data/Import_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   Bayview\n",
       "1            Bernal Heights\n",
       "2       Castro/Upper Market\n",
       "3                 Chinatown\n",
       "4            Crocker Amazon\n",
       "5           Diamond Heights\n",
       "6     Downtown/Civic Center\n",
       "7                 Excelsior\n",
       "8        Financial District\n",
       "9                 Glen Park\n",
       "10         Golden Gate Park\n",
       "11           Haight Ashbury\n",
       "12           Inner Richmond\n",
       "13             Inner Sunset\n",
       "14                Lakeshore\n",
       "15                   Marina\n",
       "16                  Mission\n",
       "17                 Nob Hill\n",
       "18               Noe Valley\n",
       "19              North Beach\n",
       "20               Ocean View\n",
       "21            Outer Mission\n",
       "22           Outer Richmond\n",
       "23             Outer Sunset\n",
       "24          Pacific Heights\n",
       "25                 Parkside\n",
       "26             Potrero Hill\n",
       "27                 Presidio\n",
       "28         Presidio Heights\n",
       "29             Russian Hill\n",
       "30                 Seacliff\n",
       "31          South of Market\n",
       "32      Treasure Island/YBI\n",
       "33               Twin Peaks\n",
       "34        Visitacion Valley\n",
       "35       West of Twin Peaks\n",
       "36         Western Addition\n",
       "Name: neighbourhood, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set dtypes for each column later.\n",
    "data = pd.read_csv('listings.csv', na_values=[\"\"], dtype=object)\n",
    "\n",
    "# Reads the neighbourhood csv file\n",
    "neighbourhoods = pd.read_csv('neighbourhoods.csv', na_values=[\"\"], dtype=object, usecols=range(1,2))\n",
    "\n",
    "#data\n",
    "\n",
    "# Sets the price column to floats without the '$' for computations\n",
    "data['price'] = (data['price'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "\n",
    "# Set ratings to floats\n",
    "data['review_scores_accuracy'] = (data['review_scores_accuracy'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "data['review_scores_cleanliness'] = (data['review_scores_cleanliness'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "data['review_scores_checkin'] = (data['review_scores_checkin'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "data['review_scores_communication'] = (data['review_scores_communication'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "data['review_scores_location'] = (data['review_scores_location'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "data['review_scores_value'] = (data['review_scores_value'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "data['review_scores_rating'] = (data['review_scores_rating'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "\n",
    "neighbourhoods['neighbourhood']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compare price means in each neighbourhood\n",
    "prices = (data['price'].replace( '[\\$,)]','', regex=True )\n",
    "               .replace( '[(]','-',   regex=True ).astype(float))\n",
    "\n",
    "#neighbourhoods = data['neighbourhood']\n",
    "bedrooms = data['bedrooms']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bayview ,Bernal Heights ,Castro/Upper Market ,Chinatown ,Crocker Amazon ,Diamond Heights ,Downtown/Civic Center ,Excelsior ,Financial District ,Glen Park ,Golden Gate Park ,Haight Ashbury ,Inner Richmond ,Inner Sunset ,Lakeshore ,Marina ,Mission ,Nob Hill ,Noe Valley ,North Beach ,Ocean View ,Outer Mission ,Outer Richmond ,Outer Sunset ,Pacific Heights ,Parkside ,Potrero Hill ,Presidio ,Presidio Heights ,Russian Hill ,Seacliff ,South of Market ,Treasure Island/YBI ,Twin Peaks ,Visitacion Valley ,West of Twin Peaks ,Western Addition ,"
     ]
    },
    {
     "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>Neighbourhood</th>\n",
       "      <th>Average Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bayview</td>\n",
       "      <td>95.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bernal Heights</td>\n",
       "      <td>132.422311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Castro/Upper Market</td>\n",
       "      <td>169.183453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinatown</td>\n",
       "      <td>198.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crocker Amazon</td>\n",
       "      <td>85.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Diamond Heights</td>\n",
       "      <td>144.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Downtown/Civic Center</td>\n",
       "      <td>216.488746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Excelsior</td>\n",
       "      <td>85.299065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Financial District</td>\n",
       "      <td>209.198113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Glen Park</td>\n",
       "      <td>143.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Golden Gate Park</td>\n",
       "      <td>121.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Haight Ashbury</td>\n",
       "      <td>144.162879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Inner Richmond</td>\n",
       "      <td>135.460227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Inner Sunset</td>\n",
       "      <td>146.594340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lakeshore</td>\n",
       "      <td>119.102564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Marina</td>\n",
       "      <td>199.373684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mission</td>\n",
