ec_votes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression as LR\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>EC votes</th>\n",
       "      <th>2016 Estimate</th>\n",
       "      <th>2010 Census</th>\n",
       "      <th>Rural population</th>\n",
       "      <th>Urban population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>9</td>\n",
       "      <td>4863300</td>\n",
       "      <td>4779736</td>\n",
       "      <td>1957932</td>\n",
       "      <td>2821804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>3</td>\n",
       "      <td>741894</td>\n",
       "      <td>710231</td>\n",
       "      <td>241338</td>\n",
       "      <td>468893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>11</td>\n",
       "      <td>6931071</td>\n",
       "      <td>6392017</td>\n",
       "      <td>651358</td>\n",
       "      <td>5740659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>6</td>\n",
       "      <td>2988248</td>\n",
       "      <td>2915918</td>\n",
       "      <td>1278329</td>\n",
       "      <td>1637589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>55</td>\n",
       "      <td>39250017</td>\n",
       "      <td>37253956</td>\n",
       "      <td>1880350</td>\n",
       "      <td>35373606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>9</td>\n",
       "      <td>5540545</td>\n",
       "      <td>5029196</td>\n",
       "      <td>696435</td>\n",
       "      <td>4332761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>7</td>\n",
       "      <td>3576452</td>\n",
       "      <td>3574097</td>\n",
       "      <td>429155</td>\n",
       "      <td>3144942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>3</td>\n",
       "      <td>952065</td>\n",
       "      <td>897934</td>\n",
       "      <td>149985</td>\n",
       "      <td>747949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>3</td>\n",
       "      <td>681170</td>\n",
       "      <td>601723</td>\n",
       "      <td>0</td>\n",
       "      <td>601723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>29</td>\n",
       "      <td>20612439</td>\n",
       "      <td>18801310</td>\n",
       "      <td>1661466</td>\n",
       "      <td>17139844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>16</td>\n",
       "      <td>10310371</td>\n",
       "      <td>9687653</td>\n",
       "      <td>2415502</td>\n",
       "      <td>7272151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>4</td>\n",
       "      <td>1428557</td>\n",
       "      <td>1360301</td>\n",
       "      <td>109812</td>\n",
       "      <td>1250489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>4</td>\n",
       "      <td>1683140</td>\n",
       "      <td>1567582</td>\n",
       "      <td>461212</td>\n",
       "      <td>1106370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>20</td>\n",
       "      <td>12801539</td>\n",
       "      <td>12830632</td>\n",
       "      <td>1477079</td>\n",
       "      <td>11353553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>11</td>\n",
       "      <td>6633053</td>\n",
       "      <td>6483802</td>\n",
       "      <td>1786702</td>\n",
       "      <td>4697100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>6</td>\n",
       "      <td>3134693</td>\n",
       "      <td>3046355</td>\n",
       "      <td>1096099</td>\n",
       "      <td>1950256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>6</td>\n",
       "      <td>2907289</td>\n",
       "      <td>2853118</td>\n",
       "      <td>736157</td>\n",
       "      <td>2116961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>4436974</td>\n",
       "      <td>4339367</td>\n",
       "      <td>1806024</td>\n",
       "      <td>2533343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>8</td>\n",
       "      <td>4681666</td>\n",
       "      <td>4533372</td>\n",
       "      <td>1215567</td>\n",
       "      <td>3317805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>4</td>\n",
       "      <td>1331479</td>\n",
       "      <td>1328361</td>\n",
       "      <td>814819</td>\n",
       "      <td>513542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>10</td>\n",
       "      <td>6016447</td>\n",
       "      <td>5773552</td>\n",
       "      <td>739221</td>\n",
       "      <td>5034331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>11</td>\n",
       "      <td>6811779</td>\n",
       "      <td>6547629</td>\n",
       "      <td>525640</td>\n",
       "      <td>6021989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>16</td>\n",
       "      <td>9928301</td>\n",
       "      <td>9883640</td>\n",
       "      <td>2513683</td>\n",
       "      <td>7369957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>10</td>\n",
