Melbourne - Sydney Walkability Comparisons.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A Walkability Comparison: Melbourne and Sydney\n",
    "\n",
    "This notebook analyses and compares and SA1s in Inner Melbourne \n",
    "and Sydney: City and Inner South. The area being analysed for Melbourne is much larger than Sydney, with more SA1s. This is due to the difference in how the SA4s are laid out for both cities. \n",
    "\n",
    "Both areas being analysed encompass the CBDs of the respective cities (though, North Sydney is not considered)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dframe = pd.read_csv('data/innermelbourne.csv')\n",
    "dframe.columns = dframe.columns.str.strip() #To fix formatting issues with headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Dropping columns deemed irrelevant\n",
    "aframe = dframe.drop(['gcc_name11','gcc_code11','sa2_5dig11','sa1_7dig11','sa3_code11','sa4_code11','ste_code11','ste_name11'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Group by SA2 suburb\n",
    "avg_sa2 = aframe[['sa2_name11','SumZScore']].groupby('sa2_name11').mean()\n",
    "\n",
    "#Group by SA3 area\n",
    "avg_sa3 = aframe[['sa3_name11','SumZScore']].groupby('sa3_name11').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick look at the SA2 and SA3 groupings for Melbourne, and the average walkability scores (represented by 'SumZScore')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa3_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brunswick - Coburg</th>\n",
       "      <td>-1.442297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darebin - South</th>\n",
       "      <td>-1.705241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Essendon</th>\n",
       "      <td>-1.501010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melbourne City</th>\n",
       "      <td>2.551466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Phillip</th>\n",
       "      <td>0.256828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stonnington - West</th>\n",
       "      <td>0.053694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yarra</th>\n",
       "      <td>0.446783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SumZScore\n",
       "sa3_name11                   \n",
       "Brunswick - Coburg  -1.442297\n",
       "Darebin - South     -1.705241\n",
       "Essendon            -1.501010\n",
       "Melbourne City       2.551466\n",
       "Port Phillip         0.256828\n",
       "Stonnington - West   0.053694\n",
       "Yarra                0.446783"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_sa3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa2_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abbotsford</th>\n",
       "      <td>0.847583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albert Park</th>\n",
       "      <td>0.425471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphington - Fairfield</th>\n",
       "      <td>-2.055192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armadale</th>\n",
       "      <td>-1.016522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ascot Vale</th>\n",
       "      <td>-1.463378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunswick</th>\n",
       "      <td>-0.680594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunswick East</th>\n",
       "      <td>-0.417797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunswick West</th>\n",
       "      <td>-1.725236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carlton</th>\n",
       "      <td>2.698953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carlton North - Princes Hill</th>\n",
       "      <td>-0.955973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coburg</th>\n",
       "      <td>-1.852370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collingwood</th>\n",
       "      <td>1.015557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docklands</th>\n",
       "      <td>1.168116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Melbourne</th>\n",
       "      <td>0.954821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elwood</th>\n",
       "      <td>-0.626253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Essendon - Aberfeldie</th>\n",
       "      <td>-2.004841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fitzroy</th>\n",
       "      <td>1.344740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fitzroy North</th>\n",
       "      <td>0.204232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flemington</th>\n",
       "      <td>-0.136415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kensington</th>\n",
       "      <td>1.062515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melbourne</th>\n",
       "      <td>3.547425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moonee Ponds</th>\n",
