Drive_vs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playing with Travel\n",
    "* In this notebook we explore Driving as a mode of transport against other forms of transport. \n",
    "* We begin by converting the travel data into propotions of the overall travel for each Local Authority (LA). \n",
    "* We then consider only the LAs where driving makes up less than 50% of the travel. \n",
    "___\n",
    "___\n",
    "## First let's convert the travel data to proportions \n",
    "* The sum of each row should be 1. \n",
    "* See the new column Row_Sum which confirms this to be the case.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Home</th>\n",
       "      <th>Underground</th>\n",
       "      <th>Train</th>\n",
       "      <th>Bus</th>\n",
       "      <th>Taxi</th>\n",
       "      <th>Motorcycle</th>\n",
       "      <th>Drive</th>\n",
       "      <th>Passenger</th>\n",
       "      <th>Bike</th>\n",
       "      <th>Walk</th>\n",
       "      <th>Other</th>\n",
       "      <th>Row_Sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>County Durham</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.090621</td>\n",
       "      <td>0.001417</td>\n",
       "      <td>0.008184</td>\n",
       "      <td>0.060256</td>\n",
       "      <td>0.006148</td>\n",
       "      <td>0.004555</td>\n",
       "      <td>0.643475</td>\n",
       "      <td>0.076185</td>\n",
       "      <td>0.009676</td>\n",
       "      <td>0.094298</td>\n",
       "      <td>0.005187</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darlington</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.085282</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.016893</td>\n",
       "      <td>0.068960</td>\n",
       "      <td>0.008222</td>\n",
       "      <td>0.003897</td>\n",
       "      <td>0.591280</td>\n",
       "      <td>0.068083</td>\n",
       "      <td>0.023483</td>\n",
       "      <td>0.128208</td>\n",
       "      <td>0.005019</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gateshead</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.069473</td>\n",
       "      <td>0.046475</td>\n",
       "      <td>0.007673</td>\n",
       "      <td>0.151387</td>\n",
       "      <td>0.004931</td>\n",
       "      <td>0.003962</td>\n",
       "      <td>0.546774</td>\n",
       "      <td>0.063454</td>\n",
       "      <td>0.014302</td>\n",
       "      <td>0.086703</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hartlepool</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.065480</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.012418</td>\n",
       "      <td>0.067678</td>\n",
       "      <td>0.017820</td>\n",
       "      <td>0.004634</td>\n",
       "      <td>0.605370</td>\n",
       "      <td>0.083591</td>\n",
       "      <td>0.018694</td>\n",
       "      <td>0.113988</td>\n",
       "      <td>0.009453</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middlesbrough</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.061177</td>\n",
       "      <td>0.000807</td>\n",
       "      <td>0.012796</td>\n",
       "      <td>0.089244</td>\n",
       "      <td>0.016536</td>\n",
       "      <td>0.003575</td>\n",
       "      <td>0.571159</td>\n",
       "      <td>0.085046</td>\n",
       "      <td>0.025208</td>\n",
       "      <td>0.124095</td>\n",
       "      <td>0.010358</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newcastle upon Tyne</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.070641</td>\n",
       "      <td>0.052826</td>\n",
       "      <td>0.011572</td>\n",
       "      <td>0.182076</td>\n",
       "      <td>0.008690</td>\n",
       "      <td>0.002891</td>\n",
       "      <td>0.453890</td>\n",
       "      <td>0.056195</td>\n",
       "      <td>0.027008</td>\n",
       "      <td>0.128227</td>\n",
       "      <td>0.005983</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Tyneside</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.072866</td>\n",
       "      <td>0.088757</td>\n",
       "      <td>0.014423</td>\n",
       "      <td>0.089622</td>\n",
       "      <td>0.007790</td>\n",
       "      <td>0.004905</td>\n",
       "      <td>0.547175</td>\n",
       "      <td>0.061973</td>\n",
       "      <td>0.024420</td>\n",
       "      <td>0.078718</td>\n",
       "      <td>0.009352</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northumberland</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.121810</td>\n",
       "      <td>0.004486</td>\n",
       "      <td>0.012607</td>\n",
       "      <td>0.051688</td>\n",
       "      <td>0.003560</td>\n",
       "      <td>0.004643</td>\n",
       "      <td>0.616510</td>\n",
       "      <td>0.063192</td>\n",
       "      <td>0.014118</td>\n",
       "      <td>0.099604</td>\n",
       "      <td>0.007781</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redcar and Cleveland</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.071583</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>0.020850</td>\n",
       "      <td>0.058807</td>\n",
       "      <td>0.006885</td>\n",
       "      <td>0.004525</td>\n",
       "      <td>0.643805</td>\n",
       "      <td>0.070554</td>\n",
       "      <td>0.015527</td>\n",
       "      <td>0.094669</td>\n",
       "      <td>0.012031</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Tyneside</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.059964</td>\n",
       "      <td>0.087911</td>\n",
       "      <td>0.016867</td>\n",
       "      <td>0.095850</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.005277</td>\n",
       "      <td>0.541781</td>\n",
       "      <td>0.058401</td>\n",
       "      <td>0.021603</td>\n",
       "      <td>0.088964</td>\n",
       "      <td>0.018693</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stockton-on-Tees</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.073185</td>\n",
       "      <td>0.001113</td>\n",
       "      <td>0.012798</td>\n",
       "      <td>0.057655</td>\n",
       "      <td>0.006807</td>\n",
       "      <td>0.004706</td>\n",
       "      <td>0.663357</td>\n",
       "      <td>0.069030</td>\n",
       "      <td>0.019433</td>\n",
       "      <td>0.081506</td>\n",
       "      <td>0.010411</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunderland</th>\n",
       "      <th>North East</th>\n",
       "      <td>0.063410</td>\n",
       "      <td>0.022730</td>\n",
       "      <td>0.007357</td>\n",
       "      <td>0.124688</td>\n",
       "      <td>0.006090</td>\n",
       "      <td>0.004279</td>\n",
       "      <td>0.581075</td>\n",
       "      <td>0.076997</td>\n",
       "      <td>0.013069</td>\n",
       "      <td>0.093078</td>\n",
       "      <td>0.007226</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allerdale</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.130762</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.008117</td>\n",
       "      <td>0.037547</td>\n",
       "      <td>0.002301</td>\n",
       "      <td>0.004948</td>\n",
       "      <td>0.603134</td>\n",
       "      <td>0.074117</td>\n",
       "      <td>0.015062</td>\n",
       "      <td>0.119281</td>\n",
