Spikes map - Workflow MAYORAL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'geopandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-b8657b7de07a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeojsonio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgeopandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGeoSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGeoDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshapely\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'geopandas'"
     ]
    }
   ],
   "source": [
    "# Libraries\n",
    "import pandas as pd \n",
    "import geojsonio\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "from geopandas import GeoSeries, GeoDataFrame\n",
    "import shapely\n",
    "from shapely.geometry import shape, Point\n",
    "from pyproj import Proj, transform\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import results data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting geopandas\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5b/0c/e6c99e561b03482220f00443f610ccf4dce9b50f4b1093d735f93c6fc8c6/geopandas-0.6.2-py2.py3-none-any.whl (919kB)\n",
      "\u001b[K     |████████████████████████████████| 921kB 1.3MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting shapely (from geopandas)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/32/9d1cbfdc94864282be7745a7f7679ca16beb12ec5d71efd12a3f6cc0690a/Shapely-1.6.4.post2-cp37-cp37m-macosx_10_9_x86_64.whl (1.6MB)\n",
      "\u001b[K     |████████████████████████████████| 1.6MB 1.8MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting pyproj (from geopandas)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/81/40/5e9c75b1b08fc5f91fb9f68afacd15144296a6cd58a8b3b0a8a23a56f181/pyproj-2.4.2.post1-cp37-cp37m-macosx_10_6_intel.whl (13.4MB)\n",
      "\u001b[K     |████████████████████████████████| 13.4MB 5.0MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting fiona (from geopandas)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/9a/3cb5728fca91a642fdb1107dcb30c97ecffe25c841a7a4cfece680fd802a/Fiona-1.8.13-cp37-cp37m-macosx_10_6_intel.whl (17.9MB)\n",
      "\u001b[K     |████████████████████████████████| 17.9MB 5.0MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pandas>=0.23.0 in /opt/anaconda3/lib/python3.7/site-packages (from geopandas) (0.25.1)\n",
      "Collecting cligj>=0.5 (from fiona->geopandas)\n",
      "  Downloading https://files.pythonhosted.org/packages/e4/be/30a58b4b0733850280d01f8bd132591b4668ed5c7046761098d665ac2174/cligj-0.5.0-py3-none-any.whl\n",
      "Collecting click-plugins>=1.0 (from fiona->geopandas)\n",
      "  Downloading https://files.pythonhosted.org/packages/e9/da/824b92d9942f4e472702488857914bdd50f73021efea15b4cad9aca8ecef/click_plugins-1.1.1-py2.py3-none-any.whl\n",
      "Requirement already satisfied: attrs>=17 in /opt/anaconda3/lib/python3.7/site-packages (from fiona->geopandas) (19.2.0)\n",
      "Requirement already satisfied: click<8,>=4.0 in /opt/anaconda3/lib/python3.7/site-packages (from fiona->geopandas) (7.0)\n",
      "Requirement already satisfied: six>=1.7 in /opt/anaconda3/lib/python3.7/site-packages (from fiona->geopandas) (1.12.0)\n",
      "Collecting munch (from fiona->geopandas)\n",
      "  Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: pytz>=2017.2 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.23.0->geopandas) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.23.0->geopandas) (2.8.0)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.23.0->geopandas) (1.17.2)\n",
      "Installing collected packages: shapely, pyproj, cligj, click-plugins, munch, fiona, geopandas\n",
      "Successfully installed click-plugins-1.1.1 cligj-0.5.0 fiona-1.8.13 geopandas-0.6.2 munch-2.5.0 pyproj-2.4.2.post1 shapely-1.6.4.post2\n"
     ]
    }
   ],
   "source": [
    "!pip install geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Turnout2012</th>\n",
       "      <th>LAB2012</th>\n",
       "      <th>CON2012</th>\n",
       "      <th>Turnout2016</th>\n",
       "      <th>LAB2016</th>\n",
       "      <th>CON2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>46135</td>\n",
       "      <td>22718</td>\n",
       "      <td>10410</td>\n",
       "      <td>39307</td>\n",
       "      <td>20965</td>\n",
       "      <td>12006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>114626</td>\n",
       "      <td>39109</td>\n",
       "      <td>54580</td>\n",
