Baseline Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (22,73,74) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "df_withlabel = pd.read_csv('datasets/listings_features_with_label.csv', index_col=['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_withlabel = df_withlabel[['Demo % - ACS 2011 Means of transportation: Public transportation - Subway - Zipcode',\n",
    "       'Demo % - Age - Age 00 - 14 - Zipcode - ACS 2011',\n",
    "       'Demo % - Age - Age 15 - 29 - Zipcode - ACS 2011',\n",
    "       'Demo % - Age - Age 30 - 44 - Zipcode - ACS 2011',\n",
    "       'Demo % - Age - Age 45 - 59 - Zipcode - ACS 2011',\n",
    "       'Demo % - Age - Age 60 + - Zipcode - ACS 2011',\n",
    "       'Demo % - Education - Pop 25 yrs + Bachelors Degree - Zipcode - ACS 2011',\n",
    "       'Demo % - Education - Pop 25 yrs + Masters degree - Zipcode - ACS 2011',\n",
    "       'Demo % - Ethnicity - Non-White Population - Zipcode - ACS 2011',\n",
    "       'Demo % - Households - Household Size: 1-2 Persons - Zipcode - ACS 2011',\n",
    "       'Demo % - Households - Household Size: 3+ Persons - Zipcode - ACS 2011',\n",
    "       'Demo % - Households - Household Size: 7 + Persons - Zipcode - ACS 2011',\n",
    "       'Demo % - Income - Hhld Income $100,000 + % - Zipcode - ACS 2011',\n",
    "       'Demo % - Population - Female Population - Zipcode - ACS 2011',\n",
    "       'Demo % - Population - Male Population - Zipcode - ACS 2011',\n",
    "       'Demo - Age - Median Age Total Population - Zipcode - ACS 2011',\n",
    "       'Demo - Households - Median Household Income - Zipcode - ACS 2011',\n",
    "       'Demo - Population Density - Total - Zipcode - ACS 2011', 'Label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/merged_wo_features_df.csv', index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = ['Unnamed: 0', 'Unnamed: 0.1','Unnamed: 0.1.1', 'Unnamed: 0.1.1.1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>last_scraped</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "      <th>...</th>\n",
       "      <th>idpop_citywide_food_sum</th>\n",
       "      <th>idpop_local_nightlife_sum</th>\n",
       "      <th>idpop_citywide_nightlife_sum</th>\n",
       "      <th>idpop_local_travel_sum</th>\n",
       "      <th>idpop_citywide_travel_sum</th>\n",
       "      <th>venue_count_500m</th>\n",
       "      <th>sub_cnt_20m</th>\n",
       "      <th>sub__line_cnt_20m</th>\n",
       "      <th>comp</th>\n",
       "      <th>comp_hotel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5435.0</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Upper West Side</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10025</td>\n",
       "      <td>40.80155</td>\n",
       "      <td>-73.97000</td>\n",
       "      <td>POINT (-73.97 40.80155)</td>\n",
       "      <td>...</td>\n",
       "      <td>1.282677</td>\n",
       "      <td>2.308846</td>\n",
       "      <td>0.946402</td>\n",
       "      <td>2.069520</td>\n",
       "      <td>0.905596</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3126</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20311.0</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Prospect Heights</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11238</td>\n",
       "      <td>40.67206</td>\n",
       "      <td>-73.96344</td>\n",
       "      <td>POINT (-73.96344000000001 40.67206)</td>\n",
       "      <td>...</td>\n",
       "      <td>0.597798</td>\n",
       "      <td>0.003813</td>\n",
       "      <td>0.496540</td>\n",
       "      <td>0.003103</td>\n",
       "      <td>0.494220</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4805</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27024.0</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10001</td>\n",
       "      <td>40.74968</td>\n",
       "      <td>-74.00052</td>\n",
       "      <td>POINT (-74.00051999999999 40.74968)</td>\n",
       "      <td>...</td>\n",
       "      <td>1.750055</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>3.074077</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>2.756507</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5777</td>\n",
       "      <td>1079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62264.0</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.72159</td>\n",
       "      <td>-73.96018</td>\n",
       "      <td>POINT (-73.96018000000002 40.72159)</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096799</td>\n",
       "      <td>0.003115</td>\n",
       "      <td>0.997872</td>\n",
       "      <td>0.004294</td>\n",
       "      <td>0.912083</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5690</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70381.0</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>Soho</td>\n",
       "      <td>SoHo</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10013</td>\n",