       "      <td>5519952</td>\n",
       "      <td>5303925</td>\n",
       "      <td>1417614</td>\n",
       "      <td>3886311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>6</td>\n",
       "      <td>2988726</td>\n",
       "      <td>2967297</td>\n",
       "      <td>1503073</td>\n",
       "      <td>1464224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>10</td>\n",
       "      <td>6093000</td>\n",
       "      <td>5988927</td>\n",
       "      <td>1770556</td>\n",
       "      <td>4218371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>3</td>\n",
       "      <td>1042520</td>\n",
       "      <td>989415</td>\n",
       "      <td>436401</td>\n",
       "      <td>553014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>5</td>\n",
       "      <td>1907116</td>\n",
       "      <td>1826341</td>\n",
       "      <td>490655</td>\n",
       "      <td>1335686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>6</td>\n",
       "      <td>2940058</td>\n",
       "      <td>2700551</td>\n",
       "      <td>156754</td>\n",
       "      <td>2543797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4</td>\n",
       "      <td>1334795</td>\n",
       "      <td>1316470</td>\n",
       "      <td>522598</td>\n",
       "      <td>793872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>14</td>\n",
       "      <td>8944469</td>\n",
       "      <td>8791894</td>\n",
       "      <td>467768</td>\n",
       "      <td>8324126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>5</td>\n",
       "      <td>2081015</td>\n",
       "      <td>2059179</td>\n",
       "      <td>464818</td>\n",
       "      <td>1594361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>29</td>\n",
       "      <td>19745289</td>\n",
       "      <td>19378102</td>\n",
       "      <td>2349997</td>\n",
       "      <td>17028105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>15</td>\n",
       "      <td>10146788</td>\n",
       "      <td>9535483</td>\n",
       "      <td>3233727</td>\n",
       "      <td>6301756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>3</td>\n",
       "      <td>757952</td>\n",
       "      <td>672591</td>\n",
       "      <td>269719</td>\n",
       "      <td>402872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>18</td>\n",
       "      <td>11614373</td>\n",
       "      <td>11536504</td>\n",
       "      <td>2546810</td>\n",
       "      <td>8989694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>7</td>\n",
       "      <td>3923561</td>\n",
       "      <td>3751351</td>\n",
       "      <td>1266322</td>\n",
       "      <td>2485029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>7</td>\n",
       "      <td>4093465</td>\n",
       "      <td>3831074</td>\n",
       "      <td>726692</td>\n",
       "      <td>3104382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>20</td>\n",
       "      <td>12802503</td>\n",
       "      <td>12702379</td>\n",
       "      <td>2711092</td>\n",
       "      <td>9991287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>4</td>\n",
       "      <td>1056426</td>\n",
       "      <td>1052567</td>\n",
       "      <td>97524</td>\n",
       "      <td>955043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>9</td>\n",
       "      <td>4961119</td>\n",
       "      <td>4625364</td>\n",
       "      <td>1557555</td>\n",
       "      <td>3067809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>3</td>\n",
       "      <td>865454</td>\n",
       "      <td>814180</td>\n",
       "      <td>352933</td>\n",
       "      <td>461247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>11</td>\n",
       "      <td>6651194</td>\n",
       "      <td>6346105</td>\n",
       "      <td>2132860</td>\n",
       "      <td>4213245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>38</td>\n",
       "      <td>27862596</td>\n",
       "      <td>25145561</td>\n",
       "      <td>3847522</td>\n",
       "      <td>21298039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Utah</td>\n",
       "      <td>6</td>\n",
       "      <td>3051217</td>\n",
       "      <td>2763885</td>\n",
       "      <td>260290</td>\n",
       "      <td>2503595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>3</td>\n",
       "      <td>624594</td>\n",
       "      <td>625741</td>\n",
       "      <td>382356</td>\n",
       "      <td>243385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>13</td>\n",
       "      <td>8411808</td>\n",
       "      <td>8001024</td>\n",
       "      <td>1963930</td>\n",
       "      <td>6037094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Washington</td>\n",
       "      <td>12</td>\n",