       "      <td>146.795714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nob Hill</td>\n",
       "      <td>180.144144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Noe Valley</td>\n",
       "      <td>173.558036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>North Beach</td>\n",
       "      <td>176.279279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ocean View</td>\n",
       "      <td>114.518987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Outer Mission</td>\n",
       "      <td>122.688679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Outer Richmond</td>\n",
       "      <td>133.915254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Outer Sunset</td>\n",
       "      <td>111.871951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>191.014085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Parkside</td>\n",
       "      <td>199.012987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Potrero Hill</td>\n",
       "      <td>243.884146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Presidio</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Presidio Heights</td>\n",
       "      <td>174.814815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Russian Hill</td>\n",
       "      <td>211.225564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Seacliff</td>\n",
       "      <td>146.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>South of Market</td>\n",
       "      <td>258.221978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Treasure Island/YBI</td>\n",
       "      <td>93.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Twin Peaks</td>\n",
       "      <td>145.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Visitacion Valley</td>\n",
       "      <td>112.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>West of Twin Peaks</td>\n",
       "      <td>118.564103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Western Addition</td>\n",
       "      <td>165.983740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Neighbourhood  Average Price\n",
       "0                 Bayview      95.055556\n",
       "1          Bernal Heights     132.422311\n",
       "2     Castro/Upper Market     169.183453\n",
       "3               Chinatown     198.760000\n",
       "4          Crocker Amazon      85.900000\n",
       "5         Diamond Heights     144.153846\n",
       "6   Downtown/Civic Center     216.488746\n",
       "7               Excelsior      85.299065\n",
       "8      Financial District     209.198113\n",
       "9               Glen Park     143.800000\n",
       "10       Golden Gate Park     121.250000\n",
       "11         Haight Ashbury     144.162879\n",
       "12         Inner Richmond     135.460227\n",
       "13           Inner Sunset     146.594340\n",
       "14              Lakeshore     119.102564\n",
       "15                 Marina     199.373684\n",
       "16                Mission     146.795714\n",
       "17               Nob Hill     180.144144\n",
       "18             Noe Valley     173.558036\n",
       "19            North Beach     176.279279\n",
       "20             Ocean View     114.518987\n",
       "21          Outer Mission     122.688679\n",
       "22         Outer Richmond     133.915254\n",
       "23           Outer Sunset     111.871951\n",
       "24        Pacific Heights     191.014085\n",
       "25               Parkside     199.012987\n",
       "26           Potrero Hill     243.884146\n",
       "27               Presidio     160.000000\n",
       "28       Presidio Heights     174.814815\n",
       "29           Russian Hill     211.225564\n",
       "30               Seacliff     146.333333\n",
       "31        South of Market     258.221978\n",
       "32    Treasure Island/YBI      93.421053\n",
       "33             Twin Peaks     145.043478\n",
       "34      Visitacion Valley     112.692308\n",
       "35     West of Twin Peaks     118.564103\n",
       "36       Western Addition     165.983740"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PvN = np.c_[prices, neighbourhoods]\n",
    "#PvN[1][0]\n",
    "\n",
    "mission_district = data\n",
    "data['id']\n",
    "\n",
    "# Get prices for a specific neighbourhood\n",
    "specific = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][0]) & (data['bedrooms'] == '1')]['price']\n",
    "\n",
    "w = 1\n",
    "h = len(neighbourhoods);\n",
    "CoL = [[0 for x in range(w)] for y in range(h)]\n",
    "\n",
    "for i in range(len(neighbourhoods)):\n",
    "    neighbourhood_listings = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i]) & (data['bedrooms'] == '1')]['price']\n",
    "    avg_price = np.mean(neighbourhood_listings, 0)\n",
    "    CoL[i][0] = neighbourhoods['neighbourhood'][i]\n",
    "    CoL[i].append(avg_price)\n",
    "    print(neighbourhoods['neighbourhood'][i], ',', end=\"\")\n",
    "    \n",
    "col_df = pd.DataFrame(CoL,columns=['Neighbourhood','Average Price'])\n",
    "col_df\n",
    "#col_df.to_csv('col.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bae1ab29b0>"
      ]
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     "execution_count": 6,
     "metadata": {},
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    {
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Fnt+3t6iOB96dZEqS+2lV4AF8GTgzyW09Daqq+4EPA5cn2Rg4K8lUYCpwQ/PR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VQXvi7JI8D/Af8y1pglSZIkSZK0/OTxnaRaWSRZr1Uarg6cB5xSVectr/X6+vqqv79/eU0vSZIk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bad9015be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_df.plot(title='Average Price', x='Neighbourhood', y='Average Price', kind='barh', figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.05555555555556"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#avg_price = np.mean(PvN[:1], 0)\n",