       "      <td>0.004254</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barrow-in-Furness</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.064274</td>\n",
       "      <td>0.001056</td>\n",
       "      <td>0.016476</td>\n",
       "      <td>0.050965</td>\n",
       "      <td>0.013629</td>\n",
       "      <td>0.017340</td>\n",
       "      <td>0.535144</td>\n",
       "      <td>0.072848</td>\n",
       "      <td>0.051828</td>\n",
       "      <td>0.169626</td>\n",
       "      <td>0.006814</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blackburn with Darwen</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.081457</td>\n",
       "      <td>0.000694</td>\n",
       "      <td>0.015796</td>\n",
       "      <td>0.053533</td>\n",
       "      <td>0.018125</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>0.606381</td>\n",
       "      <td>0.081225</td>\n",
       "      <td>0.009781</td>\n",
       "      <td>0.123259</td>\n",
       "      <td>0.004544</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blackpool</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.100506</td>\n",
       "      <td>0.000651</td>\n",
       "      <td>0.008336</td>\n",
       "      <td>0.084241</td>\n",
       "      <td>0.009199</td>\n",
       "      <td>0.008971</td>\n",
       "      <td>0.534297</td>\n",
       "      <td>0.074537</td>\n",
       "      <td>0.031196</td>\n",
       "      <td>0.141731</td>\n",
       "      <td>0.006334</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolton</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.081756</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>0.035536</td>\n",
       "      <td>0.066468</td>\n",
       "      <td>0.012459</td>\n",
       "      <td>0.006420</td>\n",
       "      <td>0.621568</td>\n",
       "      <td>0.066379</td>\n",
       "      <td>0.010222</td>\n",
       "      <td>0.092691</td>\n",
       "      <td>0.004280</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burnley</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.077125</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>0.006241</td>\n",
       "      <td>0.083130</td>\n",
       "      <td>0.008287</td>\n",
       "      <td>0.005559</td>\n",
       "      <td>0.611439</td>\n",
       "      <td>0.078436</td>\n",
       "      <td>0.010411</td>\n",
       "      <td>0.114730</td>\n",
       "      <td>0.004117</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bury</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.087339</td>\n",
       "      <td>0.048094</td>\n",
       "      <td>0.005941</td>\n",
       "      <td>0.064417</td>\n",
       "      <td>0.007838</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>0.628845</td>\n",
       "      <td>0.054739</td>\n",
       "      <td>0.013358</td>\n",
       "      <td>0.080581</td>\n",
       "      <td>0.003680</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carlisle</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.101422</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.005643</td>\n",
       "      <td>0.068512</td>\n",
       "      <td>0.003230</td>\n",
       "      <td>0.005457</td>\n",
       "      <td>0.569347</td>\n",
       "      <td>0.062683</td>\n",
       "      <td>0.025801</td>\n",
       "      <td>0.153042</td>\n",
       "      <td>0.004306</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheshire East</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.128837</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>0.027443</td>\n",
       "      <td>0.018036</td>\n",
       "      <td>0.002782</td>\n",
       "      <td>0.006454</td>\n",
       "      <td>0.645543</td>\n",
       "      <td>0.049648</td>\n",
       "      <td>0.025760</td>\n",
       "      <td>0.089888</td>\n",
       "      <td>0.004207</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheshire West and Chester</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.107270</td>\n",
       "      <td>0.001605</td>\n",
       "      <td>0.019446</td>\n",
       "      <td>0.034202</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>0.006251</td>\n",
       "      <td>0.648803</td>\n",
       "      <td>0.054747</td>\n",
       "      <td>0.026227</td>\n",
       "      <td>0.091602</td>\n",
       "      <td>0.004958</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chorley</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.108506</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>0.023481</td>\n",
       "      <td>0.029329</td>\n",
       "      <td>0.002514</td>\n",
       "      <td>0.006983</td>\n",
       "      <td>0.677405</td>\n",
       "      <td>0.054765</td>\n",
       "      <td>0.014171</td>\n",
       "      <td>0.078712</td>\n",
       "      <td>0.003501</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copeland</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.084582</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.015779</td>\n",
       "      <td>0.046578</td>\n",
       "      <td>0.004621</td>\n",
       "      <td>0.008270</td>\n",
       "      <td>0.634733</td>\n",
       "      <td>0.093825</td>\n",
       "      <td>0.016844</td>\n",
       "      <td>0.089933</td>\n",
       "      <td>0.004287</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eden</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.210153</td>\n",
       "      <td>0.000728</td>\n",
       "      <td>0.007502</td>\n",
       "      <td>0.021048</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.003132</td>\n",
       "      <td>0.559339</td>\n",
       "      <td>0.047740</td>\n",
       "      <td>0.011871</td>\n",
       "      <td>0.131131</td>\n",
       "      <td>0.004916</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fylde</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.117502</td>\n",
       "      <td>0.000927</td>\n",
       "      <td>0.012054</td>\n",
       "      <td>0.038800</td>\n",
       "      <td>0.004028</td>\n",
       "      <td>0.006810</td>\n",
       "      <td>0.641930</td>\n",
       "      <td>0.048971</td>\n",
       "      <td>0.027789</td>\n",
       "      <td>0.096349</td>\n",
       "      <td>0.004839</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Halton</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.064192</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.018775</td>\n",
       "      <td>0.065773</td>\n",
       "      <td>0.005575</td>\n",
       "      <td>0.006096</td>\n",
       "      <td>0.653733</td>\n",
       "      <td>0.069264</td>\n",
       "      <td>0.020685</td>\n",
       "      <td>0.090679</td>\n",
       "      <td>0.004568</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyndburn</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.079460</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.062429</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.006640</td>\n",
       "      <td>0.641125</td>\n",
       "      <td>0.071569</td>\n",
       "      <td>0.011058</td>\n",
       "      <td>0.104215</td>\n",
       "      <td>0.004223</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knowsley</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.057237</td>\n",
       "      <td>0.002626</td>\n",
