brexit_demographics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook I relate the brexit voting data per area to various demographic data found at [2011 Census, Key Statistics for Local Authorities in England and Wales](http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/publications/re-reference-tables.html?edition=tcm%3A77-286262). I check if there is a relation between the voting and median age, unemployment, higher education and born outside UK."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the census tables we need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "census_data = {\n",
    "     \"age\" : \"http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/rel/census/2011-census/key-statistics-for-local-authorities-in-england-and-wales/rft-table-ks102ew.xls\",\n",
    "     \"unemployment\" : \"http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/rel/census/2011-census/key-statistics-for-local-authorities-in-england-and-wales/rft-table-ks601ew.xls\",\n",
    "     \"education\" : \"http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/rel/census/2011-census/key-statistics-for-local-authorities-in-england-and-wales/rft-table-ks501ew.xls\",\n",
    "     \"outside_uk\" : \"http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/rel/census/2011-census/key-statistics-for-local-authorities-in-england-and-wales/rft-table-qs203ew.xls\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in the votes data and calculate the diff Remain - Leave (so negative is \"in favor of Leave\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "votes_org = pd.read_csv(\"http://www.electoralcommission.org.uk/__data/assets/file/0014/212135/EU-referendum-result-data.csv\", usecols=[\"Area_Code\", \"Region\", \"Remain\", \"Leave\"] )\n",
    "votes_org.rename(index=str, inplace=True, columns={\"Area_Code\": \"Area code\", }) # same col name for merge\n",
    "votes_org[\"votes\"] = votes_org[\"Remain\"] - votes_org[\"Leave\"]\n",
    "votes = votes_org[[\"Area code\", \"votes\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area code</th>\n",
       "      <th>votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E06000031</td>\n",
       "      <td>-19040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E06000032</td>\n",
       "      <td>-11065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area code  votes\n",
       "0  E06000031 -19040\n",
       "1  E06000032 -11065"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in the census data one by one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area code</th>\n",
       "      <th>median_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K04000001</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E92000001</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area code  median_age\n",
       "0  K04000001        39.0\n",
       "2  E92000001        39.0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.read_excel(io=census_data[\"age\"], sheetname=\"KS102EW_Numbers\", header=10, parse_cols=(\"A,W\"), skiprows=[11,12,13])\n",
    "age.dropna(how='all', inplace=True)\n",
    "age.rename(index=str, inplace=True, columns={\"Median age\": \"median_age\", }) \n",
    "age = age[[\"Area code\", \"median_age\"]]\n",
    "age.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area code</th>\n",
       "      <th>perc_unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K04000001</td>\n",
       "      <td>0.043756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E92000001</td>\n",
       "      <td>0.043796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area code  perc_unemployed\n",
       "0  K04000001         0.043756\n",
       "2  E92000001         0.043796"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment = pd.read_excel(io=census_data[\"unemployment\"], sheetname=\"KS601EW_Numbers\", header=10, parse_cols=(\"A,E,I\"), skiprows=[11,12,13])\n",
    "unemployment.dropna(how='all', inplace=True)\n",
    "unemployment[\"perc_unemployed\"] = unemployment[unemployment.columns[2]] / unemployment[unemployment.columns[1]]\n",
    "unemployment = unemployment[[\"Area code\", \"perc_unemployed\"]]\n",
    "unemployment.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area code</th>\n",
       "      <th>perc_high_education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K04000001</td>\n",
       "      <td>0.395661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E92000001</td>\n",
       "      <td>0.397282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area code  perc_high_education\n",
       "1  K04000001             0.395661\n",
       "3  E92000001             0.397282"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# level 3 and 4+ = cols J and K \n",
    "education = pd.read_excel(io=census_data[\"education\"], sheetname=\"KS501EW_Numbers\", header=10, parse_cols=(\"A,E,J,K\"), skiprows=[11,12,13])\n",
    "education.dropna(how='all', inplace=True)\n",
    "education[\"perc_high_education\"] = (education[education.columns[2]] + education[education.columns[3]]) / education[education.columns[1]]\n",
    "education = education[[\"Area code\", \"perc_high_education\"]]\n",