    "# Should find the average price for each neighbourhood based on number of rooms.\n",
    "avg_price = np.mean(specific, 0)\n",
    "avg_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#CoL_plot = data.plot(neighbourhoods, kind='bar', legend=None)\n",
    "#specific.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#some ideas...\n",
    "# Graph of the means per neighbourhood for 1 room- determine highest CoL area\n",
    "# Regression - relationship between # of bedrooms and price per night for each neighbourhood\n",
    "# Bed to bathroom ratio for each # of rooms for each neighbourhood\n",
    "# \n",
    "# For look up - display location on map, review scores, pretty much all listing data ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.05555555555556 ,132.4223107569721 ,169.18345323741008 ,198.76 ,85.9 ,144.15384615384616 ,216.4887459807074 ,85.29906542056075 ,209.19811320754718 ,143.8 ,121.25 ,144.16287878787878 ,135.46022727272728 ,146.5943396226415 ,119.1025641025641 ,199.3736842105263 ,146.7957142857143 ,180.14414414414415 ,173.55803571428572 ,176.27927927927928 ,114.51898734177215 ,122.68867924528301 ,133.91525423728814 ,111.8719512195122 ,191.01408450704224 ,199.01298701298703 ,243.8841463414634 ,160.0 ,174.8148148148148 ,211.22556390977442 ,146.33333333333334 ,258.22197802197803 ,93.42105263157895 ,145.04347826086956 ,112.6923076923077 ,118.56410256410257 ,165.98373983739836 ,"
     ]
    },
    {
     "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>Neighbourhood</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bayview</td>\n",
       "      <td>95.055556</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>344.666667</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bernal Heights</td>\n",
       "      <td>132.422311</td>\n",
       "      <td>242.926316</td>\n",
       "      <td>326.450000</td>\n",
       "      <td>496.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Castro/Upper Market</td>\n",
       "      <td>169.183453</td>\n",
       "      <td>308.049505</td>\n",
       "      <td>447.415094</td>\n",
       "      <td>694.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chinatown</td>\n",
       "      <td>198.760000</td>\n",
       "      <td>276.666667</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crocker Amazon</td>\n",
       "      <td>85.900000</td>\n",
       "      <td>137.333333</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Diamond Heights</td>\n",
       "      <td>144.153846</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Downtown/Civic Center</td>\n",
       "      <td>216.488746</td>\n",
       "      <td>395.029412</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Excelsior</td>\n",
       "      <td>85.299065</td>\n",
       "      <td>174.375000</td>\n",
       "      <td>442.636364</td>\n",
       "      <td>444.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Financial District</td>\n",
       "      <td>209.198113</td>\n",
       "      <td>532.523810</td>\n",
       "      <td>1240.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Glen Park</td>\n",
       "      <td>143.800000</td>\n",
       "      <td>241.750000</td>\n",
       "      <td>430.500000</td>\n",
       "      <td>445.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Golden Gate Park</td>\n",
       "      <td>121.250000</td>\n",
       "      <td>171.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>889.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Haight Ashbury</td>\n",
       "      <td>144.162879</td>\n",
       "      <td>318.454545</td>\n",
       "      <td>494.580645</td>\n",
       "      <td>783.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Inner Richmond</td>\n",
       "      <td>135.460227</td>\n",
       "      <td>315.428571</td>\n",
       "      <td>405.400000</td>\n",
       "      <td>586.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Inner Sunset</td>\n",
       "      <td>146.594340</td>\n",
       "      <td>323.370370</td>\n",
       "      <td>370.600000</td>\n",
       "      <td>1283.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lakeshore</td>\n",
       "      <td>119.102564</td>\n",
       "      <td>313.333333</td>\n",
       "      <td>463.333333</td>\n",
       "      <td>298.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Marina</td>\n",
       "      <td>199.373684</td>\n",
       "      <td>430.549020</td>\n",
       "      <td>789.681818</td>\n",
       "      <td>1081.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mission</td>\n",
       "      <td>146.795714</td>\n",
       "      <td>280.438202</td>\n",
       "      <td>493.086420</td>\n",
       "      <td>715.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nob Hill</td>\n",
       "      <td>180.144144</td>\n",
       "      <td>324.297297</td>\n",
       "      <td>696.263158</td>\n",
       "      <td>959.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Noe Valley</td>\n",
       "      <td>173.558036</td>\n",
       "      <td>330.075000</td>\n",
       "      <td>485.250000</td>\n",
       "      <td>790.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>North Beach</td>\n",
       "      <td>176.279279</td>\n",
       "      <td>621.375000</td>\n",
       "      <td>628.818182</td>\n",
       "      <td>925.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ocean View</td>\n",
       "      <td>114.518987</td>\n",