       "      <td>0.055891</td>\n",
       "      <td>0.110324</td>\n",
       "      <td>0.019371</td>\n",
       "      <td>0.004458</td>\n",
       "      <td>0.562756</td>\n",
       "      <td>0.075894</td>\n",
       "      <td>0.015594</td>\n",
       "      <td>0.090272</td>\n",
       "      <td>0.005576</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lancaster</th>\n",
       "      <th>North West</th>\n",
       "      <td>0.105742</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.019926</td>\n",
       "      <td>0.058999</td>\n",
       "      <td>0.005427</td>\n",
       "      <td>0.008483</td>\n",
       "      <td>0.556118</td>\n",
       "      <td>0.060638</td>\n",
       "      <td>0.039741</td>\n",
       "      <td>0.138560</td>\n",
       "      <td>0.005523</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tewkesbury</th>\n",
       "      <th>South West</th>\n",
       "      <td>0.125289</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>0.044772</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.010880</td>\n",
       "      <td>0.646519</td>\n",
       "      <td>0.044748</td>\n",
       "      <td>0.042798</td>\n",
       "      <td>0.073849</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Torbay</th>\n",
       "      <th>South West</th>\n",
       "      <td>0.120414</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.043383</td>\n",
       "      <td>0.005552</td>\n",
       "      <td>0.015181</td>\n",
       "      <td>0.579386</td>\n",
       "      <td>0.059214</td>\n",
       "      <td>0.013688</td>\n",
       "      <td>0.147825</td>\n",
       "      <td>0.005588</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Torridge</th>\n",
       "      <th>South West</th>\n",
       "      <td>0.198286</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>0.028638</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.008843</td>\n",
       "      <td>0.576866</td>\n",
       "      <td>0.049997</td>\n",
       "      <td>0.012414</td>\n",
       "      <td>0.113870</td>\n",
       "      <td>0.005374</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Devon</th>\n",
       "      <th>South West</th>\n",
       "      <td>0.207797</td>\n",
       "      <td>0.001228</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.022028</td>\n",
       "      <td>0.003288</td>\n",
       "      <td>0.007329</td>\n",
       "      <td>0.577632</td>\n",
       "      <td>0.043699</td>\n",
       "      <td>0.010737</td>\n",
       "      <td>0.113149</td>\n",
       "      <td>0.006695</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Dorset</th>\n",
       "      <th>South West</th>\n",
       "      <td>0.178807</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.016450</td>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.008881</td>\n",
       "      <td>0.565800</td>\n",
       "      <td>0.043072</td>\n",
       "      <td>0.023778</td>\n",
       "      <td>0.141904</td>\n",
       "      <td>0.005688</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Somerset</th>\n",
       "      <th>South West</th>\n",
       "      <td>0.253012</td>\n",
       "      <td>0.001107</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.020449</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.006447</td>\n",
       "      <td>0.502572</td>\n",
       "      <td>0.041810</td>\n",
       "      <td>0.024162</td>\n",
       "      <td>0.138261</td>\n",
       "      <td>0.005340</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weymouth and Portland</th>\n",
       "      <th>South West</th>\n",
       "      <td>0.099610</td>\n",
       "      <td>0.000767</td>\n",
       "      <td>0.012372</td>\n",
       "      <td>0.068796</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>0.016307</td>\n",
       "      <td>0.564511</td>\n",
       "      <td>0.056158</td>\n",
       "      <td>0.033381</td>\n",
       "      <td>0.136893</td>\n",
       "      <td>0.007737</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiltshire</th>\n",
       "      <th>South West</th>\n",
       "      <td>0.136701</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>0.024518</td>\n",
       "      <td>0.026211</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.007918</td>\n",
       "      <td>0.609838</td>\n",
       "      <td>0.047472</td>\n",
       "      <td>0.027749</td>\n",
       "      <td>0.111531</td>\n",
       "      <td>0.004410</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anglesey</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.132299</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.006539</td>\n",
       "      <td>0.026125</td>\n",
       "      <td>0.003286</td>\n",
       "      <td>0.007131</td>\n",
       "      <td>0.661694</td>\n",
       "      <td>0.058460</td>\n",
       "      <td>0.014886</td>\n",
       "      <td>0.081003</td>\n",
       "      <td>0.007920</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blaenau Gwent</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.058676</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.007670</td>\n",
       "      <td>0.030222</td>\n",
       "      <td>0.005443</td>\n",
       "      <td>0.005267</td>\n",
       "      <td>0.694214</td>\n",
       "      <td>0.098194</td>\n",
       "      <td>0.004736</td>\n",
       "      <td>0.090983</td>\n",
       "      <td>0.004030</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bridgend</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.078568</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.024062</td>\n",
       "      <td>0.028110</td>\n",
       "      <td>0.005942</td>\n",
       "      <td>0.005109</td>\n",
       "      <td>0.690756</td>\n",
       "      <td>0.072969</td>\n",
       "      <td>0.008211</td>\n",
       "      <td>0.081082</td>\n",
       "      <td>0.004440</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caerphilly</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.069004</td>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.038070</td>\n",
       "      <td>0.038341</td>\n",
       "      <td>0.003897</td>\n",
       "      <td>0.005304</td>\n",
       "      <td>0.687061</td>\n",
       "      <td>0.077483</td>\n",
       "      <td>0.006130</td>\n",
       "      <td>0.070579</td>\n",
       "      <td>0.003613</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cardiff</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.075169</td>\n",
       "      <td>0.001228</td>\n",
       "      <td>0.032560</td>\n",
       "      <td>0.099910</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.004104</td>\n",
       "      <td>0.543605</td>\n",
       "      <td>0.050140</td>\n",
       "      <td>0.036281</td>\n",
       "      <td>0.148132</td>\n",
       "      <td>0.004642</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carmarthenshire</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.144150</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.009643</td>\n",
       "      <td>0.020203</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.005231</td>\n",
       "      <td>0.661130</td>\n",
       "      <td>0.065035</td>\n",
       "      <td>0.007623</td>\n",
       "      <td>0.078941</td>\n",