       "      <td>7288000</td>\n",
       "      <td>6724540</td>\n",
       "      <td>1072671</td>\n",
       "      <td>5651869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>5</td>\n",
       "      <td>1831102</td>\n",
       "      <td>1852994</td>\n",
       "      <td>950184</td>\n",
       "      <td>902810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>10</td>\n",
       "      <td>5778708</td>\n",
       "      <td>5686986</td>\n",
       "      <td>1697348</td>\n",
       "      <td>3989638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>3</td>\n",
       "      <td>585501</td>\n",
       "      <td>563626</td>\n",
       "      <td>198633</td>\n",
       "      <td>364993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Region  EC votes  2016 Estimate  2010 Census  \\\n",
       "0                Alabama         9        4863300      4779736   \n",
       "1                 Alaska         3         741894       710231   \n",
       "2                Arizona        11        6931071      6392017   \n",
       "3               Arkansas         6        2988248      2915918   \n",
       "4             California        55       39250017     37253956   \n",
       "5               Colorado         9        5540545      5029196   \n",
       "6            Connecticut         7        3576452      3574097   \n",
       "7               Delaware         3         952065       897934   \n",
       "8   District of Columbia         3         681170       601723   \n",
       "9                Florida        29       20612439     18801310   \n",
       "10               Georgia        16       10310371      9687653   \n",
       "11                Hawaii         4        1428557      1360301   \n",
       "12                 Idaho         4        1683140      1567582   \n",
       "13              Illinois        20       12801539     12830632   \n",
       "14               Indiana        11        6633053      6483802   \n",
       "15                  Iowa         6        3134693      3046355   \n",
       "16                Kansas         6        2907289      2853118   \n",
       "17              Kentucky         8        4436974      4339367   \n",
       "18             Louisiana         8        4681666      4533372   \n",
       "19                 Maine         4        1331479      1328361   \n",
       "20              Maryland        10        6016447      5773552   \n",
       "21         Massachusetts        11        6811779      6547629   \n",
       "22              Michigan        16        9928301      9883640   \n",
       "23             Minnesota        10        5519952      5303925   \n",
       "24           Mississippi         6        2988726      2967297   \n",
       "25              Missouri        10        6093000      5988927   \n",
       "26               Montana         3        1042520       989415   \n",
       "27              Nebraska         5        1907116      1826341   \n",
       "28                Nevada         6        2940058      2700551   \n",
       "29         New Hampshire         4        1334795      1316470   \n",
       "30            New Jersey        14        8944469      8791894   \n",
       "31            New Mexico         5        2081015      2059179   \n",
       "32              New York        29       19745289     19378102   \n",
       "33        North Carolina        15       10146788      9535483   \n",
       "34          North Dakota         3         757952       672591   \n",
       "35                  Ohio        18       11614373     11536504   \n",
       "36              Oklahoma         7        3923561      3751351   \n",
       "37                Oregon         7        4093465      3831074   \n",
       "38          Pennsylvania        20       12802503     12702379   \n",
       "39          Rhode Island         4        1056426      1052567   \n",
       "40        South Carolina         9        4961119      4625364   \n",
       "41          South Dakota         3         865454       814180   \n",
       "42             Tennessee        11        6651194      6346105   \n",
       "43                 Texas        38       27862596     25145561   \n",
       "44                  Utah         6        3051217      2763885   \n",
       "45               Vermont         3         624594       625741   \n",
       "46              Virginia        13        8411808      8001024   \n",
       "47            Washington        12        7288000      6724540   \n",
       "48         West Virginia         5        1831102      1852994   \n",
       "49             Wisconsin        10        5778708      5686986   \n",
       "50               Wyoming         3         585501       563626   \n",
