective parents. \n",
    "The arrays bd_parent_fortis and bd_offspring_fortis contain the same information about measurements from G. fortis birds.\n",
    "\n",
    "Make a scatter plot of the average offspring beak depth (y-axis) versus average parental beak depth (x-axis) \n",
    "for both species. Use the alpha=0.5 keyword argument to help you see overlapping points.\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fortis = pd.read_csv('fortis_beak_depth_heredity.csv')\n",
    "scandens = pd.read_csv('scandens_beak_depth_heredity.csv')\n",
    "df_one = pd.read_csv('finch_beaks_1975.csv')\n",
    "df_two = pd.read_csv('finch_beaks_2012.csv')\n",
    "\n",
    "cols = ['band', 'species','beak_length', 'beak_depth']\n",
    "df_two.columns = cols\n",
    "df_one.columns = cols\n",
    "\n",
    "df_one['year'] = '1975'\n",
    "df_two['year'] = '2012'\n",
    "\n",
    "frames = [df_one, df_two]\n",
    "df = pd.concat(frames)\n",
    "\n",
    "fortis['specie'] = 'fortis'\n",
    "scandens['specie'] = 'scandens'\n",
    "\n",
    "col = ['mid_parent','Mid-offspr','specie']\n",
    "fortis['mid_parent'] = fortis.iloc[:, [1,2]].mean(axis=1)\n",
    "\n",
    "fortis = fortis[col]\n",
    "fortis.rename(columns={\"Mid-offspr\": \"mid_offspring\"}, inplace = True)\n",
    "\n",
    "bd_parent_fortis = fortis['mid_parent']\n",
    "bd_offspring_fortis = fortis['mid_offspring']\n",
    "\n",
    "bd_parent_scandens = scandens['mid_parent']\n",
    "bd_offspring_scandens = scandens['mid_offspring']\n",
    "\n",
    "# Make scatter plots\n",
    "_ = plt.plot(bd_parent_fortis, bd_offspring_fortis,\n",
    "             marker=\".\", linestyle='none', color='blue', alpha=.5)\n",
    "_ = plt.plot(bd_parent_scandens, bd_offspring_scandens,\n",
    "             marker=\".\", linestyle='none', color='red', alpha=.5)\n",
    "\n",
    "# Label axes\n",
    "_ = plt.xlabel('parental beak depth (mm)')\n",
    "_ = plt.ylabel('offspring beak depth (mm)')\n",
    "\n",
    "# Add legend\n",
    "_ = plt.legend(('G. fortis', 'G. scandens'), loc='lower right')\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n",
    "\n",
    "\"\"\"\n",
    "It appears as though there is a stronger correlation in G. fortis than in G. scandens. \n",
    "This suggests that beak depth is more strongly inherited in G. fortis. We'll quantify this correlation next.\n",
    "\n",
    "Correlation of offspring and parental data\n",
    "\n",
    "In an effort to quantify the correlation between offspring and parent beak depths, we would like to compute statistics,\n",
    "such as the Pearson correlation coefficient, between parents and offspring. To get confidence intervals on this, \n",
    "we need to do a pairs bootstrap.\n",
    "\"\"\"\n",
    "\n",
    "def draw_bs_pairs(x, y, func, size=1):\n",
    "    \"\"\"Perform pairs bootstrap for a single statistic.\"\"\"\n",
    "\n",
    "    # Set up array of indices to sample from: inds\n",
    "    inds = np.arange(len(x))\n",
    "\n",
    "    # Initialize replicates: bs_replicates\n",
    "    bs_replicates = np.empty(size)\n",
    "\n",
    "    # Generate replicates\n",
    "    for i in range(size):\n",
    "        bs_inds = np.random.choice(inds, len(inds))\n",
    "        bs_x, bs_y = x[bs_inds], y[bs_inds]\n",
    "        bs_replicates[i] = func(bs_x, bs_y)\n",
    "\n",
    "    return bs_replicates\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Pearson correlation of offspring and parental data\n",
    "The Pearson correlation coefficient seems like a useful measure of how strongly the beak depth of parents\n",
    "are inherited by their offspring. Compute the Pearson correlation coefficient between parental and offspring\n",
    "beak depths for G. scandens. Do the same for G. fortis. Then, use the function you wrote in the last exercise\n",
    "to compute a 95% confidence interval using pairs bootstrap.\n",
    "\"\"\"\n",
    "\n",
    "def pearson_r(x, y):\n",
    "    \"\"\"Compute Pearson correlation coefficient between two arrays.\"\"\"\n",
    "    # Compute correlation matrix: corr_mat\n",
    "    corr_mat = np.corrcoef(x, y)\n",
    "\n",
    "    # Return entry [0,1]\n",
    "    return corr_mat[0,1]\n",
    "\n",
    "# Compute the Pearson correlation coefficients\n",
    "r_scandens = pearson_r(bd_parent_scandens, bd_offspring_scandens)\n",
    "r_fortis = pearson_r(bd_parent_fortis, bd_offspring_fortis)\n",
    "\n",
    "# Acquire 1000 bootstrap replicates of Pearson r\n",
    "bs_replicates_scandens = draw_bs_pairs(bd_parent_scandens, bd_offspring_scandens, pearson_r,1000)\n",
    "bs_replicates_fortis = draw_bs_pairs(bd_parent_fortis, bd_offspring_fortis, pearson_r,1000)\n",
    "\n",
    "# Compute 95% confidence intervals\n",