       "      <td>0.004772</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceredigion</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.208963</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>0.003853</td>\n",
       "      <td>0.028047</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>0.004561</td>\n",
       "      <td>0.552210</td>\n",
       "      <td>0.050915</td>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.131480</td>\n",
       "      <td>0.004839</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conwy</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.134610</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.010880</td>\n",
       "      <td>0.042521</td>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.005450</td>\n",
       "      <td>0.628591</td>\n",
       "      <td>0.054559</td>\n",
       "      <td>0.012876</td>\n",
       "      <td>0.098896</td>\n",
       "      <td>0.006468</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denbighshire</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.124793</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>0.010424</td>\n",
       "      <td>0.033191</td>\n",
       "      <td>0.005953</td>\n",
       "      <td>0.004714</td>\n",
       "      <td>0.629337</td>\n",
       "      <td>0.063782</td>\n",
       "      <td>0.015381</td>\n",
       "      <td>0.106789</td>\n",
       "      <td>0.005005</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flintshire</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.088820</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.008643</td>\n",
       "      <td>0.039082</td>\n",
       "      <td>0.004308</td>\n",
       "      <td>0.007049</td>\n",
       "      <td>0.695296</td>\n",
       "      <td>0.065470</td>\n",
       "      <td>0.017475</td>\n",
       "      <td>0.068590</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gwynedd</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.158374</td>\n",
       "      <td>0.000630</td>\n",
       "      <td>0.006523</td>\n",
       "      <td>0.044087</td>\n",
       "      <td>0.001946</td>\n",
       "      <td>0.004892</td>\n",
       "      <td>0.583959</td>\n",
       "      <td>0.056911</td>\n",
       "      <td>0.012546</td>\n",
       "      <td>0.124609</td>\n",
       "      <td>0.005523</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merthyr Tydfil</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.066935</td>\n",
       "      <td>0.000956</td>\n",
       "      <td>0.018327</td>\n",
       "      <td>0.068688</td>\n",
       "      <td>0.008327</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.629029</td>\n",
       "      <td>0.104387</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.092952</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monmouthshire</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.154588</td>\n",
       "      <td>0.001425</td>\n",
       "      <td>0.020015</td>\n",
       "      <td>0.013149</td>\n",
       "      <td>0.002055</td>\n",
       "      <td>0.007964</td>\n",
       "      <td>0.644650</td>\n",
       "      <td>0.051872</td>\n",
       "      <td>0.011281</td>\n",
       "      <td>0.088563</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neath Port Talbot</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.069193</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.016497</td>\n",
       "      <td>0.033028</td>\n",
       "      <td>0.004307</td>\n",
       "      <td>0.007714</td>\n",
       "      <td>0.697304</td>\n",
       "      <td>0.076789</td>\n",
       "      <td>0.012801</td>\n",
       "      <td>0.076958</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newport</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.073407</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>0.020370</td>\n",
       "      <td>0.077770</td>\n",
       "      <td>0.006505</td>\n",
       "      <td>0.006676</td>\n",
       "      <td>0.635682</td>\n",
       "      <td>0.072087</td>\n",
       "      <td>0.014299</td>\n",
       "      <td>0.087753</td>\n",
       "      <td>0.004549</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pembrokeshire</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.173285</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.003947</td>\n",
       "      <td>0.028847</td>\n",
       "      <td>0.003597</td>\n",
       "      <td>0.006456</td>\n",
       "      <td>0.612539</td>\n",
       "      <td>0.061291</td>\n",
       "      <td>0.008023</td>\n",
       "      <td>0.094177</td>\n",
       "      <td>0.007083</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Powys</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.213988</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.005719</td>\n",
       "      <td>0.010479</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>0.587859</td>\n",
       "      <td>0.045874</td>\n",
       "      <td>0.013244</td>\n",
       "      <td>0.111495</td>\n",
       "      <td>0.005263</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhondda, Cynon, Taff</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.068239</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>0.041419</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>0.002185</td>\n",
       "      <td>0.004480</td>\n",
       "      <td>0.676090</td>\n",
       "      <td>0.080492</td>\n",
       "      <td>0.004440</td>\n",
       "      <td>0.078891</td>\n",
       "      <td>0.003564</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swansea</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.080044</td>\n",
       "      <td>0.000973</td>\n",
       "      <td>0.008590</td>\n",
       "      <td>0.056337</td>\n",
       "      <td>0.006187</td>\n",
       "      <td>0.005934</td>\n",
       "      <td>0.650939</td>\n",
       "      <td>0.073789</td>\n",
       "      <td>0.014447</td>\n",
       "      <td>0.097254</td>\n",
       "      <td>0.005506</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Vale of Glamorgan</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.099800</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.058496</td>\n",
       "      <td>0.028502</td>\n",
       "      <td>0.004256</td>\n",
       "      <td>0.005561</td>\n",
       "      <td>0.645648</td>\n",
       "      <td>0.053868</td>\n",
       "      <td>0.016922</td>\n",
       "      <td>0.079708</td>\n",
       "      <td>0.005833</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Torfaen</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.064132</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.046993</td>\n",
       "      <td>0.007650</td>\n",
       "      <td>0.007476</td>\n",
       "      <td>0.696878</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>0.008097</td>\n",
       "      <td>0.076724</td>\n",
       "      <td>0.003154</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrexham</th>\n",
       "      <th>Wales</th>\n",
       "      <td>0.083692</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.007797</td>\n",
       "      <td>0.052834</td>\n",
       "      <td>0.006665</td>\n",
       "      <td>0.006241</td>\n",
       "      <td>0.663046</td>\n",
       "      <td>0.081539</td>\n",