       "\n",
       "    Rural population  Urban population  \n",
       "0            1957932           2821804  \n",
       "1             241338            468893  \n",
       "2             651358           5740659  \n",
       "3            1278329           1637589  \n",
       "4            1880350          35373606  \n",
       "5             696435           4332761  \n",
       "6             429155           3144942  \n",
       "7             149985            747949  \n",
       "8                  0            601723  \n",
       "9            1661466          17139844  \n",
       "10           2415502           7272151  \n",
       "11            109812           1250489  \n",
       "12            461212           1106370  \n",
       "13           1477079          11353553  \n",
       "14           1786702           4697100  \n",
       "15           1096099           1950256  \n",
       "16            736157           2116961  \n",
       "17           1806024           2533343  \n",
       "18           1215567           3317805  \n",
       "19            814819            513542  \n",
       "20            739221           5034331  \n",
       "21            525640           6021989  \n",
       "22           2513683           7369957  \n",
       "23           1417614           3886311  \n",
       "24           1503073           1464224  \n",
       "25           1770556           4218371  \n",
       "26            436401            553014  \n",
       "27            490655           1335686  \n",
       "28            156754           2543797  \n",
       "29            522598            793872  \n",
       "30            467768           8324126  \n",
       "31            464818           1594361  \n",
       "32           2349997          17028105  \n",
       "33           3233727           6301756  \n",
       "34            269719            402872  \n",
       "35           2546810           8989694  \n",
       "36           1266322           2485029  \n",
       "37            726692           3104382  \n",
       "38           2711092           9991287  \n",
       "39             97524            955043  \n",
       "40           1557555           3067809  \n",
       "41            352933            461247  \n",
       "42           2132860           4213245  \n",
       "43           3847522          21298039  \n",
       "44            260290           2503595  \n",
       "45            382356            243385  \n",
       "46           1963930           6037094  \n",
       "47           1072671           5651869  \n",
       "48            950184            902810  \n",
       "49           1697348           3989638  \n",
       "50            198633            364993  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_states = ['California', 'New York', 'District of Columbia', 'Wyoming', 'Michigan']\n",
    "names = ['CA', 'NY', 'DC', 'WY', 'MI']\n",
    "alpha = .4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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777xz1uvGYjG6Itlpv77+1PCU7Y8++ihf//rX+fa3v004HOaJJ57g29/+Nn/zN3/DqlWr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\nEclw/x/xGNLbSEvvRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 3, figsize=(15, 5), sharey=True)\n",
    "ax0, ax1, ax2 = axes\n",
    "\n",
    "avg = data['EC votes'].sum() / data['2010 Census'].sum()\n",
    "rural = np.dot(data['Rural population'], data['EC votes'] / data['2010 Census']) / data['Rural population'].sum()\n",
    "urban = np.dot(data['Urban population'], data['EC votes'] / data['2010 Census']) / data['Urban population'].sum()\n",
    "\n",
    "y = data['EC votes'] / data['2010 Census']\n",
    "keys = ['2010 Census', 'Rural population', 'Urban population']\n",
    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c']\n",
    "xlabels = ['Population', 'Rural population', 'Urban population']\n",
    "for ax, key, c, xlabel in zip(axes, keys, colors, xlabels):\n",
    "    x = data[key]\n",
    "    ax.scatter(x, y, c=c, alpha=alpha)\n",
    "    ax.grid()\n",
    "    ax.plot([x.min(), x.max()], [avg, avg], c='#1f77b4', label='Total')\n",
    "    ax.plot([x.min(), x.max()], [rural, rural], c='#ff7f0e', label='Urban')\n",
    "    ax.plot([x.min(), x.max()], [urban, urban], c='#2ca02c', label='Rural')\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel('EC votes per person')\n",
    "    ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))\n",
    "    ax.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))\n",
    "    for st, n in zip(label_states, names):\n",
    "        idx = np.argmax(data['Region'] == st)\n",
    "        ax.annotate(n, (x[idx], y[idx]),\n",
    "                    xytext=(5, -5), textcoords='offset points',\n",
