{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "%matplotlib inline\n",
    "\n",
    "np.set_printoptions(legacy='1.13')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Please run all cells before this cell, including the import cell at the top of the notebook.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>Eligible</th> <th>Panels</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Asian    </td> <td>0.15    </td> <td>0.26  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.18    </td> <td>0.08  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Latino   </td> <td>0.12    </td> <td>0.08  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.54    </td> <td>0.54  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.01    </td> <td>0.04  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | Eligible | Panels\n",
       "Asian     | 0.15     | 0.26\n",
       "Black     | 0.18     | 0.08\n",
       "Latino    | 0.12     | 0.08\n",
       "White     | 0.54     | 0.54\n",
       "Other     | 0.01     | 0.04"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jury = Table().with_columns(\n",
    "    'Ethnicity', make_array('Asian', 'Black', 'Latino', 'White', 'Other'),\n",
    "    'Eligible', make_array(0.15, 0.18, 0.12, 0.54, 0.01),\n",
    "    'Panels', make_array(0.26, 0.08, 0.08, 0.54, 0.04)\n",
    ")\n",
    "jury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m05tCh7v/a8YqC2T9y5ujLiFyFRUV2l1Zn9Y6I4cO0qgRgwNVBADIRemEjIcVuwYj2aOSBmSmnPCWr1wddQmRq6qqUlFRUVrrLLzgNEIGACAt7YYMM8uTZLFJs/h0k69KSu9PYgAA0CWkciSjXpInTCdqlLQkoxUBAICckErIGKvY0YunJR2XMN8lfeLuX4QoDAAAdG7thgx3fzc+yYdtAQCAlLUZMszsV+7+/fj0Xa2Nc/d/zHRhAACgc2vvSMY7CdNbQxYCAAByS5shw91/njB9VfhyAABArkj52SVmttjMpiXNm25mizJfFgAA6OzSeUDapZLeSJr3hqQfZK4cAACQK9IJGT0k1SXNq5VUmLlyAABArkgnZLwk6Z+T5i2Q9OfMlQMAAHJFOs8uWSjpCTM7V7E7Tf5K0lBJ32xvRTOrdPfeqezEzI6XVOvuz8VfL5C0z91bvYUWAAB0PCmHDHffZGbjJJ0iaZSk30p62N0rM1zT8ZIqJT0X3++tGd4+AADIgnSOZCgeKO7JxI7N7FuSfqzYtR67JZ0jqadip2AazKxU0sWSTpRU6e7/x8zKJL0g6QRJ/SRd4O7PmlmhpH+XNFWx56tc5u5PZaJOAABwcNr7xM9H3f1v4tPPqvlBaftx9+Namt+OtZJmuLub2XclLXL3H5rZrYqHivh+T0yu2d2nm9nJkn4qaY6kC+N1HGVm4yU9bmbj3L36IOoCAAAZ0N6RjMTrIFZkeN+HSbrXzIYrdjTjnXbGN/lt/L8vSRoTn54l6RZJcvctZvaupHGSNiavfOOl05JndVpV9X31zo4Wc1/GFXRzlZeXZ2Vf2ZSL7+lg0Idm9CKms/ShpKQk6hLQhvY+8fO/EqbvzPC+b5F0o7s/FL/Y82cprlcT/2+Dmuu3VHdaUvhEqkM7vJqBpRo3YVLa65WXl/ODKfrQhD40oxcx9AGZktY1GWb2PyRNlrTfnSLu/q8Hse9iSR/Gp89LmP+5pL5pbusZxa7peDJ+cerhkv5yEDUBAIAMSTlkmNkvJZ0p6SlJ+9LcTy8z+yDh9Y2KHbn4jZl9KGm9pLHxZb+XdL+ZnarYhZ+p+DdJt5rZa4pd+Dnf3WvaWQcAAASUzpGMsyVNdvf3092Ju7f2oV8PtjD2TUmJ5wCeTVh2fML0LsWvyYhf4Dk/3boAAEA46Xzi525Jn4UqBAAA5JZ0jmTcIOnXZvZzSTsSF7j72xmtCgAAdHrphIx/j//3lKT5LqlbZsoBAAC5Ip2PFU/n1AoAAOjiCA4AACCIdG5hHStpiVr+nIzDM1wXAADo5NK5JuO/FHvE+w+V/udkAACALiadkDFR0rHu3hiqGAAAkDvSuSbjGUlfD1UIAADILe096v3qhJfbJD1mZr+VtD1x3EE+uwQAAOSw9k6XjEp6/XtJ+S3MBwAA2E97j3r/n9kqJFtqBpZGXULGNHYfEnUJAAC0Kp1bWPe4+4AW5u90907z266h16T2BwEAgEOWzoWf+ckzzCxffKQ4AABoQbtHMszsWcWeT1JoZs8kLT5M0vMhCgMAAJ1bKqdLVkgySdMkrUyY74o9jXVNgLoAAEAnl0rImOLul5jZenffYmYXuPuXYcPMHpB0RrgSAQBAZ5TKNRnzJcndt8RfX5+0/JuZLAgAAOSGVEKGtfMaAADgAKmcLvF2Xncq61/eHHUJkauoqNDuyvqoy4gcfYihD83oRUy2+jBy6CCNGjE4+H4QnVRCRnczO0HNRzCSX3eqW1iXr1wddQmRq6qqUlFRUdRlRI4+xNCHZvQiJlt9WHjBaYSMHJdKyNgp6faE17uTXu/MaEUAACAntBsy3H1MFuoAAAA5Jp1P/AQAAEgZIQMAAARByAAAAEEQMgAAQBCEDAAAEAQhAwAABEHIAAAAQRAyAABAEIQMAAAQRNZChpktN7MfJLx+zMxWJLy+wcwuM7OHW1l/hZkdEZ++InzFAADgUGTzSMZzko6RJDPLkzRI0sSE5cdIym9tZXf/rru/EX9JyAAAoIPLZshYp3jIUCxcvC7pczPrb2YFkiZIellSbzO738y2mNmvzcwkyczKzGyqmS2V1NPMXjGzX8eXlZrZi/F5t5lZp3oyLAAAuShrIcPdP5JUb2aHKxY2npf0gqSZkqZK2iipVtLXJf1A0hGSviLp2KTtLJb0hbtPdvdzzGyCpLMkHevukyU1SDonO+8KAAC0JpVHvWdS09GMYyTdKGlkfLpCsdMpkvSiu38gSWb2iqQxkta2sc0TJU2RtCF+0KOn2nj8/I2XTjukNwAAyAzrUavy8vJD2kZJSUmGqkEI2Q4ZTddlHKXY6ZL3Jf1Q0l5Jt8fH1CSMb1D7NZqkO9398lQKKCl8Ip16c1JVVZWKioqiLiNy9CGGPjSjFzHZ6kPNwFI19CIk5LJs38K6TtIpkva4e4O775HUT7FTJs+nsZ06M2u6SHSNpHlmNkSSzGyAmY3OZNEAACB92Q4Zryl2V8n6pHkV7r4rje38StJGM/t1/I6TH0t63Mw2SnpC0vBMFQwAAA6OuXvUNQRXUVHx5Zvs9f6iKEvpEDgkHEMfYuhDM3oRk93TJZMytr3i4mLL2MaQEXziJwAACIKQAQAAgiBkAACAIAgZAAAgCEIGAAAIgpABAACCIGQAAIAgCBkAACAIQgYAAAiCkAEAAIIgZAAAgCCy/aj3yNUMLI26hMhVdqtQ937FUZcROfoQQx+a0YuYbPWhsfuQ4PtAtLpcyMjkw3g6q+0flqvPiJKoy4gcfYihD83oRQx9QKZwugQAAARByAAAAEEQMgAAQBCEDAAAEAQhAwAABEHIAAAAQXS5W1jXv7z5gHkjhw7SqBGDI6gGAIDc1eVCxvKVqw+Yt/CC0wgZAABkGKdLAABAEIQMAAAQBCEDAAAEQcgAAABBEDIAAEAQhAwAABAEIQMAAARByAAAAEEQMgAAQBCEDAAAEAQhAwAABJH1kGFmh5nZg2ZWbmZbzexmM+thZpPN7OSEcT8zs/+d7foAAEBmZDVkmJlJ+q2k37l7iaRxknpLWiJpsqST21g93X11y9S2AABA+rJ9JGO2pGp3/09JcvcGSQslfVfSMklnmdkrZnZWfPwRZlZmZm+b2SVNGzGzUjN7MT72tqZAYWaVZna1mb0gaWZW3xkAANhPtkPGREkvJc5w972Stkm6VtK97j7Z3e+NLx4v6SRJ0yX91MzyzWyCpLMkHevukyU1SDonPr5I0uvu/t/dfW3wdwMAAFrVPcv7M0mexvw/uHuNpBoz2ylpqKQTJU2RtCF29kU9Je2Mj2+Q9EBbBdx46bQD5vUs2KnPP9qV4ltoX636aU9VYca2F0J5eXnUJXQI9CGGPjSjFzGdpQ8lJSVRl4A2ZDtkbJJ0RuIMM+sraZRiASFZTcJ0g2L1mqQ73f3yFsZXx0/BtKqk8ImWF7S5VnpqBpZq4IiO+41fXl7OD6boQxP60IxexNAHZEq2T5eskdTLzP5R+vLizBsk3SFph6Q+KW5jnpkNiW9jgJmNDlMuAAA4WFkNGe7ukk6T9A9mVi7pTUnVkq6Q9JRiF3omXvjZ0jbekPRjSY+b2UZJT0gaHrx4AACQlmyfLpG7vy/pWy0sqpF04AUTzesdmTB9r6R7WxjTOxM1AgCAQ8cnfgIAgCAIGQAAIAhCBgAACIKQAQAAgiBkAACAIAgZAAAgCEIGAAAIgpABAACCIGQAAIAgCBkAACAIQgYAAAgi688uiVrNwNLg+2jsPiT4PgAA6Oi6XMho6DUp6hIAAOgSOF0CAACCIGQAAIAgCBkAACAIQgYAAAiCkAEAAIIgZAAAgCAIGQAAIAhz96hrCK6ioiL33yQAdHHFxcUWdQ3YH0cyAABAEIQMAAAQRJc4XQIAALKPIxkAACCInAsZZvY3ZvYXM3vLzBa3sLzAzO6NL3/BzMZkv8rwUujDcWb2ZzOrN7N5UdSYLSn04jIze8PMNprZGjMbHUWdoaXQhwVm9pqZvWJma83siCjqDK29PiSMm2dmbmZTs1lfNqXwPTHfzD6Jf0+8YmbfjaJOdGLunjNfkrpJ2irpK5J6SHpV0hFJY/5Z0q3x6W9LujfquiPqwxhJkyTdJWle1DVH3IsTJPWKT/9TF/6e6JswPVfSo1HXHUUf4uP6SHpG0npJU6OuO8LvifmSfhl1rXx13q9cO5IxXdJb7v62u9dKukfSqUljTpV0Z3z6fkknmlmu3fbUbh/cfZu7b5TUGEWBWZRKL55y933xl+slHZblGrMhlT7sTXhZJCkXL9hK5d8ISbpG0jJJ1dksLstS7QVw0HItZIyU9H7C6w/i81oc4+71kiokDcxKddmTSh+6inR7cYGkPwatKBop9cHMLjSzrYr9gr0kS7VlU7t9MLOvSxrl7g9ns7AIpPqzcUb8VOL9ZjYqO6UhV+RayGjpiETyX2OpjOnsusJ7TFXKvTCzUklTJV0ftKJopNQHd/+/7v5VSf8i6cfBq8q+NvtgZnmSlkv6YdYqik4q3xO/lzTG3SdJ+n9qPgoMpCTXQsYHkhKT9mGSPmptjJl1l1QsaU9WqsueVPrQVaTUCzObI+lKSXPdvSZLtWVTut8T90j6+6AVRaO9PvSRdKSkMjPbJmmGpIdy9OLPdr8n3H13ws/Df0iakqXakCNyLWRskFRiZmPNrIdiF3Y+lDTmIUnnxafnSXrS3XPtr/xU+tBVtNuL+OHx2xQLGDsjqDEbUulDScLLv5NUnsX6sqXNPrh7hbsPcvcx7j5GsWt05rr7n6IpN6hUvieGJ7ycK2lzFutDDugedQGZ5O71ZnaRpMcUu3L6dnffZGZXS/qTuz8kaaWkVWb2lmJHML4dXcVhpNIHM5smabWk/pK+ZWZXufvECMsOIsXviesl9Zb0m/g1wO+5+9zIig4gxT5cFD+iUyfpUzWH8ZyRYh+6hBR7cYmZzZVUr9i/l/MjKxidEp/4CQAAgsi10yUAAKCDIGQAAIAgCBkAACAIQgYAAAiCkAEAAIIgZAAAgCAIGQAAIAhCBgAACOL/A1rQaKEWHUjAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jury.barh('Ethnicity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "jury_with_diffs = jury.with_column('Difference', jury.column('Panels') - jury.column('Eligible'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>Eligible</th> <th>Panels</th> <th>Difference</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Asian    </td> <td>0.15    </td> <td>0.26  </td> <td>0.11      </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.18    </td> <td>0.08  </td> <td>-0.1      </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Latino   </td> <td>0.12    </td> <td>0.08  </td> <td>-0.04     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.54    </td> <td>0.54  </td> <td>0         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.01    </td> <td>0.04  </td> <td>0.03      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | Eligible | Panels | Difference\n",
       "Asian     | 0.15     | 0.26   | 0.11\n",
       "Black     | 0.18     | 0.08   | -0.1\n",
       "Latino    | 0.12     | 0.08   | -0.04\n",
       "White     | 0.54     | 0.54   | 0\n",
       "Other     | 0.01     | 0.04   | 0.03"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jury_with_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "jury_with_diffs = jury_with_diffs.with_column('Absolute Difference', np.abs(jury_with_diffs.column('Difference')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>Eligible</th> <th>Panels</th> <th>Difference</th> <th>Absolute Difference</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Asian    </td> <td>0.15    </td> <td>0.26  </td> <td>0.11      </td> <td>0.11               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.18    </td> <td>0.08  </td> <td>-0.1      </td> <td>0.1                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Latino   </td> <td>0.12    </td> <td>0.08  </td> <td>-0.04     </td> <td>0.04               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.54    </td> <td>0.54  </td> <td>0         </td> <td>0                  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.01    </td> <td>0.04  </td> <td>0.03      </td> <td>0.03               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | Eligible | Panels | Difference | Absolute Difference\n",
       "Asian     | 0.15     | 0.26   | 0.11       | 0.11\n",
       "Black     | 0.18     | 0.08   | -0.1       | 0.1\n",
       "Latino    | 0.12     | 0.08   | -0.04      | 0.04\n",
       "White     | 0.54     | 0.54   | 0          | 0\n",