       "      <td>105544</td>\n",
       "      <td>32051</td>\n",
       "      <td>57655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>75537</td>\n",
       "      <td>19383</td>\n",
       "      <td>36923</td>\n",
       "      <td>67804</td>\n",
       "      <td>17594</td>\n",
       "      <td>39040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brent</td>\n",
       "      <td>92197</td>\n",
       "      <td>45623</td>\n",
       "      <td>26895</td>\n",
       "      <td>75883</td>\n",
       "      <td>39629</td>\n",
       "      <td>23855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>116007</td>\n",
       "      <td>28751</td>\n",
       "      <td>60510</td>\n",
       "      <td>102810</td>\n",
       "      <td>19926</td>\n",
       "      <td>65904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough  Turnout2012  LAB2012  CON2012  Turnout2016  LAB2016  \\\n",
       "0  Barking & Dagenham        46135    22718    10410        39307    20965   \n",
       "1              Barnet       114626    39109    54580       105544    32051   \n",
       "2              Bexley        75537    19383    36923        67804    17594   \n",
       "3               Brent        92197    45623    26895        75883    39629   \n",
       "4             Bromley       116007    28751    60510       102810    19926   \n",
       "\n",
       "   CON2016  \n",
       "0    12006  \n",
       "1    57655  \n",
       "2    39040  \n",
       "3    23855  \n",
       "4    65904  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.read_csv('mayoral.csv')\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate \"deltas\" (percent) to display the variation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Turnout2012</th>\n",
       "      <th>LAB2012</th>\n",
       "      <th>CON2012</th>\n",
       "      <th>Turnout2016</th>\n",
       "      <th>LAB2016</th>\n",
       "      <th>CON2016</th>\n",
       "      <th>LABdeltaPC</th>\n",
       "      <th>CONdeltaPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>46135</td>\n",
       "      <td>22718</td>\n",
       "      <td>10410</td>\n",
       "      <td>39307</td>\n",
       "      <td>20965</td>\n",
       "      <td>12006</td>\n",
       "      <td>4.094115</td>\n",
       "      <td>7.979964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>114626</td>\n",
       "      <td>39109</td>\n",
       "      <td>54580</td>\n",
       "      <td>105544</td>\n",
       "      <td>32051</td>\n",
       "      <td>57655</td>\n",
       "      <td>-3.751357</td>\n",
       "      <td>7.010782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>75537</td>\n",
       "      <td>19383</td>\n",
       "      <td>36923</td>\n",
       "      <td>67804</td>\n",
       "      <td>17594</td>\n",
       "      <td>39040</td>\n",
       "      <td>0.288049</td>\n",
       "      <td>8.697043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brent</td>\n",
       "      <td>92197</td>\n",
       "      <td>45623</td>\n",
       "      <td>26895</td>\n",
       "      <td>75883</td>\n",
       "      <td>39629</td>\n",
       "      <td>23855</td>\n",
       "      <td>2.739562</td>\n",
       "      <td>2.265322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>116007</td>\n",
       "      <td>28751</td>\n",
       "      <td>60510</td>\n",
       "      <td>102810</td>\n",
       "      <td>19926</td>\n",
       "      <td>65904</td>\n",
       "      <td>-5.402466</td>\n",
       "      <td>11.942068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough  Turnout2012  LAB2012  CON2012  Turnout2016  LAB2016  \\\n",
       "0  Barking & Dagenham        46135    22718    10410        39307    20965   \n",
       "1              Barnet       114626    39109    54580       105544    32051   \n",
       "2              Bexley        75537    19383    36923        67804    17594   \n",
       "3               Brent        92197    45623    26895        75883    39629   \n",
       "4             Bromley       116007    28751    60510       102810    19926   \n",
       "\n",
       "   CON2016  LABdeltaPC  CONdeltaPC  \n",
       "0    12006    4.094115    7.979964  \n",
       "1    57655   -3.751357    7.010782  \n",
       "2    39040    0.288049    8.697043  \n",
       "3    23855    2.739562    2.265322  \n",