       "      <td>-1.473731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Melbourne</th>\n",
       "      <td>1.697015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northcote</th>\n",
       "      <td>-1.240260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parkville</th>\n",
       "      <td>0.655882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pascoe Vale South</th>\n",
       "      <td>-2.406909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Melbourne</th>\n",
       "      <td>0.927201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Melbourne Industrial</th>\n",
       "      <td>-1.290091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prahran - Windsor</th>\n",
       "      <td>1.201360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond (Vic.)</th>\n",
       "      <td>0.836230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Melbourne</th>\n",
       "      <td>1.838322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Yarra - East</th>\n",
       "      <td>1.249292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Yarra - West</th>\n",
       "      <td>0.823606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southbank</th>\n",
       "      <td>6.816946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St Kilda</th>\n",
       "      <td>0.652861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St Kilda East</th>\n",
       "      <td>-1.086723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thornbury</th>\n",
       "      <td>-2.108694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toorak</th>\n",
       "      <td>-1.939547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yarra - North</th>\n",
       "      <td>-0.451671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              SumZScore\n",
       "sa2_name11                             \n",
       "Abbotsford                     0.847583\n",
       "Albert Park                    0.425471\n",
       "Alphington - Fairfield        -2.055192\n",
       "Armadale                      -1.016522\n",
       "Ascot Vale                    -1.463378\n",
       "Brunswick                     -0.680594\n",
       "Brunswick East                -0.417797\n",
       "Brunswick West                -1.725236\n",
       "Carlton                        2.698953\n",
       "Carlton North - Princes Hill  -0.955973\n",
       "Coburg                        -1.852370\n",
       "Collingwood                    1.015557\n",
       "Docklands                      1.168116\n",
       "East Melbourne                 0.954821\n",
       "Elwood                        -0.626253\n",
       "Essendon - Aberfeldie         -2.004841\n",
       "Fitzroy                        1.344740\n",
       "Fitzroy North                  0.204232\n",
       "Flemington                    -0.136415\n",
       "Kensington                     1.062515\n",
       "Melbourne                      3.547425\n",
       "Moonee Ponds                  -1.473731\n",
       "North Melbourne                1.697015\n",
       "Northcote                     -1.240260\n",
       "Parkville                      0.655882\n",
       "Pascoe Vale South             -2.406909\n",
       "Port Melbourne                 0.927201\n",
       "Port Melbourne Industrial     -1.290091\n",
       "Prahran - Windsor              1.201360\n",
       "Richmond (Vic.)                0.836230\n",
       "South Melbourne                1.838322\n",
       "South Yarra - East             1.249292\n",
       "South Yarra - West             0.823606\n",
       "Southbank                      6.816946\n",
       "St Kilda                       0.652861\n",
       "St Kilda East                 -1.086723\n",
       "Thornbury                     -2.108694\n",
       "Toorak                        -1.939547\n",
       "Yarra - North                 -0.451671"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_sa2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sframe = pd.read_csv('data/sydcityinnersouthresults.csv')\n",
    "sframe.columns = sframe.columns.str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bframe = sframe.drop(['gcc_name11','gcc_code11','sa2_5dig11','sa1_7dig11','sa3_code11','sa4_code11','ste_code11','ste_name11'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA1s in Melbourne: 1185\n",
      "SA1s in Sydney: 628\n"
     ]
    }
   ],
   "source": [
    "#Counting the SA1s in Melbourne and Sydney\n",
    "print \"SA1s in Melbourne: \"+ str(len(aframe.index))\n",
    "print \"SA1s in Sydney: \"+ str(len(bframe.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Group by SA2 suburb\n",
    "savg_sa2 = bframe[['sa2_name11','SumZScore']].groupby('sa2_name11').mean()\n",
    "# savg_sa2.reset_index()\n",