    "education.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area code</th>\n",
       "      <th>perc_born_outside_uk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K04000001</td>\n",
       "      <td>0.133837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E92000001</td>\n",
       "      <td>0.138404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area code  perc_born_outside_uk\n",
       "0  K04000001              0.133837\n",
       "2  E92000001              0.138404"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outside_uk = pd.read_excel(io=census_data[\"outside_uk\"], sheetname=\"QS203EW_Numbers\", header=10, parse_cols=(\"A,E,G\"), skiprows=[11,12,13])\n",
    "outside_uk.dropna(how='all', inplace=True)\n",
    "outside_uk.columns[2]\n",
    "# all birth (col E) - UK birth (col G) = outside birth\n",
    "outside_uk[\"perc_born_outside_uk\"] = (outside_uk[outside_uk.columns[1]] - outside_uk[outside_uk.columns[2]]) / outside_uk[outside_uk.columns[1]]\n",
    "outside_uk = outside_uk[[\"Area code\", \"perc_born_outside_uk\"]]\n",
    "outside_uk.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge all datasets to one dataframe, on key = Area code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area code</th>\n",
       "      <th>votes</th>\n",
       "      <th>median_age</th>\n",
       "      <th>perc_unemployed</th>\n",
       "      <th>perc_high_education</th>\n",
       "      <th>perc_born_outside_uk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E06000031</td>\n",
       "      <td>-19040</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.050568</td>\n",
       "      <td>0.310281</td>\n",
       "      <td>0.206349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E06000032</td>\n",
       "      <td>-11065</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.057414</td>\n",
       "      <td>0.333318</td>\n",
       "      <td>0.309408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E06000033</td>\n",
       "      <td>-15174</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.049669</td>\n",
       "      <td>0.332425</td>\n",
       "      <td>0.098596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E06000034</td>\n",
       "      <td>-35614</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.054239</td>\n",
       "      <td>0.283631</td>\n",
       "      <td>0.119692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E06000055</td>\n",
       "      <td>-3072</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.044478</td>\n",
       "      <td>0.399608</td>\n",
       "      <td>0.176049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area code  votes  median_age  perc_unemployed  perc_high_education  \\\n",
       "0  E06000031 -19040        35.0         0.050568             0.310281   \n",
       "1  E06000032 -11065        32.0         0.057414             0.333318   \n",
       "2  E06000033 -15174        40.0         0.049669             0.332425   \n",
       "3  E06000034 -35614        36.0         0.054239             0.283631   \n",
       "4  E06000055  -3072        39.0         0.044478             0.399608   \n",
       "\n",
       "   perc_born_outside_uk  \n",
       "0              0.206349  \n",
       "1              0.309408  \n",
       "2              0.098596  \n",
       "3              0.119692  \n",
       "4              0.176049  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = votes.merge(age, on='Area code').merge(unemployment, on='Area code').merge(education, on='Area code').merge(outside_uk, on='Area code')\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Important note, not all area codes of the votes csv data were in the census data, but I was able to match 89,5%, not too bad:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.52879581151832"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 - ((len(votes) - len(data)) / len(votes)*100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use scatter plots to show the relations. Defining a function to avoid too much repetition of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def showplot(data, factor):\n",
    "    fig = plt.figure(figsize=(6, 6))\n",
    "    ax = fig.add_subplot(1,1,1) \n",
    "    ax.scatter(data[\"votes\"], data[factor], s=20, c='r', marker=\"o\", label=factor)\n",
    "    plt.legend(loc='upper left');\n",
    "    plt.xlabel(\"votes (< 0 = leave / > 0 = remain)\")\n",
    "    plt.ylabel(factor)\n",
    "    plt.axvline(x=0)\n",
    "    plt.axhline(y=data[factor].mean()) # draw a horizontal line in the mean of the dataset\n",
    "    plt.show()\n",