       "4    65904   -5.402466   11.942068  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['LABdeltaPC'] = (results['LAB2016']/results['Turnout2016'] - results['LAB2012']/results['Turnout2012']) * 100\n",
    "results['CONdeltaPC'] = (results['CON2016']/results['Turnout2016'] - results['CON2012']/results['Turnout2012']) * 100\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the map and plot it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114fc07b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2tdppa7Cwu8nGiSEXfQ4f66tMVJq19Ez6OD3i4fy4l+ODrpQ2mtG4RHVZdibk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114fa8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assumption: you have a map in geojson format\n",
    "# If you have a shapefile, you can convert it, e.g.\n",
    "# ogr2ogr -f GeoJSON countries.json ne_10m_admin_0_countries_lakes.shp\n",
    "# (http://www.tnoda.com/blog/2013-12-07)\n",
    "# Other useful tips:\n",
    "# https://gis.stackexchange.com/questions/78838/converting-projected-coordinates-to-lat-lon-using-python\n",
    "# https://stackoverflow.com/questions/39620105/converting-between-projections-using-pyproj-in-pandas-dataframe\n",
    "# https://github.com/geohackweek/tutorial_contents/blob/master/vector/notebooks/geopandas_intro.ipynb\n",
    "\n",
    "mapdf = gpd.read_file('maps/councils.json')\n",
    "mapdf.plot() # If this has worked, it should draw a map of london!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# See what a geographical data frame looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>GSS_CODE</th>\n",
       "      <th>HECTARES</th>\n",
       "      <th>NONLD_AREA</th>\n",
       "      <th>ONS_INNER</th>\n",
       "      <th>SUB_2009</th>\n",
       "      <th>SUB_2006</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kingston upon Thames</td>\n",
       "      <td>E09000021</td>\n",
       "      <td>3726.117</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((516401.6 160201.8, 516407.3 160210.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Croydon</td>\n",
       "      <td>E09000008</td>\n",
       "      <td>8649.441</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((535009.2 159504.7, 535005.5 159502, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>E09000006</td>\n",
       "      <td>15013.487</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((540373.6 157530.4, 540361.2 157551.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hounslow</td>\n",
       "      <td>E09000018</td>\n",
       "      <td>5658.541</td>\n",
       "      <td>60.755</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((521975.8 178100, 521967.7 178096.8, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ealing</td>\n",
       "      <td>E09000009</td>\n",
       "      <td>5554.428</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((510253.5 182881.6, 510249.9 182886, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   NAME   GSS_CODE   HECTARES  NONLD_AREA ONS_INNER SUB_2009  \\\n",
       "0  Kingston upon Thames  E09000021   3726.117       0.000         F            \n",
       "1               Croydon  E09000008   8649.441       0.000         F            \n",
       "2               Bromley  E09000006  15013.487       0.000         F            \n",
       "3              Hounslow  E09000018   5658.541      60.755         F            \n",
       "4                Ealing  E09000009   5554.428       0.000         F            \n",
       "\n",
       "  SUB_2006                                           geometry  \n",
       "0           POLYGON ((516401.6 160201.8, 516407.3 160210.5...  \n",
       "1           POLYGON ((535009.2 159504.7, 535005.5 159502, ...  \n",
       "2           POLYGON ((540373.6 157530.4, 540361.2 157551.9...  \n",
       "3           POLYGON ((521975.8 178100, 521967.7 178096.8, ...  \n",
       "4           POLYGON ((510253.5 182881.6, 510249.9 182886, ...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapdf.head()\n",
    "# You should see a number of parameters; what matters is the presence of a unique value (GSS_CODE, but also NAME in this case)\n",
    "# and a column called \"geometry\", that contains the councils polygons\n",
    "# We'll need to match councils['NAME'] with results['Borough'] - but we'll see that in a minute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We want to position the triangle around the centroid of each council polygon, so we add longitude and latitude of each centroid "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>GSS_CODE</th>\n",