       "      <td>194.263158</td>\n",
       "      <td>269.750000</td>\n",
       "      <td>488.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Outer Mission</td>\n",
       "      <td>122.688679</td>\n",
       "      <td>304.357143</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>252.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Outer Richmond</td>\n",
       "      <td>133.915254</td>\n",
       "      <td>208.722222</td>\n",
       "      <td>294.363636</td>\n",
       "      <td>943.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Outer Sunset</td>\n",
       "      <td>111.871951</td>\n",
       "      <td>209.549020</td>\n",
       "      <td>396.117647</td>\n",
       "      <td>527.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>191.014085</td>\n",
       "      <td>429.735849</td>\n",
       "      <td>715.076923</td>\n",
       "      <td>1522.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Parkside</td>\n",
       "      <td>199.012987</td>\n",
       "      <td>247.520000</td>\n",
       "      <td>387.250000</td>\n",
       "      <td>474.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Potrero Hill</td>\n",
       "      <td>243.884146</td>\n",
       "      <td>398.409836</td>\n",
       "      <td>529.935484</td>\n",
       "      <td>951.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Presidio</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Presidio Heights</td>\n",
       "      <td>174.814815</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>667.500000</td>\n",
       "      <td>3060.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Russian Hill</td>\n",
       "      <td>211.225564</td>\n",
       "      <td>460.632653</td>\n",
       "      <td>1069.000000</td>\n",
       "      <td>1544.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Seacliff</td>\n",
       "      <td>146.333333</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>386.666667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>South of Market</td>\n",
       "      <td>258.221978</td>\n",
       "      <td>592.147368</td>\n",
       "      <td>1231.428571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Treasure Island/YBI</td>\n",
       "      <td>93.421053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Twin Peaks</td>\n",
       "      <td>145.043478</td>\n",
       "      <td>293.842105</td>\n",
       "      <td>398.333333</td>\n",
       "      <td>711.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Visitacion Valley</td>\n",
       "      <td>112.692308</td>\n",
       "      <td>225.250000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>260.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>West of Twin Peaks</td>\n",
       "      <td>118.564103</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>462.888889</td>\n",
       "      <td>560.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Western Addition</td>\n",
       "      <td>165.983740</td>\n",
       "      <td>340.808511</td>\n",
       "      <td>447.111111</td>\n",
       "      <td>1484.758621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Neighbourhood           1           2             3           4+\n",
       "0                 Bayview   95.055556  187.000000    344.666667   400.000000\n",
       "1          Bernal Heights  132.422311  242.926316    326.450000   496.277778\n",
       "2     Castro/Upper Market  169.183453  308.049505    447.415094   694.235294\n",
       "3               Chinatown  198.760000  276.666667  10000.000000  1500.000000\n",
       "4          Crocker Amazon   85.900000  137.333333    315.000000   300.000000\n",
       "5         Diamond Heights  144.153846  284.000000    310.000000   295.000000\n",
       "6   Downtown/Civic Center  216.488746  395.029412    630.000000    84.000000\n",
       "7               Excelsior   85.299065  174.375000    442.636364   444.375000\n",
       "8      Financial District  209.198113  532.523810   1240.000000          NaN\n",
       "9               Glen Park  143.800000  241.750000    430.500000   445.500000\n",
       "10       Golden Gate Park  121.250000  171.500000           NaN   889.000000\n",
       "11         Haight Ashbury  144.162879  318.454545    494.580645   783.050000\n",
       "12         Inner Richmond  135.460227  315.428571    405.400000   586.043478\n",
       "13           Inner Sunset  146.594340  323.370370    370.600000  1283.333333\n",
       "14              Lakeshore  119.102564  313.333333    463.333333   298.000000\n",
       "15                 Marina  199.373684  430.549020    789.681818  1081.307692\n",
       "16                Mission  146.795714  280.438202    493.086420   715.050000\n",
       "17               Nob Hill  180.144144  324.297297    696.263158   959.833333\n",
       "18             Noe Valley  173.558036  330.075000    485.250000   790.529412\n",
       "19            North Beach  176.279279  621.375000    628.818182   925.000000\n",
       "20             Ocean View  114.518987  194.263158    269.750000   488.500000\n",
       "21          Outer Mission  122.688679  304.357143    362.000000   252.400000\n",
       "22         Outer Richmond  133.915254  208.722222    294.363636   943.222222\n",
       "23           Outer Sunset  111.871951  209.549020    396.117647   527.500000\n",
       "24        Pacific Heights  191.014085  429.735849    715.076923  1522.000000\n",
       "25               Parkside  199.012987  247.520000    387.250000   474.500000\n",
       "26           Potrero Hill  243.884146  398.409836    529.935484   951.250000\n",
       "27               Presidio  160.000000         NaN    699.000000   800.000000\n",