       "      <td>40.72195</td>\n",
       "      <td>-74.00356</td>\n",
       "      <td>POINT (-74.00355999999999 40.72195)</td>\n",
       "      <td>...</td>\n",
       "      <td>4.359837</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>1.935436</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>1.409476</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>5956</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136807.0</th>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10036</td>\n",
       "      <td>40.76186</td>\n",
       "      <td>-73.99834</td>\n",
       "      <td>POINT (-73.99834 40.76186)</td>\n",
       "      <td>...</td>\n",
       "      <td>1.274203</td>\n",
       "      <td>4.709646</td>\n",
       "      <td>2.345423</td>\n",
       "      <td>4.274339</td>\n",
       "      <td>2.742672</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4503</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40142730.0</th>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10036</td>\n",
       "      <td>40.76104</td>\n",
       "      <td>-73.99150</td>\n",
       "      <td>POINT (-73.9915 40.76104)</td>\n",
       "      <td>...</td>\n",
       "      <td>1.401041</td>\n",
       "      <td>5.789506</td>\n",
       "      <td>2.500809</td>\n",
       "      <td>4.665473</td>\n",
       "      <td>2.938662</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4996</td>\n",
       "      <td>1441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40173530.0</th>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10026</td>\n",
       "      <td>40.80005</td>\n",
       "      <td>-73.95271</td>\n",
       "      <td>POINT (-73.95271 40.80005)</td>\n",
       "      <td>...</td>\n",
       "      <td>1.308795</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.929913</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.951481</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4027</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40293492.0</th>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10031</td>\n",
       "      <td>40.82973</td>\n",
       "      <td>-73.94749</td>\n",
       "      <td>POINT (-73.94749 40.82973)</td>\n",
       "      <td>...</td>\n",
       "      <td>0.512243</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>1.261972</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.733215</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2302</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40370819.0</th>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10019</td>\n",
       "      <td>40.76479</td>\n",
       "      <td>-73.98753</td>\n",
       "      <td>POINT (-73.98753000000001 40.76479000000001)</td>\n",
       "      <td>...</td>\n",
       "      <td>1.797050</td>\n",
       "      <td>6.468309</td>\n",
       "      <td>2.915099</td>\n",
       "      <td>3.920589</td>\n",
       "      <td>2.303129</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4769</td>\n",
       "      <td>1435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40135 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           last_scraped   neighbourhood neighbourhood_cleansed  \\\n",
       "id                                                               \n",
       "5435.0       2019-03-07       Manhattan        Upper West Side   \n",
       "20311.0      2019-03-07        Brooklyn       Prospect Heights   \n",
       "27024.0      2019-03-07         Chelsea                Chelsea   \n",
       "62264.0      2019-03-07    Williamsburg           Williamsburg   \n",
       "70381.0      2019-03-07            Soho                   SoHo   \n",
       "...                 ...             ...                    ...   \n",
       "40136807.0   2019-12-06  Hell's Kitchen         Hell's Kitchen   \n",
       "40142730.0   2019-12-06         Midtown         Hell's Kitchen   \n",
       "40173530.0   2019-12-06       Manhattan                 Harlem   \n",
       "40293492.0   2019-12-06       Manhattan                 Harlem   \n",
       "40370819.0   2019-12-05  Hell's Kitchen         Hell's Kitchen   \n",
       "\n",
       "           neighbourhood_group_cleansed      city state zipcode  latitude  \\\n",
       "id                                                                          \n",
       "5435.0                        Manhattan  New York    NY   10025  40.80155   \n",
       "20311.0                        Brooklyn  Brooklyn    NY   11238  40.67206   \n",
       "27024.0                       Manhattan  New York    NY   10001  40.74968   \n",
       "62264.0                        Brooklyn  Brooklyn    NY   11211  40.72159   \n",
       "70381.0                       Manhattan  New York    NY   10013  40.72195   \n",
       "...                                 ...       ...   ...     ...       ...   \n",