       "      <th>HECTARES</th>\n",
       "      <th>NONLD_AREA</th>\n",
       "      <th>ONS_INNER</th>\n",
       "      <th>SUB_2009</th>\n",
       "      <th>SUB_2006</th>\n",
       "      <th>geometry</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kingston upon Thames</td>\n",
       "      <td>E09000021</td>\n",
       "      <td>3726.117</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((516401.6 160201.8, 516407.3 160210.5...</td>\n",
       "      <td>519297.634807</td>\n",
       "      <td>166820.011490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Croydon</td>\n",
       "      <td>E09000008</td>\n",
       "      <td>8649.441</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((535009.2 159504.7, 535005.5 159502, ...</td>\n",
       "      <td>533290.153919</td>\n",
       "      <td>163541.243574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>E09000006</td>\n",
       "      <td>15013.487</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((540373.6 157530.4, 540361.2 157551.9...</td>\n",
       "      <td>542895.511607</td>\n",
       "      <td>165655.521340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hounslow</td>\n",
       "      <td>E09000018</td>\n",
       "      <td>5658.541</td>\n",
       "      <td>60.755</td>\n",
       "      <td>F</td>\n",
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       "      <td></td>\n",
       "      <td>POLYGON ((521975.8 178100, 521967.7 178096.8, ...</td>\n",
       "      <td>513515.505536</td>\n",
       "      <td>175643.224320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ealing</td>\n",
       "      <td>E09000009</td>\n",
       "      <td>5554.428</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((510253.5 182881.6, 510249.9 182886, ...</td>\n",
       "      <td>515887.937770</td>\n",
       "      <td>181715.495617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   NAME   GSS_CODE   HECTARES  NONLD_AREA ONS_INNER SUB_2009  \\\n",
       "0  Kingston upon Thames  E09000021   3726.117       0.000         F            \n",
       "1               Croydon  E09000008   8649.441       0.000         F            \n",
       "2               Bromley  E09000006  15013.487       0.000         F            \n",
       "3              Hounslow  E09000018   5658.541      60.755         F            \n",
       "4                Ealing  E09000009   5554.428       0.000         F            \n",
       "\n",
       "  SUB_2006                                           geometry            lon  \\\n",
       "0           POLYGON ((516401.6 160201.8, 516407.3 160210.5...  519297.634807   \n",
       "1           POLYGON ((535009.2 159504.7, 535005.5 159502, ...  533290.153919   \n",
       "2           POLYGON ((540373.6 157530.4, 540361.2 157551.9...  542895.511607   \n",
       "3           POLYGON ((521975.8 178100, 521967.7 178096.8, ...  513515.505536   \n",
       "4           POLYGON ((510253.5 182881.6, 510249.9 182886, ...  515887.937770   \n",
       "\n",
       "             lat  \n",
       "0  166820.011490  \n",
       "1  163541.243574  \n",
       "2  165655.521340  \n",
       "3  175643.224320  \n",
       "4  181715.495617  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add lon/lat of centroid to our polygons \n",
    "data = dict()\n",
    "data['name'] = []\n",
    "data['lat'] = []\n",
    "data['lon'] = []\n",
    "i = 0\n",
    "\n",
    "# We iterate over each geometry\n",
    "for geom in mapdf['geometry']:\n",
    "    # calculate the centroid coordinates\n",
    "    centroid = shape(geom).centroid.coords\n",
    "    # centroid = shape(geom).centroid.wkt\n",
    "    lon = centroid[0][0] \n",
    "    lat = centroid[0][1]\n",
    "    # data['name'].append(i)\n",
    "    data['lon'].append(lon)\n",
    "    data['lat'].append(lat)\n",
    "    \n",
    "mapdf['lon'] = data['lon']\n",
    "mapdf['lat'] = data['lat']\n",
    "\n",
    "mapdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The two data frames use the same unique names for councils, so we can merge them into one by using the column NAME on mapdf and Borough on results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>GSS_CODE</th>\n",
       "      <th>HECTARES</th>\n",
       "      <th>NONLD_AREA</th>\n",
       "      <th>ONS_INNER</th>\n",
       "      <th>SUB_2009</th>\n",
       "      <th>SUB_2006</th>\n",