    "    #fig.savefig(factor+\".png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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USg1Ii2rGSJNqSKVSbNu/f1xMysi+fS13LERKpQakRTVjpEk1pI/DGxMJFgEnAXs7Ovi3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\niIiURQ2IiIiURQ2IiIiURQ2IiIiU5f8HbyCw1hlwjGgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150fed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showplot(data, \"median_age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems more older people want to leave the EU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Unemployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I0GhUxtjNaDVDhkhdJJJ181UleDTqOSiEsOlsNPAlLM/lp8lPEEZ6sC2vwi4E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\nWa9+AbGYOwdb2my2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118249d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showplot(data, \"perc_unemployed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The higher the unemployment rate, the higher the desire to leave."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Higher education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QzSDFHo9s27Yt4euHxiaGFxTIPUuXqqAoScUtQUkky6s/Yk6nROQFETleRCaL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\nCIKgEv4LKiFXDl/kggYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115428668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showplot(data, \"perc_high_education\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Areas with a higher percentage of education tend to be in favor of remain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Born outside UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RpGRUD8etyDnRXXxJVbfnxKpBYkEDRraxoIHMsblChUfoskWLSAuwHvg/4Hbg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\npwEAWggOAC0EB4AWggNAC8EBoMUXY5GJSgefr4EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103425668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showplot(data, \"perc_born_outside_uk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that the greater % of an area born outside UK is more in favor of remain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last but not least I wanted to find out how the income level influenced the voting. I found [this source](https://www.gov.uk/government/statistics/income-and-tax-by-county-and-region-2010-to-2011) but it is per region (not area) so I had to group the voting data by region as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Remain</th>\n",
       "      <th>Leave</th>\n",
       "      <th>votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>1448616</td>\n",
       "      <td>1880367</td>\n",
       "      <td>-431751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Midlands</th>\n",
       "      <td>1033036</td>\n",
       "      <td>1475479</td>\n",
       "      <td>-442443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Remain    Leave   votes\n",
       "Region                                 \n",
       "East           1448616  1880367 -431751\n",
       "East Midlands  1033036  1475479 -442443"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes_region = votes_org.groupby(\"Region\").sum()\n",
    "votes_region.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(votes_region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>22100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>20400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyne &amp; Wear</th>\n",
       "      <td>20400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unitary Authorities</th>\n",
       "      <td>20400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>20600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        median_income\n",
       "Region                                               \n",
       "England                                       22100.0\n",
       "North East                                    20400.0\n",
       "Tyne & Wear                                   20400.0\n",
       "Unitary Authorities                           20400.0\n",
       "North West                                    20600.0"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the median income per area / region (they are in the same column but merge later will fix that)\n",
    "income_url = \"https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/510864/Table_3_13_14.xlsx\"\n",
    "income = pd.read_excel(io=income_url, sheetname=\"Table_3_13_14\", header=10, parse_cols=(\"A,U\"), skiprows=[11,12,13], )\n",
    "income.dropna(how='all', inplace=True)\n",
    "income.rename(index=str, columns={income.columns[0]: \"Region\", income.columns[1]: \"median_income\"}, inplace=True)\n",
    "income.set_index(\"Region\", inplace=True)\n",
    "income.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['East', 'East Midlands', 'London', 'North East', 'North West',\n",
       "       'Northern Ireland', 'Scotland', 'South East', 'South West', 'Wales',\n",
       "       'West Midlands', 'Yorkshire and The Humber'],\n",
       "      dtype='object', name='Region')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes_region.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# income data has whitespace so merge did not match, clean the whitespaces\n",
    "income.index = income.index.str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['East', 'Yorkshire and The Humber']"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we miss 2 regions, accepting that for now (want to get a 'rough' indication)\n",
    "[i for i in votes_region.index if i not in income.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Remain</th>\n",
       "      <th>Leave</th>\n",