       "40136807.0                    Manhattan  New York    NY   10036  40.76186   \n",
       "40142730.0                    Manhattan  New York    NY   10036  40.76104   \n",
       "40173530.0                    Manhattan  New York    NY   10026  40.80005   \n",
       "40293492.0                    Manhattan  New York    NY   10031  40.82973   \n",
       "40370819.0                    Manhattan  New York    NY   10019  40.76479   \n",
       "\n",
       "            longitude                                      geometry  ...  \\\n",
       "id                                                                   ...   \n",
       "5435.0      -73.97000                       POINT (-73.97 40.80155)  ...   \n",
       "20311.0     -73.96344           POINT (-73.96344000000001 40.67206)  ...   \n",
       "27024.0     -74.00052           POINT (-74.00051999999999 40.74968)  ...   \n",
       "62264.0     -73.96018           POINT (-73.96018000000002 40.72159)  ...   \n",
       "70381.0     -74.00356           POINT (-74.00355999999999 40.72195)  ...   \n",
       "...               ...                                           ...  ...   \n",
       "40136807.0  -73.99834                    POINT (-73.99834 40.76186)  ...   \n",
       "40142730.0  -73.99150                     POINT (-73.9915 40.76104)  ...   \n",
       "40173530.0  -73.95271                    POINT (-73.95271 40.80005)  ...   \n",
       "40293492.0  -73.94749                    POINT (-73.94749 40.82973)  ...   \n",
       "40370819.0  -73.98753  POINT (-73.98753000000001 40.76479000000001)  ...   \n",
       "\n",
       "            idpop_citywide_food_sum  idpop_local_nightlife_sum  \\\n",
       "id                                                               \n",
       "5435.0                     1.282677                   2.308846   \n",
       "20311.0                    0.597798                   0.003813   \n",
       "27024.0                    1.750055                   0.000806   \n",
       "62264.0                    1.096799                   0.003115   \n",
       "70381.0                    4.359837                   0.000806   \n",
       "...                             ...                        ...   \n",
       "40136807.0                 1.274203                   4.709646   \n",
       "40142730.0                 1.401041                   5.789506   \n",
       "40173530.0                 1.308795                   0.000769   \n",
       "40293492.0                 0.512243                   0.000769   \n",
       "40370819.0                 1.797050                   6.468309   \n",
       "\n",
       "            idpop_citywide_nightlife_sum  idpop_local_travel_sum  \\\n",
       "id                                                                 \n",
       "5435.0                          0.946402                2.069520   \n",
       "20311.0                         0.496540                0.003103   \n",
       "27024.0                         3.074077                0.000806   \n",
       "62264.0                         0.997872                0.004294   \n",
       "70381.0                         1.935436                0.000806   \n",
       "...                                  ...                     ...   \n",
       "40136807.0                      2.345423                4.274339   \n",
       "40142730.0                      2.500809                4.665473   \n",
       "40173530.0                      0.929913                0.000769   \n",
       "40293492.0                      1.261972                0.000769   \n",
       "40370819.0                      2.915099                3.920589   \n",
       "\n",
       "            idpop_citywide_travel_sum  venue_count_500m  sub_cnt_20m  \\\n",
       "id                                                                     \n",
       "5435.0                       0.905596                12            1   \n",
       "20311.0                      0.494220                 8            1   \n",
       "27024.0                      2.756507                 3            3   \n",
       "62264.0                      0.912083                17            0   \n",
       "70381.0                      1.409476                39            8   \n",
       "...                               ...               ...          ...   \n",
       "40136807.0                   2.742672                 8            0   \n",
       "40142730.0                   2.938662                 3            1   \n",
       "40173530.0                   0.951481                11            2   \n",
       "40293492.0                   0.733215                 4            1   \n",
       "40370819.0                   2.303129                 4            4   \n",
       "\n",
       "            sub__line_cnt_20m  comp  comp_hotel  \n",
       "id                                               \n",