       "      <th>geometry</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Turnout2012</th>\n",
       "      <th>LAB2012</th>\n",
       "      <th>CON2012</th>\n",
       "      <th>Turnout2016</th>\n",
       "      <th>LAB2016</th>\n",
       "      <th>CON2016</th>\n",
       "      <th>LABdeltaPC</th>\n",
       "      <th>CONdeltaPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kingston upon Thames</td>\n",
       "      <td>E09000021</td>\n",
       "      <td>3726.117</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((516401.6 160201.8, 516407.3 160210.5...</td>\n",
       "      <td>519297.634807</td>\n",
       "      <td>166820.011490</td>\n",
       "      <td>Kingston upon Thames</td>\n",
       "      <td>59422.0</td>\n",
       "      <td>17174.0</td>\n",
       "      <td>27609.0</td>\n",
       "      <td>48024.0</td>\n",
       "      <td>11963.0</td>\n",
       "      <td>25849.0</td>\n",
       "      <td>-3.991292</td>\n",
       "      <td>7.362581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Croydon</td>\n",
       "      <td>E09000008</td>\n",
       "      <td>8649.441</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((535009.2 159504.7, 535005.5 159502, ...</td>\n",
       "      <td>533290.153919</td>\n",
       "      <td>163541.243574</td>\n",
       "      <td>Croydon</td>\n",
       "      <td>116696.0</td>\n",
       "      <td>46048.0</td>\n",
       "      <td>46794.0</td>\n",
       "      <td>100173.0</td>\n",
       "      <td>37300.0</td>\n",
       "      <td>46997.0</td>\n",
       "      <td>-2.224211</td>\n",
       "      <td>6.816775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>E09000006</td>\n",
       "      <td>15013.487</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((540373.6 157530.4, 540361.2 157551.9...</td>\n",
       "      <td>542895.511607</td>\n",
       "      <td>165655.521340</td>\n",
       "      <td>Bromley</td>\n",
       "      <td>116007.0</td>\n",
       "      <td>28751.0</td>\n",
       "      <td>60510.0</td>\n",
       "      <td>102810.0</td>\n",
       "      <td>19926.0</td>\n",
       "      <td>65904.0</td>\n",
       "      <td>-5.402466</td>\n",
       "      <td>11.942068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hounslow</td>\n",
       "      <td>E09000018</td>\n",
       "      <td>5658.541</td>\n",
       "      <td>60.755</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((521975.8 178100, 521967.7 178096.8, ...</td>\n",
       "      <td>513515.505536</td>\n",
       "      <td>175643.224320</td>\n",
       "      <td>Hounslow</td>\n",
       "      <td>78754.0</td>\n",
       "      <td>33277.0</td>\n",
       "      <td>27779.0</td>\n",
       "      <td>62516.0</td>\n",
       "      <td>26403.0</td>\n",
       "      <td>26247.0</td>\n",
       "      <td>-0.020374</td>\n",
       "      <td>6.711323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ealing</td>\n",
       "      <td>E09000009</td>\n",
       "      <td>5554.428</td>\n",
       "      <td>0.000</td>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((510253.5 182881.6, 510249.9 182886, ...</td>\n",
       "      <td>515887.937770</td>\n",
       "      <td>181715.495617</td>\n",
       "      <td>Ealing</td>\n",
       "      <td>116300.0</td>\n",
       "      <td>53907.0</td>\n",
       "      <td>35609.0</td>\n",
       "      <td>93123.0</td>\n",
       "      <td>41217.0</td>\n",
       "      <td>36291.0</td>\n",
       "      <td>-2.090860</td>\n",
       "      <td>8.352810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   NAME   GSS_CODE   HECTARES  NONLD_AREA ONS_INNER SUB_2009  \\\n",
       "0  Kingston upon Thames  E09000021   3726.117       0.000         F            \n",
       "1               Croydon  E09000008   8649.441       0.000         F            \n",
       "2               Bromley  E09000006  15013.487       0.000         F            \n",
       "3              Hounslow  E09000018   5658.541      60.755         F            \n",
       "4                Ealing  E09000009   5554.428       0.000         F            \n",
       "\n",
       "  SUB_2006                                           geometry            lon  \\\n",
       "0           POLYGON ((516401.6 160201.8, 516407.3 160210.5...  519297.634807   \n",
       "1           POLYGON ((535009.2 159504.7, 535005.5 159502, ...  533290.153919   \n",
       "2           POLYGON ((540373.6 157530.4, 540361.2 157551.9...  542895.511607   \n",