       "      <td>0.012702</td>\n",
       "      <td>0.080611</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>348 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Home  Underground     Train  \\\n",
       "LA                        Region                                        \n",
       "County Durham             North East  0.090621     0.001417  0.008184   \n",
       "Darlington                North East  0.085282     0.000673  0.016893   \n",
       "Gateshead                 North East  0.069473     0.046475  0.007673   \n",
       "Hartlepool                North East  0.065480     0.000874  0.012418   \n",
       "Middlesbrough             North East  0.061177     0.000807  0.012796   \n",
       "Newcastle upon Tyne       North East  0.070641     0.052826  0.011572   \n",
       "North Tyneside            North East  0.072866     0.088757  0.014423   \n",
       "Northumberland            North East  0.121810     0.004486  0.012607   \n",
       "Redcar and Cleveland      North East  0.071583     0.000763  0.020850   \n",
       "South Tyneside            North East  0.059964     0.087911  0.016867   \n",
       "Stockton-on-Tees          North East  0.073185     0.001113  0.012798   \n",
       "Sunderland                North East  0.063410     0.022730  0.007357   \n",
       "Allerdale                 North West  0.130762     0.000477  0.008117   \n",
       "Barrow-in-Furness         North West  0.064274     0.001056  0.016476   \n",
       "Blackburn with Darwen     North West  0.081457     0.000694  0.015796   \n",
       "Blackpool                 North West  0.100506     0.000651  0.008336   \n",
       "Bolton                    North West  0.081756     0.002221  0.035536   \n",
       "Burnley                   North West  0.077125     0.000524  0.006241   \n",
       "Bury                      North West  0.087339     0.048094  0.005941   \n",
       "Carlisle                  North West  0.101422     0.000557  0.005643   \n",
       "Cheshire East             North West  0.128837     0.001402  0.027443   \n",
       "Cheshire West and Chester North West  0.107270     0.001605  0.019446   \n",
       "Chorley                   North West  0.108506     0.000633  0.023481   \n",
       "Copeland                  North West  0.084582     0.000547  0.015779   \n",
       "Eden                      North West  0.210153     0.000728  0.007502   \n",
       "Fylde                     North West  0.117502     0.000927  0.012054   \n",
       "Halton                    North West  0.064192     0.000660  0.018775   \n",
       "Hyndburn                  North West  0.079460     0.000250  0.009030   \n",
       "Knowsley                  North West  0.057237     0.002626  0.055891   \n",
       "Lancaster                 North West  0.105742     0.000844  0.019926   \n",
       "...                                        ...          ...       ...   \n",
       "Tewkesbury                South West  0.125289     0.000794  0.006282   \n",
       "Torbay                    South West  0.120414     0.000545  0.009225   \n",
       "Torridge                  South West  0.198286     0.000544  0.002959   \n",
       "West Devon                South West  0.207797     0.001228  0.006418   \n",
       "West Dorset               South West  0.178807     0.002034  0.010894   \n",
       "West Somerset             South West  0.253012     0.001107  0.004689   \n",
       "Weymouth and Portland     South West  0.099610     0.000767  0.012372   \n",
       "Wiltshire                 South West  0.136701     0.001471  0.024518   \n",
       "Anglesey                  Wales       0.132299     0.000657  0.006539   \n",
       "Blaenau Gwent             Wales       0.058676     0.000566  0.007670   \n",
       "Bridgend                  Wales       0.078568     0.000751  0.024062   \n",
       "Caerphilly                Wales       0.069004     0.000516  0.038070   \n",
       "Cardiff                   Wales       0.075169     0.001228  0.032560   \n",
       "Carmarthenshire           Wales       0.144150     0.000719  0.009643   \n",
       "Ceredigion                Wales       0.208963     0.000986  0.003853   \n",
       "Conwy                     Wales       0.134610     0.000739  0.010880   \n",
       "Denbighshire              Wales       0.124793     0.000632  0.010424   \n",
       "Flintshire                Wales       0.088820     0.000594  0.008643   \n",
       "Gwynedd                   Wales       0.158374     0.000630  0.006523   \n",
       "Merthyr Tydfil            Wales       0.066935     0.000956  0.018327   \n",
       "Monmouthshire             Wales       0.154588     0.001425  0.020015   \n",
       "Neath Port Talbot         Wales       0.069193     0.000593  0.016497   \n",
       "Newport                   Wales       0.073407     0.000901  0.020370   \n",
       "Pembrokeshire             Wales       0.173285     0.000756  0.003947   \n",
       "Powys                     Wales       0.213988     0.000848  0.005719   \n",
       "Rhondda, Cynon, Taff      Wales       0.068239     0.000614  0.041419   \n",
       "Swansea                   Wales       0.080044     0.000973  0.008590   \n",
       "The Vale of Glamorgan     Wales       0.099800     0.001407  0.058496   \n",
       "Torfaen                   Wales       0.064132     0.000745  0.011798   \n",
       "Wrexham                   Wales       0.083692     0.000582  0.007797   \n",
       "\n",
       "                                           Bus      Taxi  Motorcycle  \\\n",
       "LA                        Region                                       \n",
       "County Durham             North East  0.060256  0.006148    0.004555   \n",
       "Darlington                North East  0.068960  0.008222    0.003897   \n",
       "Gateshead                 North East  0.151387  0.004931    0.003962   \n",
       "Hartlepool                North East  0.067678  0.017820    0.004634   \n",
       "Middlesbrough             North East  0.089244  0.016536    0.003575   \n",
       "Newcastle upon Tyne       North East  0.182076  0.008690    0.002891   \n",
       "North Tyneside            North East  0.089622  0.007790    0.004905   \n",
       "Northumberland            North East  0.051688  0.003560    0.004643   \n",
       "Redcar and Cleveland      North East  0.058807  0.006885    0.004525   \n",
       "South Tyneside            North East  0.095850  0.004689    0.005277   \n",
       "Stockton-on-Tees          North East  0.057655  0.006807    0.004706   \n",
       "Sunderland                North East  0.124688  0.006090    0.004279   \n",
       "Allerdale                 North West  0.037547  0.002301    0.004948   \n",
       "Barrow-in-Furness         North West  0.050965  0.013629    0.017340   \n",
       "Blackburn with Darwen     North West  0.053533  0.018125    0.005205   \n",
       "Blackpool                 North West  0.084241  0.009199    0.008971   \n",
       "Bolton                    North West  0.066468  0.012459    0.006420   \n",
       "Burnley                   North West  0.083130  0.008287    0.005559   \n",
       "Bury                      North West  0.064417  0.007838    0.005168   \n",
       "Carlisle                  North West  0.068512  0.003230    0.005457   \n",