       "Other     | 0.01     | 0.04   | 0.03       | 0.03"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jury_with_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28000000000000003"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(jury_with_diffs.column('Absolute Difference'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14000000000000001"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(jury_with_diffs.column('Absolute Difference')) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_variation_distance(distribution_1, distribution_2):\n",
    "    return sum(np.abs(distribution_1 - distribution_2)) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14000000000000001"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_variation_distance(jury.column('Panels'), jury.column('Eligible'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "eligible = jury.column('Eligible')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "panels_and_sample = jury.with_column('Random Sample', sample_proportions(1453, eligible))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>Eligible</th> <th>Panels</th> <th>Random Sample</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Asian    </td> <td>0.15    </td> <td>0.26  </td> <td>0.146593     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.18    </td> <td>0.08  </td> <td>0.200275     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Latino   </td> <td>0.12    </td> <td>0.08  </td> <td>0.113558     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.54    </td> <td>0.54  </td> <td>0.528562     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.01    </td> <td>0.04  </td> <td>0.0110117    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | Eligible | Panels | Random Sample\n",
       "Asian     | 0.15     | 0.26   | 0.146593\n",
       "Black     | 0.18     | 0.08   | 0.200275\n",
       "Latino    | 0.12     | 0.08   | 0.113558\n",
       "White     | 0.54     | 0.54   | 0.528562\n",
       "Other     | 0.01     | 0.04   | 0.0110117"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panels_and_sample"
   ]
  },
  {
   "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"
    }
   ],
   "source": [
    "panels_and_sample.barh('Ethnicity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021286992429456288"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_variation_distance(panels_and_sample.column('Random Sample'), eligible)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14000000000000001"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_variation_distance(jury.column('Panels'), eligible)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvds = make_array()\n",
    "\n",
    "repetitions = 10000\n",
    "for i in np.arange(repetitions):\n",
    "    sample_distribution = sample_proportions(1453, eligible)\n",
    "    new_tvd = total_variation_distance(sample_distribution, eligible)\n",
    "    tvds = np.append(tvds, new_tvd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "Table().with_column('Total Variation Distance', tvds).hist(bins = np.arange(0, 0.2, 0.005), ec='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14000000000000001"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_variation_distance(jury.column('Panels'), eligible)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}