       "5435.0                      1  3126         190  \n",
       "20311.0                     3  4805          38  \n",
       "27024.0                     8  5777        1079  \n",
       "62264.0                     0  5690          54  \n",
       "70381.0                    20  5956         596  \n",
       "...                       ...   ...         ...  \n",
       "40136807.0                  0  4503        1414  \n",
       "40142730.0                  3  4996        1441  \n",
       "40173530.0                  5  4027         195  \n",
       "40293492.0                  1  2302          27  \n",
       "40370819.0                  9  4769        1435  \n",
       "\n",
       "[40135 rows x 23 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df.merge(df_withlabel, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.drop(columns =['last_scraped', 'neighbourhood_cleansed',\n",
    "       'neighbourhood_group_cleansed', 'city', 'state', 'zipcode',\n",
    "       'latitude', 'longitude', 'geometry'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.get_dummies(df_final, columns=['neighbourhood'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idpop_local_sum</th>\n",
       "      <th>idpop_citywide_sum</th>\n",
       "      <th>idpop_local_food_sum</th>\n",
       "      <th>idpop_citywide_food_sum</th>\n",
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       "      <th>idpop_citywide_travel_sum</th>\n",
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       "      <th>sub_cnt_20m</th>\n",
       "      <th>...</th>\n",
       "      <th>neighbourhood_West Village</th>\n",
       "      <th>neighbourhood_Westchester Village</th>\n",
       "      <th>neighbourhood_Westerleigh</th>\n",
       "      <th>neighbourhood_Whitestone</th>\n",
       "      <th>neighbourhood_Williamsbridge</th>\n",
       "      <th>neighbourhood_Williamsburg</th>\n",
       "      <th>neighbourhood_Windsor Terrace</th>\n",
       "      <th>neighbourhood_Woodhaven</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3168 rows × 229 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            idpop_local_sum  idpop_citywide_sum  idpop_local_food_sum  \\\n",
       "id                                                                      \n",
       "1433326.0          0.003415            0.975497              0.004235   \n",
       "4642269.0          0.003416            0.984790              0.004236   \n",
       "5260247.0          0.003416            0.992160              0.004236   \n",
       "7070705.0          0.003415            0.932460              0.004235   \n",
       "7649846.0          0.003415            0.918434              0.004235   \n",
       "...                     ...                 ...                   ...   \n",
       "38549920.0         0.003416            1.019706              0.004236   \n",
       "38752368.0         0.003416            1.029052              0.004237   \n",
       "39062074.0         0.003416            1.014063              0.004237   \n",
       "39251197.0         0.003415            0.930939              0.004235   \n",
       "39251559.0         0.003415            0.915058              0.004235   \n",
       "\n",
       "            idpop_citywide_food_sum  idpop_local_nightlife_sum  \\\n",
       "id                                                               \n",
       "1433326.0                  1.120538                   0.003115   \n",
       "4642269.0                  1.141218                   0.003115   \n",
       "5260247.0                  1.152432                   0.003115   \n",
       "7070705.0                  1.063380                   0.003114   \n",
       "7649846.0                  1.043656                   0.003114   \n",
       "...                             ...                        ...   \n",
       "38549920.0                 1.174236                   0.003115   \n",
       "38752368.0                 1.208095                   0.003116   \n",
       "39062074.0                 1.185546                   0.003115   \n",
       "39251197.0                 1.060094                   0.003114   \n",
       "39251559.0                 1.036536                   0.003114   \n",
       "\n",
       "            idpop_citywide_nightlife_sum  idpop_local_travel_sum  \\\n",
       "id                                                                 \n",
       "1433326.0                       0.934015                0.004294   \n",
       "4642269.0                       0.976371                0.004294   \n",
       "5260247.0                       0.985834                0.004295   \n",
       "7070705.0                       0.950462                0.004294   \n",
       "7649846.0                       0.920817                0.004293   \n",
       "...                                  ...                     ...   \n",