       "Cheshire East             North West  0.018036  0.002782    0.006454   \n",
       "Cheshire West and Chester North West  0.034202  0.004890    0.006251   \n",
       "Chorley                   North West  0.029329  0.002514    0.006983   \n",
       "Copeland                  North West  0.046578  0.004621    0.008270   \n",
       "Eden                      North West  0.021048  0.002440    0.003132   \n",
       "Fylde                     North West  0.038800  0.004028    0.006810   \n",
       "Halton                    North West  0.065773  0.005575    0.006096   \n",
       "Hyndburn                  North West  0.062429  0.010002    0.006640   \n",
       "Knowsley                  North West  0.110324  0.019371    0.004458   \n",
       "Lancaster                 North West  0.058999  0.005427    0.008483   \n",
       "...                                        ...       ...         ...   \n",
       "Tewkesbury                South West  0.044772  0.000698    0.010880   \n",
       "Torbay                    South West  0.043383  0.005552    0.015181   \n",
       "Torridge                  South West  0.028638  0.002211    0.008843   \n",
       "West Devon                South West  0.022028  0.003288    0.007329   \n",
       "West Dorset               South West  0.016450  0.002691    0.008881   \n",
       "West Somerset             South West  0.020449  0.002149    0.006447   \n",
       "Weymouth and Portland     South West  0.068796  0.003468    0.016307   \n",
       "Wiltshire                 South West  0.026211  0.002182    0.007918   \n",
       "Anglesey                  Wales       0.026125  0.003286    0.007131   \n",
       "Blaenau Gwent             Wales       0.030222  0.005443    0.005267   \n",
       "Bridgend                  Wales       0.028110  0.005942    0.005109   \n",
       "Caerphilly                Wales       0.038341  0.003897    0.005304   \n",
       "Cardiff                   Wales       0.099910  0.004229    0.004104   \n",
       "Carmarthenshire           Wales       0.020203  0.002553    0.005231   \n",
       "Ceredigion                Wales       0.028047  0.003020    0.004561   \n",
       "Conwy                     Wales       0.042521  0.004412    0.005450   \n",
       "Denbighshire              Wales       0.033191  0.005953    0.004714   \n",
       "Flintshire                Wales       0.039082  0.004308    0.007049   \n",
       "Gwynedd                   Wales       0.044087  0.001946    0.004892   \n",
       "Merthyr Tydfil            Wales       0.068688  0.008327    0.003068   \n",
       "Monmouthshire             Wales       0.013149  0.002055    0.007964   \n",
       "Neath Port Talbot         Wales       0.033028  0.004307    0.007714   \n",
       "Newport                   Wales       0.077770  0.006505    0.006676   \n",
       "Pembrokeshire             Wales       0.028847  0.003597    0.006456   \n",
       "Powys                     Wales       0.010479  0.001807    0.003425   \n",
       "Rhondda, Cynon, Taff      Wales       0.039586  0.002185    0.004480   \n",
       "Swansea                   Wales       0.056337  0.006187    0.005934   \n",
       "The Vale of Glamorgan     Wales       0.028502  0.004256    0.005561   \n",
       "Torfaen                   Wales       0.046993  0.007650    0.007476   \n",
       "Wrexham                   Wales       0.052834  0.006665    0.006241   \n",
       "\n",
       "                                         Drive  Passenger      Bike      Walk  \\\n",
       "LA                        Region                                                \n",
       "County Durham             North East  0.643475   0.076185  0.009676  0.094298   \n",
       "Darlington                North East  0.591280   0.068083  0.023483  0.128208   \n",
       "Gateshead                 North East  0.546774   0.063454  0.014302  0.086703   \n",
       "Hartlepool                North East  0.605370   0.083591  0.018694  0.113988   \n",
       "Middlesbrough             North East  0.571159   0.085046  0.025208  0.124095   \n",
       "Newcastle upon Tyne       North East  0.453890   0.056195  0.027008  0.128227   \n",
       "North Tyneside            North East  0.547175   0.061973  0.024420  0.078718   \n",
       "Northumberland            North East  0.616510   0.063192  0.014118  0.099604   \n",
       "Redcar and Cleveland      North East  0.643805   0.070554  0.015527  0.094669   \n",
       "South Tyneside            North East  0.541781   0.058401  0.021603  0.088964   \n",
       "Stockton-on-Tees          North East  0.663357   0.069030  0.019433  0.081506   \n",
       "Sunderland                North East  0.581075   0.076997  0.013069  0.093078   \n",
       "Allerdale                 North West  0.603134   0.074117  0.015062  0.119281   \n",
       "Barrow-in-Furness         North West  0.535144   0.072848  0.051828  0.169626   \n",
       "Blackburn with Darwen     North West  0.606381   0.081225  0.009781  0.123259   \n",
       "Blackpool                 North West  0.534297   0.074537  0.031196  0.141731   \n",
       "Bolton                    North West  0.621568   0.066379  0.010222  0.092691   \n",
       "Burnley                   North West  0.611439   0.078436  0.010411  0.114730   \n",
       "Bury                      North West  0.628845   0.054739  0.013358  0.080581   \n",
       "Carlisle                  North West  0.569347   0.062683  0.025801  0.153042   \n",
       "Cheshire East             North West  0.645543   0.049648  0.025760  0.089888   \n",
       "Cheshire West and Chester North West  0.648803   0.054747  0.026227  0.091602   \n",
       "Chorley                   North West  0.677405   0.054765  0.014171  0.078712   \n",
       "Copeland                  North West  0.634733   0.093825  0.016844  0.089933   \n",
       "Eden                      North West  0.559339   0.047740  0.011871  0.131131   \n",
       "Fylde                     North West  0.641930   0.048971  0.027789  0.096349   \n",
       "Halton                    North West  0.653733   0.069264  0.020685  0.090679   \n",
       "Hyndburn                  North West  0.641125   0.071569  0.011058  0.104215   \n",
       "Knowsley                  North West  0.562756   0.075894  0.015594  0.090272   \n",
       "Lancaster                 North West  0.556118   0.060638  0.039741  0.138560   \n",
       "...                                        ...        ...       ...       ...   \n",
       "Tewkesbury                South West  0.646519   0.044748  0.042798  0.073849   \n",
       "Torbay                    South West  0.579386   0.059214  0.013688  0.147825   \n",
       "Torridge                  South West  0.576866   0.049997  0.012414  0.113870   \n",
       "West Devon                South West  0.577632   0.043699  0.010737  0.113149   \n",
       "West Dorset               South West  0.565800   0.043072  0.023778  0.141904   \n",
       "West Somerset             South West  0.502572   0.041810  0.024162  0.138261   \n",
       "Weymouth and Portland     South West  0.564511   0.056158  0.033381  0.136893   \n",
       "Wiltshire                 South West  0.609838   0.047472  0.027749  0.111531   \n",