    "conf_int_scandens = np.percentile(bs_replicates_scandens, q=[2.5, 97.5])\n",
    "conf_int_fortis = np.percentile(bs_replicates_fortis, q=[2.5, 97.5])\n",
    "\n",
    "# Print results\n",
    "print('G. scandens:', r_scandens, conf_int_scandens)\n",
    "print('G. fortis:', r_fortis, conf_int_fortis)\n",
    "\n",
    "\"\"\"\n",
    "It is clear from the confidence intervals that beak depth of the offspring of G. fortis parents is\n",
    "more strongly correlated with their offspring than their G. scandens counterparts.\n",
    "\n",
    "\n",
    "Measuring heritability\n",
    "Remember that the Pearson correlation coefficient is the ratio of the covariance to the geometric mean\n",
    "of the variances of the two data sets. This is a measure of the correlation between parents and offspring,\n",
    "but might not be the best estimate of heritability. If we stop and think, it makes more sense to define heritability\n",
    "as the ratio of the covariance between parent and offspring to the variance of the parents alone. In this exercise,\n",
    "you will estimate the heritability and perform a pairs bootstrap calculation to get the 95% confidence interval.\n",
    "\n",
    "This exercise highlights a very important point. Statistical inference (and data analysis in general)\n",
    "is not a plug-n-chug enterprise. You need to think carefully about the questions you are seeking to answer\n",
    "with your data and analyze them appropriately. If you are interested in how heritable traits are, \n",
    "the quantity we defined as the heritability is more apt than the off-the-shelf statistic, the Pearson\n",
    "correlation coefficient.\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def heritability(parents, offspring):\n",
    "    \"\"\"Compute the heritability from parent and offspring samples.\"\"\"\n",
    "    covariance_matrix = np.cov(parents, offspring)\n",
    "    return covariance_matrix[0,1] / covariance_matrix[0,0]\n",
    "\n",
    "# Compute the heritability\n",
    "heritability_scandens = heritability(bd_parent_scandens, bd_offspring_scandens)\n",
    "heritability_fortis = heritability(bd_parent_fortis, bd_offspring_fortis)\n",
    "\n",
    "# Acquire 1000 bootstrap replicates of heritability\n",
    "replicates_scandens = draw_bs_pairs(\n",
    "        bd_parent_scandens, bd_offspring_scandens, heritability, size=1000)\n",
    "        \n",
    "replicates_fortis = draw_bs_pairs(\n",
    "        bd_parent_fortis, bd_offspring_fortis, heritability, size=1000)\n",
    "\n",
    "\n",
    "# Compute 95% confidence intervals\n",
    "conf_int_scandens = np.percentile( replicates_scandens, q=[2.5,97.5])\n",
    "conf_int_fortis = np.percentile( replicates_fortis, q=[2.5,97.5])\n",
    "\n",
    "# Print results\n",
    "print('G. scandens:', heritability_scandens, conf_int_scandens)\n",
    "print('G. fortis:', heritability_fortis, conf_int_fortis)\n",
    "\n",
    "\"\"\"\n",
    "Here again, we see that G. fortis has stronger heritability than G. scandens. \n",
    "This suggests that the traits of G. fortis may be strongly incorporated into \n",
    "G. scandens by introgressive hybridization. (???)\n",
    "\n",
    "\n",
    "Is beak depth heritable at all in G. scandens?\n",
    "The heritability of beak depth in G. scandens seems low. It could be that this observed heritability\n",
    "was just achieved by chance and beak depth is actually not really heritable in the species. \n",
    "You will test that hypothesis here. To do this, you will do a pairs permutation test.\n",
    "\"\"\"\n",
    "\n",
    "# Initialize array of replicates: perm_replicates\n",
    "perm_replicates = np.empty(10000)\n",
    "\n",
    "# Draw replicates\n",
    "for i in range(10000):\n",
    "    # Permute parent beak depths\n",
    "    bd_parent_permuted = np.random.permutation(bd_parent_scandens)\n",
    "    perm_replicates[i] = heritability(bd_parent_permuted, bd_offspring_scandens)\n",
    "\n",
    "\n",
    "# Compute p-value: p\n",
    "p = np.sum(perm_replicates >= heritability_scandens) / len(perm_replicates)\n",
    "\n",
    "# Print the p-value\n",
    "print('p-val =', p)\n",
    "\n",
    "\"\"\"\n",
    "You get a p-value of zero, which means that none of the 10,000 permutation pairs replicates you drew\n",
    "had a heritability high enough to match that which was observed. This strongly suggests that beak depth\n",
    "is heritable in G. scandens, just not as much as in G. fortis. If you like, you can plot a histogram of\n",
    "the heritability replicates to get a feel for how extreme of a value of heritability you might expect by chance.\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n"
   ]
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