    "# savg_sa2.columns = ['SA2 Name','Walkability Index']\n",
    "\n",
    "#Group by SA3 area\n",
    "savg_sa3 = bframe[['sa3_name11','SumZScore']].groupby('sa3_name11').mean()\n",
    "# savg_sa2.reset_index()\n",
    "# savg_sa3.columns = ['SA2 Name','Walkability Index']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA1s in Melbourne: 39\n",
      "SA1s in Sydney: 17\n"
     ]
    }
   ],
   "source": [
    "#Counting the SA1s in Melbourne and Sydney\n",
    "print \"SA1s in Melbourne: \"+ str(len(avg_sa2.index))\n",
    "print \"SA1s in Sydney: \"+ str(len(savg_sa2.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A look at Sydney's SA2 and SA3 grouping, and average Walkability Scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa3_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Botany</th>\n",
       "      <td>-1.466938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marrickville - Sydenham - Petersham</th>\n",
       "      <td>-1.057064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sydney Inner City</th>\n",
       "      <td>0.697534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     SumZScore\n",
       "sa3_name11                                    \n",
       "Botany                               -1.466938\n",
       "Marrickville - Sydenham - Petersham  -1.057064\n",
       "Sydney Inner City                     0.697534"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savg_sa3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa2_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Banksmeadow</th>\n",
       "      <td>-5.089664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botany</th>\n",
       "      <td>-2.020042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darlinghurst</th>\n",
       "      <td>2.300351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Erskineville - Alexandria</th>\n",
       "      <td>-0.070819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glebe - Forest Lodge</th>\n",
       "      <td>0.205308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marrickville</th>\n",
       "      <td>-1.379702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mascot - Eastlakes</th>\n",
       "      <td>-1.152618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newtown - Camperdown - Darlington</th>\n",
       "      <td>1.066498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pagewood - Hillsdale - Daceyville</th>\n",
       "      <td>-1.419358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petersham - Stanmore</th>\n",
       "      <td>-0.930810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potts Point - Woolloomooloo</th>\n",
       "      <td>1.295943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyrmont - Ultimo</th>\n",
       "      <td>-0.934770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redfern - Chippendale</th>\n",
       "      <td>0.654447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surry Hills</th>\n",
       "      <td>1.903201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sydenham - Tempe - St Peters</th>\n",
       "      <td>-0.429833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sydney - Haymarket - The Rocks</th>\n",
       "      <td>1.403998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waterloo - Beaconsfield</th>\n",
       "      <td>-0.727512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   SumZScore\n",
       "sa2_name11                                  \n",
       "Banksmeadow                        -5.089664\n",
       "Botany                             -2.020042\n",
       "Darlinghurst                        2.300351\n",
       "Erskineville - Alexandria          -0.070819\n",
       "Glebe - Forest Lodge                0.205308\n",
       "Marrickville                       -1.379702\n",
       "Mascot - Eastlakes                 -1.152618\n",
       "Newtown - Camperdown - Darlington   1.066498\n",
       "Pagewood - Hillsdale - Daceyville  -1.419358\n",
       "Petersham - Stanmore               -0.930810\n",
       "Potts Point - Woolloomooloo         1.295943\n",
       "Pyrmont - Ultimo                   -0.934770\n",
       "Redfern - Chippendale               0.654447\n",
       "Surry Hills                         1.903201\n",
       "Sydenham - Tempe - St Peters       -0.429833\n",
       "Sydney - Haymarket - The Rocks      1.403998\n",
       "Waterloo - Beaconsfield            -0.727512"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savg_sa2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yyZEkSbU0aJITEa/qY9qHmxOOJEnSyJgwhHk+ExGvBT4EPAf4FvAg8LkmxiVJ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\nJZMcSZJUSyY5kiSplkxyJElSLf0/+FIDv7g15TEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11296e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 5 SA2 suburbs Melbourne\n",