       "28       Presidio Heights  174.814815  356.000000    667.500000  3060.000000\n",
       "29           Russian Hill  211.225564  460.632653   1069.000000  1544.666667\n",
       "30               Seacliff  146.333333  243.000000    386.666667          NaN\n",
       "31        South of Market  258.221978  592.147368   1231.428571          NaN\n",
       "32    Treasure Island/YBI   93.421053         NaN           NaN          NaN\n",
       "33             Twin Peaks  145.043478  293.842105    398.333333   711.250000\n",
       "34      Visitacion Valley  112.692308  225.250000    235.000000   260.000000\n",
       "35     West of Twin Peaks  118.564103  217.000000    462.888889   560.833333\n",
       "36       Western Addition  165.983740  340.808511    447.111111  1484.758621"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regression graph data\n",
    "\n",
    "w = 1\n",
    "h = len(neighbourhoods);\n",
    "averages = [[0 for x in range(w)] for y in range(h)]\n",
    "\n",
    "for i in range(len(neighbourhoods)):\n",
    "    neighbourhood_listings1 = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i]) & (data['bedrooms'] == '1')]['price']\n",
    "    neighbourhood_listings2 = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i]) & (data['bedrooms'] == '2')]['price']\n",
    "    neighbourhood_listings3 = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i]) & (data['bedrooms'] == '3')]['price']\n",
    "    neighbourhood_listings4 = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i]) & (data['bedrooms'] >= '4')]['price']\n",
    "\n",
    "    avg_price1 = np.mean(neighbourhood_listings1, 0)\n",
    "    avg_price2 = np.mean(neighbourhood_listings2, 0)\n",
    "    avg_price3 = np.mean(neighbourhood_listings3, 0)\n",
    "    avg_price4 = np.mean(neighbourhood_listings4, 0)\n",
    "\n",
    "    averages[i][0] = neighbourhoods['neighbourhood'][i]\n",
    "    averages[i].append(avg_price1)\n",
    "    print(avg_price1, \",\", end=\"\")\n",
    "    averages[i].append(avg_price2)\n",
    "    averages[i].append(avg_price3)\n",
    "    averages[i].append(avg_price4)\n",
    "    \n",
    "avg_df = pd.DataFrame(averages,columns=['Neighbourhood','1', '2', '3', '4+'])\n",
    "avg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4244    999.0\n",
      "7456    690.0\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "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>1 Room</th>\n",
       "      <th>2 Rooms</th>\n",
       "      <th>3 Rooms</th>\n",
       "      <th>4 Rooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>160.082607</td>\n",
       "      <td>278.225319</td>\n",
       "      <td>411.962121</td>\n",
       "      <td>500.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>178.569492</td>\n",
       "      <td>374.651042</td>\n",
       "      <td>450.508143</td>\n",
       "      <td>641.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84.357143</td>\n",
       "      <td>494.090909</td>\n",
       "      <td>610.016949</td>\n",
       "      <td>793.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81.785714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>844.500000</td>\n",
       "      <td>936.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       1 Room     2 Rooms     3 Rooms     4 Rooms\n",
       "0  160.082607  278.225319  411.962121  500.588235\n",
       "1  178.569492  374.651042  450.508143  641.750000\n",
       "2   84.357143  494.090909  610.016949  793.800000\n",
       "3   81.785714         NaN  844.500000  936.285714"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Room to bathroom ratio graph\n",
    "\n",
    "w = 4\n",
    "h = 4\n",
    "room_to_bath = [[0 for x in range(w)] for y in range(h)]\n",
    "# Outliers\n",
    "excluded_data = data[data.price < 5000]\n",
    "for room in range(1, 5):\n",
    "    for bath in range(1, 5):\n",
    "        ratio = excluded_data.loc[(data['bathrooms'] == str(float(bath))) & (data['bedrooms'] == str(room))]['price']\n",
    "        if (room == 3 and bath == 4):\n",
    "                print(ratio)\n",
    "\n",
    "        \n",
    "        \n",
    "        avg_price = np.mean(ratio, 0)\n",
    "        room_to_bath[bath-1][room-1] = avg_price\n",
    "#         if room == 2 and bath == 4:\n",
    "#             print(ratio)\n",
    "        \n",
    "rtb_df = pd.DataFrame(room_to_bath,columns=['1 Room', '2 Rooms', '3 Rooms', '4 Rooms'])\n",
    "# for idx in range(4):\n",
    "#     print(rtb_df['3 Rooms'][idx])\n",
    "rtb_df\n",
    "# data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.01063829787235\n",
      "96.15151515151516\n",
      "96.15538847117794\n",
      "93.16\n",
      "91.21739130434783\n",
      "97.0\n",
      "89.656836461126\n",
      "92.30232558139535\n",
      "93.47\n",
      "96.01449275362319\n",
      "87.75\n",
      "94.04416403785488\n",
      "94.35321100917432\n",
      "94.93548387096774\n",
      "92.41176470588235\n",
      "95.17021276595744\n",
      "95.29928741092637\n",
      "93.48523206751055\n",
      "95.81651376146789\n",
      "94.2536231884058\n",
      "92.01098901098901\n",
      "95.38129496402878\n",
      "94.67785234899328\n",
      "94.33175355450237\n",
      "94.75757575757575\n",
      "93.70833333333333\n",
      "96.08968609865471\n",
      "97.66666666666667\n",
      "96.35483870967742\n",
      "94.9873417721519\n",
      "95.27777777777777\n",
      "93.82742316784869\n",
      "91.6470588235294\n",
      "96.21794871794872\n",
      "94.21875\n",
      "96.05319148936171\n",
      "95.31206896551724\n"
     ]
    }
   ],
   "source": [
    "# Radar Review Graph\n",