       "Anglesey                  Wales       0.661694   0.058460  0.014886  0.081003   \n",
       "Blaenau Gwent             Wales       0.694214   0.098194  0.004736  0.090983   \n",
       "Bridgend                  Wales       0.690756   0.072969  0.008211  0.081082   \n",
       "Caerphilly                Wales       0.687061   0.077483  0.006130  0.070579   \n",
       "Cardiff                   Wales       0.543605   0.050140  0.036281  0.148132   \n",
       "Carmarthenshire           Wales       0.661130   0.065035  0.007623  0.078941   \n",
       "Ceredigion                Wales       0.552210   0.050915  0.011126  0.131480   \n",
       "Conwy                     Wales       0.628591   0.054559  0.012876  0.098896   \n",
       "Denbighshire              Wales       0.629337   0.063782  0.015381  0.106789   \n",
       "Flintshire                Wales       0.695296   0.065470  0.017475  0.068590   \n",
       "Gwynedd                   Wales       0.583959   0.056911  0.012546  0.124609   \n",
       "Merthyr Tydfil            Wales       0.629029   0.104387  0.003187  0.092952   \n",
       "Monmouthshire             Wales       0.644650   0.051872  0.011281  0.088563   \n",
       "Neath Port Talbot         Wales       0.697304   0.076789  0.012801  0.076958   \n",
       "Newport                   Wales       0.635682   0.072087  0.014299  0.087753   \n",
       "Pembrokeshire             Wales       0.612539   0.061291  0.008023  0.094177   \n",
       "Powys                     Wales       0.587859   0.045874  0.013244  0.111495   \n",
       "Rhondda, Cynon, Taff      Wales       0.676090   0.080492  0.004440  0.078891   \n",
       "Swansea                   Wales       0.650939   0.073789  0.014447  0.097254   \n",
       "The Vale of Glamorgan     Wales       0.645648   0.053868  0.016922  0.079708   \n",
       "Torfaen                   Wales       0.696878   0.076352  0.008097  0.076724   \n",
       "Wrexham                   Wales       0.663046   0.081539  0.012702  0.080611   \n",
       "\n",
       "                                         Other  Row_Sum  \n",
       "LA                        Region                         \n",
       "County Durham             North East  0.005187      1.0  \n",
       "Darlington                North East  0.005019      1.0  \n",
       "Gateshead                 North East  0.004865      1.0  \n",
       "Hartlepool                North East  0.009453      1.0  \n",
       "Middlesbrough             North East  0.010358      1.0  \n",
       "Newcastle upon Tyne       North East  0.005983      1.0  \n",
       "North Tyneside            North East  0.009352      1.0  \n",
       "Northumberland            North East  0.007781      1.0  \n",
       "Redcar and Cleveland      North East  0.012031      1.0  \n",
       "South Tyneside            North East  0.018693      1.0  \n",
       "Stockton-on-Tees          North East  0.010411      1.0  \n",
       "Sunderland                North East  0.007226      1.0  \n",
       "Allerdale                 North West  0.004254      1.0  \n",
       "Barrow-in-Furness         North West  0.006814      1.0  \n",
       "Blackburn with Darwen     North West  0.004544      1.0  \n",
       "Blackpool                 North West  0.006334      1.0  \n",
       "Bolton                    North West  0.004280      1.0  \n",
       "Burnley                   North West  0.004117      1.0  \n",
       "Bury                      North West  0.003680      1.0  \n",
       "Carlisle                  North West  0.004306      1.0  \n",
       "Cheshire East             North West  0.004207      1.0  \n",
       "Cheshire West and Chester North West  0.004958      1.0  \n",
       "Chorley                   North West  0.003501      1.0  \n",
       "Copeland                  North West  0.004287      1.0  \n",
       "Eden                      North West  0.004916      1.0  \n",
       "Fylde                     North West  0.004839      1.0  \n",
       "Halton                    North West  0.004568      1.0  \n",
       "Hyndburn                  North West  0.004223      1.0  \n",
       "Knowsley                  North West  0.005576      1.0  \n",
       "Lancaster                 North West  0.005523      1.0  \n",
       "...                                        ...      ...  \n",
       "Tewkesbury                South West  0.003370      1.0  \n",
       "Torbay                    South West  0.005588      1.0  \n",
       "Torridge                  South West  0.005374      1.0  \n",
       "West Devon                South West  0.006695      1.0  \n",
       "West Dorset               South West  0.005688      1.0  \n",
       "West Somerset             South West  0.005340      1.0  \n",
       "Weymouth and Portland     South West  0.007737      1.0  \n",
       "Wiltshire                 South West  0.004410      1.0  \n",
       "Anglesey                  Wales       0.007920      1.0  \n",
       "Blaenau Gwent             Wales       0.004030      1.0  \n",
       "Bridgend                  Wales       0.004440      1.0  \n",
       "Caerphilly                Wales       0.003613      1.0  \n",
       "Cardiff                   Wales       0.004642      1.0  \n",
       "Carmarthenshire           Wales       0.004772      1.0  \n",
       "Ceredigion                Wales       0.004839      1.0  \n",
       "Conwy                     Wales       0.006468      1.0  \n",
       "Denbighshire              Wales       0.005005      1.0  \n",
       "Flintshire                Wales       0.004673      1.0  \n",
       "Gwynedd                   Wales       0.005523      1.0  \n",
       "Merthyr Tydfil            Wales       0.004144      1.0  \n",
       "Monmouthshire             Wales       0.004437      1.0  \n",
       "Neath Port Talbot         Wales       0.004815      1.0  \n",
       "Newport                   Wales       0.004549      1.0  \n",
       "Pembrokeshire             Wales       0.007083      1.0  \n",
       "Powys                     Wales       0.005263      1.0  \n",
       "Rhondda, Cynon, Taff      Wales       0.003564      1.0  \n",
       "Swansea                   Wales       0.005506      1.0  \n",
       "The Vale of Glamorgan     Wales       0.005833      1.0  \n",
       "Torfaen                   Wales       0.003154      1.0  \n",
       "Wrexham                   Wales       0.004292      1.0  \n",
       "\n",
       "[348 rows x 12 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from lds.data import Data\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn\")\n",
    "\n",
    "df = Data[\"Travel_2011\"].set_index([\"LA\",\"Region\"])\n",
    "#rewrite the travel methods as propotions of the total\n",
    "df = df.loc[:,\"Home\":\"Other\"].apply(lambda x: x / x.sum() , axis = 1)\n",
    "df[\"Row_Sum\"] = df.sum(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Only consider LAs where Driving is less than 50%\n",
    "### Notice that we now only have 52 LAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at just the data where Driving makes up less than 50% of travel\n",
    "df_50 = df.query('Drive<0.5')\n",
    "len(df_50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "___\n",
    "# Scatter Diagrams\n",