    "top_sa2_melb = avg_sa2.sort_values('SumZScore',ascending=False).head(5).plot(kind='bar',figsize=(8,6))\n",
    "\n",
    "top_sa2_melb.set_ylabel('Walkability Index') #Setting the Y label\n",
    "top_sa2_melb.set_xlabel('SA2 Suburb') #Setting the X label\n",
    "top_sa2_melb.legend(['Walkability Index'])\n",
    "fig_top_sa2_melb = top_sa2_melb.get_figure() #Assigning the figure to a new variable for further operations\n",
    "fig_top_sa2_melb.tight_layout() #To fit everything in the saved image\n",
    "fig_top_sa2_melb.subplots_adjust(top=0.93)\n",
    "fig_top_sa2_melb.suptitle('Top 5 Melbourne SA2 Suburbs based on Walkability',fontsize=15,fontweight='bold') #Title for the figure\n",
    "fig_top_sa2_melb.savefig(\"figures/top_5_melbourne_sa2.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the plot above that the suburb 'Southbank', has twice the walkability score of the next highest suburb 'Melbourne'. So it might be possible that it impacts our comparison later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1kiFHkiQ1kiFHkiQ10qic1kEaaXPmzGHGjNs6fj+zZk0ZlaNfr7LKqkyc2NlT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\nvravKZ2jG9KSExGNIekUqs7Gomo5WBv4JrB7yVy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1146676d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 5 Suburbs Sydney\n",
    "top_sa2_syd = savg_sa2.sort_values('SumZScore',ascending=False).head(5).plot(kind='bar',figsize=(8,8))\n",
    "\n",
    "\n",
    "top_sa2_syd.set_ylabel('Walkability Index') #Setting the Y label\n",
    "top_sa2_syd.set_xlabel('SA2 Suburb') #Setting the X label\n",
    "top_sa2_syd.legend(['Walkability Index'])\n",
    "fig_top_sa2_syd = top_sa2_syd.get_figure() #Assigning the figure to a new variable for further operations\n",
    "fig_top_sa2_syd.tight_layout() #To fit everything in the saved image\n",
    "fig_top_sa2_syd.subplots_adjust(top=0.93)\n",
    "fig_top_sa2_syd.suptitle('Top 5 Sydney SA2 Suburbs based on Walkability',fontsize=15,fontweight='bold') #Title for the figure\n",
    "fig_top_sa2_syd.savefig(\"figures/top_5_sydney_sa2.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sydney doesn’t seem to have any ‘outlier’ suburbs like Melbourne’s Southbank. Let’s look at Melbourne’s worst 5 suburbs, in terms of walkability:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UJOdbM1sXmAhsa2bLAt8pb1giIiIiLVNKknMacBZwH7AD8ClwVzmDEhEREWmp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\nM4sWW6IOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113babfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Least 5 Suburbs Melbourne\n",
    "bot_sa2_melb = avg_sa2.sort_values('SumZScore',ascending=True).head(5).plot(kind='bar',figsize=(8,6))\n",
    "\n",
    "bot_sa2_melb.set_ylabel('Walkability Index') #Setting the Y label\n",
    "bot_sa2_melb.set_xlabel('SA2 Suburb') #Setting the X label\n",
    "bot_sa2_melb.legend(['Walkability Index'],loc=4)\n",
    "fig_bot_sa2_melb = bot_sa2_melb.get_figure() #Assigning the figure to a new variable for further operations\n",
    "fig_bot_sa2_melb.tight_layout() #To fit everything in the saved image\n",
    "fig_bot_sa2_melb.subplots_adjust(top=0.93)\n",
    "fig_bot_sa2_melb.suptitle('Bottom 5 Melbourne SA2 Suburbs based on Walkability',fontsize=15,fontweight='bold') #Title for the figure\n",
    "fig_bot_sa2_melb.savefig(\"figures/bottom_5_melbourne_sa2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h3wYYwIJ3IV/N3XuVNjQRERGRRVfMODk3AQ8REqI/Ae8CV5UyKBEREZHWKuoG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1079c1dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bot_sa2_syd = savg_sa2.sort_values('SumZScore').head(5).plot(kind='bar',figsize=(8,6))\n",
    "\n",
    "bot_sa2_syd.set_ylabel('Walkability Index') #Setting the Y label\n",
    "bot_sa2_syd.set_xlabel('SA2 Suburb') #Setting the X label\n",
    "bot_sa2_syd.legend(['Walkability Index'],loc=4)\n",
    "fig_bot_sa2_syd = bot_sa2_syd.get_figure() #Assigning the figure to a new variable for further operations\n",
    "fig_bot_sa2_syd.tight_layout() #To fit everything in the saved image\n",
    "fig_bot_sa2_syd.subplots_adjust(top=0.93)\n",
    "fig_bot_sa2_syd.suptitle('Bottom 5 Sydney SA2 Suburbs based on Walkability',fontsize=15,fontweight='bold') #Title for the figure\n",
    "fig_bot_sa2_syd.savefig(\"figures/bottom_5_sydney_sa2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the above bar plot, that Sydney has somewhat of an 'anti Southbank'. The SA2 suburb, Banksmeadow has a twice as low walkability score than the next worse suburb, Botany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa4_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Melbourne - Inner</th>\n",
       "      <td>-0.000008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SumZScore\n",