    "\n",
    "w = 1\n",
    "h = len(neighbourhoods);\n",
    "ratings = [[0 for x in range(w)] for y in range(h)]\n",
    "\n",
    "for i in range(len(neighbourhoods)):\n",
    "    accuracy = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i])]['review_scores_accuracy']\n",
    "    cleanliness= data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i])]['review_scores_cleanliness']\n",
    "    checkin = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i])]['review_scores_checkin']\n",
    "    communication = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i])]['review_scores_communication']\n",
    "    location = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i])]['review_scores_location']\n",
    "    value = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i])]['review_scores_value']\n",
    "    overall = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i])]['review_scores_rating']\n",
    "    \n",
    "    avg_accuracy = np.mean(accuracy, 0)\n",
    "    avg_cleanliness = np.mean(cleanliness, 0)\n",
    "    avg_checkin = np.mean(checkin, 0)\n",
    "    avg_communication = np.mean(communication, 0)\n",
    "    avg_location = np.mean(location, 0)\n",
    "    avg_value = np.mean(value, 0)\n",
    "    avg_overall = np.mean(overall, 0)\n",
    "  \n",
    "\n",
    "    ratings[i][0] = neighbourhoods['neighbourhood'][i]\n",
    "    ratings[i].append(avg_accuracy)\n",
    "    ratings[i].append(avg_cleanliness)\n",
    "    ratings[i].append(avg_checkin)\n",
    "    ratings[i].append(avg_communication)\n",
    "    ratings[i].append(avg_location)\n",
    "    ratings[i].append(avg_value)\n",
    "    ratings[i].append(avg_overall)\n",
    "    print(ratings[i][7])\n",
    "    \n",
    "rating_df = pd.DataFrame(ratings,columns=['Neighbourhood', 'Accuracy', 'Cleanliness', 'Check-in', 'Communication', 'Location', 'Value', 'Overall Rating'])\n",
    "rating_df\n",
    "\n",
    "rating_df.to_csv('rating_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>rooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.7541839478958</td>\n",
       "      <td>-122.4065137873994</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.75416620694072</td>\n",
       "      <td>-122.42153361123903</td>\n",
       "      <td>220.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.758505841987684</td>\n",
       "      <td>-122.40615234615572</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.75654875319113</td>\n",
       "      <td>-122.42202512272395</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.76005055162609</td>\n",
       "      <td>-122.42135244186619</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37.759494875174084</td>\n",
       "      <td>-122.42487369983884</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37.76099686978345</td>\n",
       "      <td>-122.41312370743405</td>\n",
       "      <td>230.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37.75054282250833</td>\n",
       "      <td>-122.41655854238532</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37.75628817825092</td>\n",
       "      <td>-122.40873765927407</td>\n",
       "      <td>600.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37.757246417145325</td>\n",
       "      <td>-122.40931471446184</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37.74932934983145</td>\n",
       "      <td>-122.41079246439557</td>\n",
       "      <td>300.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>37.75135310287518</td>\n",
       "      <td>-122.41765884005434</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>37.751992462976204</td>\n",
       "      <td>-122.40421045324041</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>37.76142933262309</td>\n",
       "      <td>-122.41935005071524</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>37.766520788195436</td>\n",
       "      <td>-122.4246600892409</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>37.7599551383521</td>\n",
       "      <td>-122.42410641814496</td>\n",
       "      <td>325.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>37.758701665232124</td>\n",
       "      <td>-122.42124003051889</td>\n",
       "      <td>119.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>37.76147948623888</td>\n",
       "      <td>-122.41571353188468</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37.75939066340097</td>\n",
       "      <td>-122.42116735753424</td>\n",
       "      <td>490.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>37.749617823996026</td>\n",
       "      <td>-122.41813141973981</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>37.759585813647796</td>\n",
       "      <td>-122.41000109683404</td>\n",
       "      <td>185.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>37.75560550525029</td>\n",
       "      <td>-122.40671929926322</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>37.75052129599319</td>\n",
       "      <td>-122.40735678108761</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>37.76832742785573</td>\n",
       "      <td>-122.42505707071774</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37.768887191674914</td>\n",
       "      <td>-122.42405987916307</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>37.7513430565728</td>\n",
       "      <td>-122.42265006283215</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>37.755069551371655</td>\n",
       "      <td>-122.41681709924057</td>\n",
       "      <td>169.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>37.76220649847767</td>\n",
       "      <td>-122.41989540417708</td>\n",