    "                   rotation=-45)\n",
    "        ax.scatter(x[idx], y[idx], c=c)\n",
    "\n",
    "ax0.set_ylim(0, 1.1 * y.max())\n",
    "ax0.legend(loc='best')\n",
    "\n",
    "plt.savefig('ec_per_person.png')\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3770255809340237"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[8] / y[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_states2 = ['California', 'New York', 'Wyoming', 'Michigan']\n",
    "names2 = ['CA', 'NY', 'WY', 'MI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-19.9163335536\n",
      "-14.7670268647\n",
      "12.0178617966\n",
      "-11.2225916347\n",
      "12.0178617966\n",
      "-11.2225916347\n"
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    },
    {
     "data": {
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vd+c6H/OWW25h5cqVjBkzpmqi0YYNG+ohWvtitk8IrfURrXXvin/dtdZvVmzP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\nlo5NXJ70X8JiUs7m8/n6I6z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ZDz74ADc3t/Mee+SRR3jmmWeIiooyawxCCNtQvZB6gLcr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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(10, 5), sharey=False)\n",
    "\n",
    "plot_first = 45\n",
    "x = data['2010 Census'].values\n",
    "idxs = np.argsort(x)\n",
    "x = x[idxs]\n",
    "y = data['EC votes'].values\n",
    "y = y[idxs]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.scatter(x, y, c='#1f77b4', alpha=alpha)\n",
    "    ax.grid()\n",
    "    ax.plot([x.min(), x.max()], [x.min() * avg, x.max() * avg], c='#1f77b4',\n",
    "            label='Proportional representation')\n",
    "    for xp, yp in zip(x, y):\n",
    "        ax.plot([xp, xp], [yp, xp * avg], c='red', lw=.5)\n",
    "    ax.set_xlabel('Population')\n",
    "    ax.set_ylabel('EC votes')\n",
    "    ax.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))\n",
    "\n",
    "    for st, n in zip(label_states2, names2):\n",
    "        idx = np.argmax(data['Region'].values[idxs] == st)\n",
    "        try:\n",
    "            xp, yp = x[idx], y[idx]\n",
    "            yloc = yp - (avg * xp) + 10 * np.sign(yp - (avg * xp))\n",
    "            print(yloc)\n",
    "            ax.annotate(n, (xp, yp),\n",
    "                        xytext=(0, yloc), textcoords='offset points',\n",
    "                       rotation=0, ha='center', va='center')\n",
    "            ax.scatter(x[idx], y[idx], c='#1f77b4')\n",
    "        except IndexError:\n",
    "            pass\n",
    "    x = x[:plot_first]\n",
    "    y = y[:plot_first]\n",
    "\n",
    "axes[0].set_xticks(np.arange(0, 5e7, 1e7))\n",
    "axes[0].set_yticks(np.arange(0, 70, 10))\n",
    "\n",
    "axes[1].set_xticks(np.arange(0, 1.5e7, .25e7))\n",
    "axes[1].set_yticks(np.arange(0, 25, 5))\n",
    "\n",
    "axes[0].legend(loc='best')\n",
    "plt.savefig('ec_per_state.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bCE1DA3+xj51SquHq64LoJyLHqWgJhzq+x/HYGGOaur06izT2ymdWimwSyG+6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\nUkpZRANYKaUsogGslFIW0QBWSimL/H/aj8GHrtHP7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(5, 5))\n",
    "x = data['2010 Census'].values\n",
    "idxs = np.argsort(x)\n",
    "y = data['Rural population'].values / x\n",
    "x = x[idxs]\n",
    "y = y[idxs]\n",
    "model = LR()\n",
    "model.fit(x[:, np.newaxis], y[:, np.newaxis])\n",
    "ax.plot([x.min(), x.max()], [float(model.predict(x.min())), float(model.predict(x.max()))],\n",
    "        label='Fit', c='#1f77b4')\n",
    "ax.scatter(x, y, c='#1f77b4', alpha=alpha)\n",
    "ax.grid()\n",
    "ax.set_xlabel('Population')\n",
    "ax.set_ylabel('Fraction Rural')\n",
    "ax.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))\n",
    "ax.set_xticks(np.arange(0, 5e7, 1e7))\n",
    "ax.set_yticks(np.arange(0, .8, .2))\n",
    "for st, n in zip(label_states, names):\n",
    "    idx = np.argmax(data['Region'].values[idxs] == st)\n",
    "    ax.annotate(n, (x[idx], y[idx]),\n",
    "                xytext=(0, 10), textcoords='offset points',\n",
    "               rotation=0, ha='center', va='center')\n",
    "    ax.scatter(x[idx], y[idx], c='#1f77b4')\n",
    "\n",
    "ax.legend(loc='best')\n",
    "f.tight_layout()\n",
    "\n",
    "plt.savefig('pop_vs_rural.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Region', 'EC votes', '2016 Estimate', '2010 Census',\n",
      "       'Rural population', 'Urban population'],\n",
      "      dtype='object')\n",
      "538\n",
      "308745538\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)\n",
    "print(data['EC votes'].sum())\n",
    "print(data['2010 Census'].sum())"
   ]
  }
 ],
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