       "sa4_name11                  \n",
       "Melbourne - Inner  -0.000008"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating the average walkabiity score for Inner Melbourne\n",
    "avg_sa4 = aframe[['sa4_name11','SumZScore']].round(2).groupby('sa4_name11').mean()\n",
    "avg_sa4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa4_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sydney - City and Inner South</th>\n",
       "      <td>-0.000127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               SumZScore\n",
       "sa4_name11                              \n",
       "Sydney - City and Inner South  -0.000127"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating the average walkability score for Sydney City and Inner South\n",
    "savg_sa4 = bframe[['sa4_name11','SumZScore']].round(2).groupby('sa4_name11').mean()\n",
    "savg_sa4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above walkability scores are a bit unintitive to analyse, so let's plot them on a figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qKUmJiEhqKUmJiEhqKUmJiEhqKUmJiEhqKUmJiEhqKUmJiEhqKUmJiEhqdShX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114060890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "melb_syd_avg_sa4_fig = pd.concat([avg_sa4,savg_sa4]).plot(kind='bar',figsize=(6,7))\n",
    "\n",
    "melb_syd_avg_sa4_fig.set_ylabel('Walkability Index') #Setting the Y label\n",
    "melb_syd_avg_sa4_fig.set_xlabel('SA4 Region') #Setting the X label\n",
    "melb_syd_avg_sa4_fig.legend(['Walkability Index'],loc=1)\n",
    "fig_melb_syd_avg_sa4 = melb_syd_avg_sa4_fig.get_figure() #Assigning the figure to a new variable for further operations\n",
    "fig_melb_syd_avg_sa4.tight_layout() #To fit everything in the saved image\n",
    "fig_melb_syd_avg_sa4.subplots_adjust(top=0.93)\n",
    "fig_melb_syd_avg_sa4.suptitle('Comparison of SA4 Regions - Mean',fontsize=15,fontweight='bold') #Title for the figure\n",
    "fig_melb_syd_avg_sa4.savefig(\"figures/melb_syd_avg_sa4.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the Melbourne has somewhat, if not substantially, better walkability than Sydney. But is that really true? \n",
    "\n",
    "We noticed that while Melbourne has 'Southbank', an SA2 with almost twice the walkability index of the second top suburb, 'Melbourne City', Sydney has its own 'anti-Southbank' with 'Banksmeadow', a dockland area with walkability more than twice worse off than the next worst SA2.\n",
    "\n",
    "So what if we represented the aggregate walkability using medians, rather than averages?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa4_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Melbourne - Inner</th>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SumZScore\n",
       "sa4_name11                  \n",
       "Melbourne - Inner      -0.28"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_sa4 = aframe[['sa4_name11','SumZScore']].round(2).groupby('sa4_name11').median()\n",
    "median_sa4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa4_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sydney - City and Inner South</th>\n",
       "      <td>-0.205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               SumZScore\n",
       "sa4_name11                              \n",
       "Sydney - City and Inner South     -0.205"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smedian_sa4 = bframe[['sa4_name11','SumZScore']].round(2).groupby('sa4_name11').median()\n",
    "smedian_sa4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can already see that Sydney peforms a bit better than Melbourne, but let's plot it on a figure anyways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yyYmIYcBY4CngaWDTiDig2YFJkiT1RHe6q34CrA7cT3nW4/L2kmYFJUmS1FPd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\nyZAjSZKaZMiRJElNMuRIkqQmfQEDpSNbYuQSAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112c9cb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "melb_syd_median_sa4_fig = pd.concat([median_sa4,smedian_sa4]).plot(kind='bar',figsize=(8,6))\n",
    "\n",
    "melb_syd_median_sa4_fig.set_ylabel('Walkability Index') #Setting the Y label\n",
    "melb_syd_median_sa4_fig.set_xlabel('SA4 Region') #Setting the X label\n",
    "melb_syd_median_sa4_fig.legend(['Walkability Index'],loc=1)\n",
    "fig_melb_syd_median_sa4 = melb_syd_median_sa4_fig.get_figure() #Assigning the figure to a new variable for further operations\n",
    "fig_melb_syd_median_sa4.tight_layout() #To fit everything in the saved image\n",
    "fig_melb_syd_median_sa4.subplots_adjust(top=0.93)\n",
    "fig_melb_syd_median_sa4.suptitle('Comparison of SA4 Regions - Median',fontsize=15,fontweight='bold') #Title for the figure\n",