       "      <td>186.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>37.75385200146517</td>\n",
       "      <td>-122.41115766043127</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>37.76251893043086</td>\n",
       "      <td>-122.42428458725807</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8676</th>\n",
       "      <td>37.76846904745975</td>\n",
       "      <td>-122.42480278907435</td>\n",
       "      <td>170.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8677</th>\n",
       "      <td>37.75090953135027</td>\n",
       "      <td>-122.41381122914737</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8678</th>\n",
       "      <td>37.76110411293791</td>\n",
       "      <td>-122.41974220810849</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8679</th>\n",
       "      <td>37.76740963559629</td>\n",
       "      <td>-122.41466329052969</td>\n",
       "      <td>450.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8680</th>\n",
       "      <td>37.750123432098256</td>\n",
       "      <td>-122.42133307192641</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8681</th>\n",
       "      <td>37.766266800086235</td>\n",
       "      <td>-122.42473757776204</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8682</th>\n",
       "      <td>37.753751068336015</td>\n",
       "      <td>-122.41764604026453</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8683</th>\n",
       "      <td>37.75745809273714</td>\n",
       "      <td>-122.42313560663133</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8684</th>\n",
       "      <td>37.75074152100561</td>\n",
       "      <td>-122.4113174271127</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8685</th>\n",
       "      <td>37.75168248177475</td>\n",
       "      <td>-122.40601607626824</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8686</th>\n",
       "      <td>37.762530802125575</td>\n",
       "      <td>-122.41625999471223</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8687</th>\n",
       "      <td>37.76726458593008</td>\n",
       "      <td>-122.42640583424895</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8688</th>\n",
       "      <td>37.76900457198222</td>\n",
       "      <td>-122.42362081232133</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8689</th>\n",
       "      <td>37.75990454917188</td>\n",
       "      <td>-122.42016255572486</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8690</th>\n",
       "      <td>37.75991138550237</td>\n",
       "      <td>-122.4106486486969</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8691</th>\n",
       "      <td>37.752917715443324</td>\n",
       "      <td>-122.41904909964842</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8692</th>\n",
       "      <td>37.76060946025367</td>\n",
       "      <td>-122.41964117931072</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8693</th>\n",
       "      <td>37.76202211098672</td>\n",
       "      <td>-122.41876038242056</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8694</th>\n",
       "      <td>37.75802513613258</td>\n",
       "      <td>-122.40977862466657</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8695</th>\n",
       "      <td>37.75332068374915</td>\n",
       "      <td>-122.41799285267265</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8696</th>\n",
       "      <td>37.75778075316126</td>\n",
       "      <td>-122.42063888002036</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8697</th>\n",
       "      <td>37.756012031573434</td>\n",
       "      <td>-122.4110670990613</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8698</th>\n",
       "      <td>37.76852807846753</td>\n",
       "      <td>-122.4220284066595</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8699</th>\n",
       "      <td>37.75045501756861</td>\n",
       "      <td>-122.41401120504423</td>\n",
       "      <td>295.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8700</th>\n",
       "      <td>37.76733277541302</td>\n",
       "      <td>-122.42373527266868</td>\n",
       "      <td>285.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8701</th>\n",
       "      <td>37.75384870492505</td>\n",
       "      <td>-122.42265517114879</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8702</th>\n",
       "      <td>37.75499269667693</td>\n",
       "      <td>-122.41128530046475</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8703</th>\n",
       "      <td>37.75123108221792</td>\n",
       "      <td>-122.40921079990189</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8704</th>\n",
       "      <td>37.76981890402803</td>\n",
       "      <td>-122.42160383626248</td>\n",
       "      <td>125.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8705</th>\n",
       "      <td>37.7588220386069</td>\n",
       "      <td>-122.42348240360832</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8706 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Latitude            Longitude   price rooms\n",
       "0       37.7541839478958   -122.4065137873994    49.0     1\n",
       "1      37.75416620694072  -122.42153361123903   220.0     2\n",
       "2     37.758505841987684  -122.40615234615572   100.0     1\n",
       "3      37.75654875319113  -122.42202512272395   117.0     1\n",
       "4      37.76005055162609  -122.42135244186619   200.0     1\n",
       "5     37.759494875174084  -122.42487369983884   162.0     1\n",
       "6      37.76099686978345  -122.41312370743405   230.0     2\n",
       "7      37.75054282250833  -122.41655854238532   173.0     1\n",
       "8      37.75628817825092  -122.40873765927407   600.0     2\n",