       "      <th>votes</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Midlands</th>\n",
       "      <td>1033036</td>\n",
       "      <td>1475479</td>\n",
       "      <td>-442443</td>\n",
       "      <td>20800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>2263519</td>\n",
       "      <td>1513232</td>\n",
       "      <td>750287</td>\n",
       "      <td>25600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North East</th>\n",
       "      <td>562595</td>\n",
       "      <td>778103</td>\n",
       "      <td>-215508</td>\n",
       "      <td>20400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North West</th>\n",
       "      <td>1699020</td>\n",
       "      <td>1966925</td>\n",
       "      <td>-267905</td>\n",
       "      <td>20600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern Ireland</th>\n",
       "      <td>440707</td>\n",
       "      <td>349442</td>\n",
       "      <td>91265</td>\n",
       "      <td>20100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scotland</th>\n",
       "      <td>1661191</td>\n",
       "      <td>1018322</td>\n",
       "      <td>642869</td>\n",
       "      <td>21800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South East</th>\n",
       "      <td>2391718</td>\n",
       "      <td>2567965</td>\n",
       "      <td>-176247</td>\n",
       "      <td>23900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South West</th>\n",
       "      <td>1503019</td>\n",
       "      <td>1669711</td>\n",
       "      <td>-166692</td>\n",
       "      <td>21200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wales</th>\n",
       "      <td>772347</td>\n",
       "      <td>854572</td>\n",
       "      <td>-82225</td>\n",
       "      <td>20300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Midlands</th>\n",
       "      <td>1207175</td>\n",
       "      <td>1755687</td>\n",
       "      <td>-548512</td>\n",
       "      <td>20500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Remain    Leave   votes  median_income\n",
       "Region                                                   \n",
       "East Midlands     1033036  1475479 -442443        20800.0\n",
       "London            2263519  1513232  750287        25600.0\n",
       "North East         562595   778103 -215508        20400.0\n",
       "North West        1699020  1966925 -267905        20600.0\n",
       "Northern Ireland   440707   349442   91265        20100.0\n",
       "Scotland          1661191  1018322  642869        21800.0\n",
       "South East        2391718  2567965 -176247        23900.0\n",
       "South West        1503019  1669711 -166692        21200.0\n",
       "Wales              772347   854572  -82225        20300.0\n",
       "West Midlands     1207175  1755687 -548512        20500.0"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the votes and income data sets on their region indexes\n",
    "votes_and_income = votes_region.merge(income, left_index=True, right_index=True, how='left')\n",
    "votes_and_income.drop_duplicates(subset=\"Remain\", keep=\"first\", inplace=True)\n",
    "votes_and_income = votes_and_income[votes_and_income[\"median_income\"] > 0] # clean 2 NANs\n",
    "votes_and_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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7772XefPmARXDx2PGjOHRRx/ltddeo7S0lDvuuKPyWMO/9S12Nm/OMOA/qqljPhWBb9yE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1156d3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cannot reuse earlier showplot method, so tweak the code here\n",
    "# had to add FuncFormatter as well because the x axis has much greater vote values, because we measure \n",
    "# regions (=bigger) instead of areas (=smaller) here\n",
    "factor = \"median_income\"\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.scatter(votes_and_income[\"votes\"], votes_and_income[factor], s=100, c='r', marker=\"o\", label=factor)\n",
    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda y, pos: ('%2d')%(y*1e-3)))\n",
    "# show region name for each dot\n",
    "for i, txt in enumerate(votes_and_income.index):\n",
    "    ax.annotate(txt, (votes_and_income[\"votes\"][i], votes_and_income[factor][i]), \n",
    "               horizontalalignment='right', verticalalignment='top',\n",
    "               bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=0.2'))\n",
    "plt.legend(loc='upper left');\n",
    "plt.xlabel(\"votes in 1000s (< 0 = leave / > 0 = remain)\")\n",
    "plt.ylabel(factor)\n",
    "plt.axvline(x=0)\n",
    "\n",
    "plt.axhline(y=votes_and_income[factor].mean()) # draw a horizontal line in the mean of the dataset\n",
    "plt.show()\n",
    "#fig.savefig(factor+\".png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: we clearly see that regions with a relatively smaller median income are more in favor of leaving the EU, although it is not 100% consistent: Scotland has a lower median income but voted Remain, South East has a higher median income but wants to leave. Interesting though how general trends become visible by merging different data sets."
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}