    "fig_melb_syd_median_sa4.savefig(\"figures/melb_syd_median_sa4.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see that Sydney has a better median walkability than Melbourne. It's possible that the median is a better metric to compare the two cities, as it is not affected by outliers.\n",
    "\n",
    "So, as some extra analysis, why don't we take out both Southbank and Bankstown, the two outliers in the Melbourne and Sydney datasets, and calculate the mean again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "aframe_no_southbank = aframe[aframe.sa2_name11 != 'Southbank']\n",
    "bframe_no_banksmeadow = bframe[bframe.sa2_name11 != 'Banksmeadow']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to calculate average walkabilities of the two cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa4_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Melbourne - Inner</th>\n",
       "      <td>-0.164996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SumZScore\n",
       "sa4_name11                  \n",
       "Melbourne - Inner  -0.164996"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_sa4_no_south = aframe_no_southbank[['sa4_name11','SumZScore']].round(2).groupby('sa4_name11').mean()\n",
    "avg_sa4_no_south"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SumZScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sa4_name11</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sydney - City and Inner South</th>\n",
       "      <td>0.00799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               SumZScore\n",
       "sa4_name11                              \n",
       "Sydney - City and Inner South    0.00799"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savg_sa4_no_banks = bframe_no_banksmeadow[['sa4_name11','SumZScore']].round(2).groupby('sa4_name11').mean()\n",
    "savg_sa4_no_banks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kkxxJktRIJjmSJKmRTHIkSVIjmeRIkqRGMsmRJEmNZJIjSZIaySRHkiQ1kkmO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\nAwAkiRwAIEnkAABJIgcASBo7L8C5ZyHguwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b71b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "melb_syd_avg_sa4_no_fig = pd.concat([avg_sa4_no_south,savg_sa4_no_banks]).plot(kind='bar',figsize=(8,6))\n",
    "\n",
    "melb_syd_avg_sa4_no_fig.set_ylabel('Walkability Index') #Setting the Y label\n",
    "melb_syd_avg_sa4_no_fig.set_xlabel('SA4 Region') #Setting the X label\n",
    "melb_syd_avg_sa4_no_fig.legend(['Walkability Index'],loc=1)\n",
    "fig_melb_syd_avg_sa4_no = melb_syd_avg_sa4_no_fig.get_figure() #Assigning the figure to a new variable for further operations\n",
    "fig_melb_syd_avg_sa4_no.tight_layout() #To fit everything in the saved image\n",
    "fig_melb_syd_avg_sa4_no.subplots_adjust(top=0.93)\n",
    "fig_melb_syd_avg_sa4_no.suptitle('Comparison of SA4 Regions - Mean | Without outliers',fontsize=15,fontweight='bold') #Title for the figure\n",
    "fig_melb_syd_avg_sa4_no.savefig(\"figures/melb_syd_avg_sa4_no_outliers.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check out how important 'Southbank' and 'Banksmeadow' are to Melbourne or Sydney's Walkability by taking the absolute difference of the means with and without those outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   SumZScore\n",
      "sa4_name11                  \n",
      "Melbourne - Inner  -0.000008\n",
      "                               SumZScore\n",
      "sa4_name11                              \n",
      "Sydney - City and Inner South  -0.000127\n",
      "                   SumZScore\n",
      "sa4_name11                  \n",
      "Melbourne - Inner  -0.164996\n",
      "                               SumZScore\n",
      "sa4_name11                              \n",
      "Sydney - City and Inner South    0.00799\n",
      "\n",
      "\n",
      "\n",
      "Differences\n",
      "                   SumZScore\n",
      "sa4_name11                  \n",
      "Melbourne - Inner   0.164987\n",
      "                               SumZScore\n",
      "sa4_name11                              \n",
      "Sydney - City and Inner South   0.008118\n"
     ]
    }
   ],
   "source": [
    "#All means\n",
    "print avg_sa4 \n",
    "print savg_sa4\n",
    "print avg_sa4_no_south\n",
    "print savg_sa4_no_banks\n",
    "print \"\\n\\n\"\n",
    "print \"Differences\"\n",
    "\n",
    "print abs(avg_sa4 - avg_sa4_no_south)\n",
    "print abs(savg_sa4 - savg_sa4_no_banks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, it seems that Sydney has a slight edge to Melbourne in our comparison, but that might just be due to the area of comparison. In conclusion, both cities, in specific, their inner city areas are well developed, making them optimum walkable areas.\n"
   ]
  }
 ],
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