       "9     37.757246417145325  -122.40931471446184   250.0     2\n",
       "10     37.74932934983145  -122.41079246439557   300.0     3\n",
       "11     37.75135310287518  -122.41765884005434   189.0     2\n",
       "12    37.751992462976204  -122.40421045324041   130.0     1\n",
       "13     37.76142933262309  -122.41935005071524    90.0     1\n",
       "14    37.766520788195436   -122.4246600892409    95.0     1\n",
       "15      37.7599551383521  -122.42410641814496   325.0     3\n",
       "16    37.758701665232124  -122.42124003051889   119.0     1\n",
       "17     37.76147948623888  -122.41571353188468   134.0     1\n",
       "18     37.75939066340097  -122.42116735753424   490.0     3\n",
       "19    37.749617823996026  -122.41813141973981   140.0     1\n",
       "20    37.759585813647796  -122.41000109683404   185.0     1\n",
       "21     37.75560550525029  -122.40671929926322   550.0     1\n",
       "22     37.75052129599319  -122.40735678108761   113.0     1\n",
       "23     37.76832742785573  -122.42505707071774   115.0     1\n",
       "24    37.768887191674914  -122.42405987916307   155.0     0\n",
       "25      37.7513430565728  -122.42265006283215    45.0     1\n",
       "26    37.755069551371655  -122.41681709924057   169.0     1\n",
       "27     37.76220649847767  -122.41989540417708   186.0     1\n",
       "28     37.75385200146517  -122.41115766043127    77.0     1\n",
       "29     37.76251893043086  -122.42428458725807   250.0     2\n",
       "...                  ...                  ...     ...   ...\n",
       "8676   37.76846904745975  -122.42480278907435   170.0     2\n",
       "8677   37.75090953135027  -122.41381122914737   170.0     1\n",
       "8678   37.76110411293791  -122.41974220810849  1500.0     3\n",
       "8679   37.76740963559629  -122.41466329052969   450.0     4\n",
       "8680  37.750123432098256  -122.42133307192641   210.0     1\n",
       "8681  37.766266800086235  -122.42473757776204    80.0     1\n",
       "8682  37.753751068336015  -122.41764604026453    75.0     1\n",
       "8683   37.75745809273714  -122.42313560663133   130.0     1\n",
       "8684   37.75074152100561   -122.4113174271127   114.0     2\n",
       "8685   37.75168248177475  -122.40601607626824   149.0     1\n",
       "8686  37.762530802125575  -122.41625999471223   115.0     1\n",
       "8687   37.76726458593008  -122.42640583424895   109.0     1\n",
       "8688   37.76900457198222  -122.42362081232133    87.0     1\n",
       "8689   37.75990454917188  -122.42016255572486   300.0     2\n",
       "8690   37.75991138550237   -122.4106486486969   250.0     2\n",
       "8691  37.752917715443324  -122.41904909964842   250.0     2\n",
       "8692   37.76060946025367  -122.41964117931072   245.0     2\n",
       "8693   37.76202211098672  -122.41876038242056   179.0     2\n",
       "8694   37.75802513613258  -122.40977862466657   150.0     1\n",
       "8695   37.75332068374915  -122.41799285267265   120.0     1\n",
       "8696   37.75778075316126  -122.42063888002036   100.0     0\n",
       "8697  37.756012031573434   -122.4110670990613    60.0     1\n",
       "8698   37.76852807846753   -122.4220284066595   140.0     1\n",
       "8699   37.75045501756861  -122.41401120504423   295.0     2\n",
       "8700   37.76733277541302  -122.42373527266868   285.0     2\n",
       "8701   37.75384870492505  -122.42265517114879    75.0     1\n",
       "8702   37.75499269667693  -122.41128530046475   250.0     2\n",
       "8703   37.75123108221792  -122.40921079990189   120.0     1\n",
       "8704   37.76981890402803  -122.42160383626248   125.0     1\n",
       "8705    37.7588220386069  -122.42348240360832   100.0     1\n",
       "\n",
       "[8706 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all geocoordinates and prices\n",
    "\n",
    "w = 1\n",
    "h = len(data);\n",
    "geocodes = [[0 for x in range(w)] for y in range(h)]\n",
    "\n",
    "for i in range(len(data)):\n",
    "#     neighbourhood_listings = data.loc[(data['neighbourhood_cleansed'] == neighbourhoods['neighbourhood'][i]) & (data['bedrooms'] == '1')]['price']\n",
    "#     avg_price = np.mean(neighbourhood_listings, 0)\n",
    "#     geocodes[i][0] = \"placeholder\"\n",
    "    geocodes[i][0] = (data['latitude'][i])\n",
    "    geocodes[i].append(data['longitude'][i])\n",
    "    geocodes[i].append(data['price'][i])\n",
    "    geocodes[i].append(data['bedrooms'][i])\n",
    "#     CoL[i].append(avg_price)\n",
    "#     print(neighbourhoods['neighbourhood'][i], ',', end=\"\")\n",
    "    \n",
    "    \n",
    "# col_df = pd.DataFrame(CoL,columns=['Neighbourhood','Average Price'])\n",
    "# col_df\n",
    "#col_df.to_csv('col.csv')\n",
    "\n",
    "# geocodes.append(data['latitude'])\n",
    "# geocodes.append(data['longitude'])\n",
    "# geocodes.append(data['price'])\n",
    "# geocodes[1][1]\n",
    "\n",
    "geocodes_df = pd.DataFrame(geocodes,columns=['Latitude','Longitude','price', 'rooms'])\n",
    "geocodes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Latitude       37.83109278506102\n",
       "Longitude    -122.51149998987212\n",
       "price                      10000\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geocodes_df.min()\n",
    "geocodes_df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# col_df.to_csv('col_data.csv')\n",
    "\n",
    "# avg_df.to_csv('regression_data.csv')\n",
    "\n",
    "rtb_df.to_csv('room_to_bath_data.csv')\n",
    "\n",
    "# geocodes_df.to_csv('geocode_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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