       "3           POLYGON ((521975.8 178100, 521967.7 178096.8, ...  513515.505536   \n",
       "4           POLYGON ((510253.5 182881.6, 510249.9 182886, ...  515887.937770   \n",
       "\n",
       "             lat               Borough  Turnout2012  LAB2012  CON2012  \\\n",
       "0  166820.011490  Kingston upon Thames      59422.0  17174.0  27609.0   \n",
       "1  163541.243574               Croydon     116696.0  46048.0  46794.0   \n",
       "2  165655.521340               Bromley     116007.0  28751.0  60510.0   \n",
       "3  175643.224320              Hounslow      78754.0  33277.0  27779.0   \n",
       "4  181715.495617                Ealing     116300.0  53907.0  35609.0   \n",
       "\n",
       "   Turnout2016  LAB2016  CON2016  LABdeltaPC  CONdeltaPC  \n",
       "0      48024.0  11963.0  25849.0   -3.991292    7.362581  \n",
       "1     100173.0  37300.0  46997.0   -2.224211    6.816775  \n",
       "2     102810.0  19926.0  65904.0   -5.402466   11.942068  \n",
       "3      62516.0  26403.0  26247.0   -0.020374    6.711323  \n",
       "4      93123.0  41217.0  36291.0   -2.090860    8.352810  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the wards dataset with the results dataset for ease\n",
    "df = pd.merge(mapdf, results, how = 'left', left_on = 'NAME', right_on = 'Borough')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time to draw our triangles. In a few steps:\n",
    "### - we set the size of the triangles (you will need to play around with these)\n",
    "### - we calculate the base vertex to be around the centroid\n",
    "### - we calculate the height\n",
    "### - we calculate each edge, to be drawn as a 'patch'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Which variable are we drawing, and which colour we want it to be\n",
    "TRIANGLE_COLOUR = 'blue' \n",
    "COLUMN_NAME = 'CONdeltaPC'\n",
    "\n",
    "# Settings\n",
    "HALF_BASE = 250\n",
    "HEIGHT_MULTIPLIER = 500\n",
    "POLYGON_FILL_COLOUR = 'white'\n",
    "POLYGON_EDGE_COLOUR = 'darkgrey'\n",
    "\n",
    "# Calculate vertex of the triangles\n",
    "df['triangleL'] = df['lon'] - HALF_BASE\n",
    "df['triangleR'] = df['lon'] + HALF_BASE\n",
    "df['triangleH'] = df['lat'] + HEIGHT_MULTIPLIER * df[COLUMN_NAME]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3dzejo6McOXKEwsLCDQdqPp8Pn8/Hb3/7W5KSklheXmZ8fBydTofX62VkZITM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\nBi6KVjYajWm30cqSerKqNIns7e0hJ5PyIZMMSZJnHuTEFIn6jNOo9/gQ/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DQtToIuMvn5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\n4sD/B46twgy7mR5DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115810278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the vertex, calculate the edges\n",
    "pointsX = []\n",
    "pointsY = []\n",
    "# patches are basically the edges \n",
    "patches = []\n",
    "\n",
    "\n",
    "# calculate triangles and turn them into patches\n",
    "for index, row in df.iterrows():\n",
    "    L = row['triangleL']\n",
    "    R = row['triangleR']\n",
    "    H = row['triangleH']\n",
    "    lat = row['lat']\n",
    "    lon = row['lon']\n",
    "    \n",
    "    pointx1 = L\n",
    "    pointx2 = R\n",
    "    pointx3 = lon\n",
    "    pointy1 = lat\n",
    "    pointy2 = lat\n",
    "    pointy3 = H\n",
    "    \n",
    "    pointsX.append(L)\n",
    "    pointsX.append(R)\n",
    "    pointsX.append(lon)\n",
    "    \n",
    "    pointsY.append(lat)\n",
    "    pointsY.append(lat)\n",
    "    pointsY.append(H)\n",
    "    \n",
    "    \n",
    "    patch = plt.Polygon([[L,lat], [R,lat], [lon,H]], color=TRIANGLE_COLOUR)\n",
    "    patches.append(patch)\n",
    "\n",
    "    # You could print the vertex - I have no need to show, so setting s=0\n",
    "    # ax.scatter(pointsX, pointsY, s = 0, color = 'white')   \n",
    "\n",
    "# Let's draw the underlying borough map\n",
    "ax = mapdf.plot(figsize=(15,10), color=POLYGON_FILL_COLOUR, edgecolor=POLYGON_EDGE_COLOUR)\n",
    "    \n",
    "# Finally, draw the patches\n",
    "for patch in patches:\n",
    "    ax.add_patch(patch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Contains National Statistics data © Crown copyright and database right [2015]\n",
    "# Contains Ordnance Survey data © Crown copyright and database right [2015]"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}