    "* Here we produce 9 scatter diagrams. We include all modes other than \"Other\"\n",
    "* The code below looks intimidating but don't worry about the code. \n",
    "* Think about what the Scatter Diagrams tell us. \n",
    "* What questions do you have about the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HRsmKCvO1cuGklMQbLb7jxBBf6mVyrsvmOGLJsamWyv1hh/Yjm4/TVMrkXBet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h1q1b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\nAAAA2/j/AdUkOMlzkl1CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df60358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize = (16,16))\n",
    "non_drive_modes = [mode for mode in df.axes[1] if mode not in [\"Drive\",\"Other\",\"Row_Sum\"]]\n",
    "for i,mode in enumerate(non_drive_modes):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.scatter(df_50[mode],df_50[\"Drive\"])\n",
    "    plt.title('Drive vs ' + mode)\n",
    "    plt.xlim(xmin=0,xmax=max(df[mode]))\n",
    "    plt.ylim(ymin=0)\n",
    "    \n",
    "plt.subplots_adjust(hspace=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extreme LAs\n",
    "* In this code we find the LAs in the scatter diagram that have the highest proportion of each transport mode other than Driving. \n",
    "* Can you identify the LA in each Scatter Diagram?\n",
    "* Does each LA make sense for the category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Location</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Home</th>\n",
       "      <td>(Isles of Scilly, South West)</td>\n",
       "      <td>0.326468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Underground</th>\n",
       "      <td>(Newham, London)</td>\n",
       "      <td>0.385923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>(Bromley, London)</td>\n",
       "      <td>0.300050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bus</th>\n",
       "      <td>(Hackney, London)</td>\n",
       "      <td>0.257608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxi</th>\n",
       "      <td>(Knowsley, North West)</td>\n",
       "      <td>0.019371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorcycle</th>\n",
       "      <td>(Isles of Scilly, South West)</td>\n",
       "      <td>0.025934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passenger</th>\n",
       "      <td>(Boston, East Midlands)</td>\n",
       "      <td>0.112669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike</th>\n",
       "      <td>(Cambridge, East of England)</td>\n",
       "      <td>0.290341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walk</th>\n",
       "      <td>(City of London, London)</td>\n",
       "      <td>0.457973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Location    Values\n",
       "Mode                                                \n",
       "Home         (Isles of Scilly, South West)  0.326468\n",
       "Underground               (Newham, London)  0.385923\n",
       "Train                    (Bromley, London)  0.300050\n",
       "Bus                      (Hackney, London)  0.257608\n",
       "Taxi                (Knowsley, North West)  0.019371\n",
       "Motorcycle   (Isles of Scilly, South West)  0.025934\n",
       "Passenger          (Boston, East Midlands)  0.112669\n",
       "Bike          (Cambridge, East of England)  0.290341\n",
       "Walk              (City of London, London)  0.457973"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"Mode\": non_drive_modes,\n",
    "        \"Location\":[df[mode].idxmax() for mode in non_drive_modes], \n",
    "        \"Values\":[df[mode][df[mode].idxmax()] for mode in non_drive_modes]}\n",
    "\n",
    "pd.DataFrame(data).set_index(\"Mode\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# London\n",
    "## How many of the LAs from the scatter diagrams are based in London?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_50.query('Region == \"London\"'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What about from the original data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.query('Region == \"London\"'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which one is missing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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></th>\n",
       "      <th>Home</th>\n",
       "      <th>Underground</th>\n",
       "      <th>Train</th>\n",
       "      <th>Bus</th>\n",
       "      <th>Taxi</th>\n",
       "      <th>Motorcycle</th>\n",
       "      <th>Drive</th>\n",
       "      <th>Passenger</th>\n",
       "      <th>Bike</th>\n",
       "      <th>Walk</th>\n",
       "      <th>Other</th>\n",
       "      <th>Row_Sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hillingdon</th>\n",
       "      <th>London</th>\n",
       "      <td>0.086185</td>\n",
       "      <td>0.129826</td>\n",
       "      <td>0.041745</td>\n",
       "      <td>0.1173</td>\n",
       "      <td>0.003239</td>\n",
       "      <td>0.007944</td>\n",
       "      <td>0.501566</td>\n",
       "      <td>0.030309</td>\n",
       "      <td>0.014521</td>\n",
       "      <td>0.063704</td>\n",
       "      <td>0.003661</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Home  Underground     Train     Bus      Taxi  \\\n",
       "LA         Region                                                      \n",
       "Hillingdon London  0.086185     0.129826  0.041745  0.1173  0.003239   \n",
       "\n",
       "                   Motorcycle     Drive  Passenger      Bike      Walk  \\\n",
       "LA         Region                                                        \n",
       "Hillingdon London    0.007944  0.501566   0.030309  0.014521  0.063704   \n",
       "\n",
       "                      Other  Row_Sum  \n",
       "LA         Region                     \n",
       "Hillingdon London  0.003661      1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('Region == \"London\"').sort_values(by = \"Drive\", ascending = False).head(n = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter diagrams highlighting London"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oj3GoQ81+3cA+kjV3CKZfXoi7b7wMRmNsxU20/ZFo4hWV1vEmWx+wy92DdrsLXe4exWLt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\nksaMGaO6ujoVFxfrxhtv1BNPPCFJ2rhxo66//vqwzw/Yvn2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w8OIlqtmyIfjYLjmc/AST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\n0bwWsWpHb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9qfN954I+jz/+AHP9APf/hDXXjhhSoqKtLw4cOVk5Mz4McDkHxWzsvuwyM6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d950b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize = (16,16))\n",
    "df_london = df_50.query('Region == \"London\"')\n",
    "non_drive_modes = [mode for mode in df.axes[1] if mode not in [\"Drive\",\"Other\",\"Row_Sum\"]]\n",
    "for i,mode in enumerate(non_drive_modes):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.scatter(df_50[mode],df_50[\"Drive\"])\n",
    "    plt.scatter(df_london[mode],df_london[\"Drive\"], c = \"r\")\n",
    "    plt.title('Drive vs ' + mode)\n",
    "    plt.xlim(xmin=0,xmax=max(df[mode]))\n",
    "    plt.ylim(ymin=0)\n",
    "    \n",
    "plt.subplots_adjust(hspace=0.3)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}