       "38549920.0                      0.946594                0.004295   \n",
       "38752368.0                      1.026027                0.004295   \n",
       "39062074.0                      1.020905                0.004295   \n",
       "39251197.0                      0.956384                0.004294   \n",
       "39251559.0                      0.939335                0.004293   \n",
       "\n",
       "            idpop_citywide_travel_sum  venue_count_500m  sub_cnt_20m  ...  \\\n",
       "id                                                                    ...   \n",
       "1433326.0                    0.856886                17            0  ...   \n",
       "4642269.0                    0.888313                16            0  ...   \n",
       "5260247.0                    0.895219                15            0  ...   \n",
       "7070705.0                    0.872462                24            1  ...   \n",
       "7649846.0                    0.847996                16            2  ...   \n",
       "...                               ...               ...          ...  ...   \n",
       "38549920.0                   0.868492                 9            0  ...   \n",
       "38752368.0                   0.923456                 8            0  ...   \n",
       "39062074.0                   0.920814                10            0  ...   \n",
       "39251197.0                   0.878322                23            1  ...   \n",
       "39251559.0                   0.865424                24            1  ...   \n",
       "\n",
       "            neighbourhood_West Village  neighbourhood_Westchester Village  \\\n",
       "id                                                                          \n",
       "1433326.0                            0                                  0   \n",
       "4642269.0                            0                                  0   \n",
       "5260247.0                            0                                  0   \n",
       "7070705.0                            0                                  0   \n",
       "7649846.0                            0                                  0   \n",
       "...                                ...                                ...   \n",
       "38549920.0                           0                                  0   \n",
       "38752368.0                           0                                  0   \n",
       "39062074.0                           0                                  0   \n",
       "39251197.0                           0                                  0   \n",
       "39251559.0                           0                                  0   \n",
       "\n",
       "            neighbourhood_Westerleigh  neighbourhood_Whitestone  \\\n",
       "id                                                                \n",
       "1433326.0                           0                         0   \n",
       "4642269.0                           0                         0   \n",
       "5260247.0                           0                         0   \n",
       "7070705.0                           0                         0   \n",
       "7649846.0                           0                         0   \n",
       "...                               ...                       ...   \n",
       "38549920.0                          0                         0   \n",
       "38752368.0                          0                         0   \n",
       "39062074.0                          0                         0   \n",
       "39251197.0                          0                         0   \n",
       "39251559.0                          0                         0   \n",
       "\n",
       "            neighbourhood_Williamsbridge  neighbourhood_Williamsburg  \\\n",
       "id                                                                     \n",
       "1433326.0                              0                           0   \n",
       "4642269.0                              0                           1   \n",
       "5260247.0                              0                           0   \n",
       "7070705.0                              0                           1   \n",
       "7649846.0                              0                           0   \n",
       "...                                  ...                         ...   \n",
       "38549920.0                             0                           0   \n",
       "38752368.0                             0                           1   \n",
       "39062074.0                             0                           0   \n",
       "39251197.0                             0                           1   \n",
       "39251559.0                             0                           0   \n",
       "\n",
       "            neighbourhood_Windsor Terrace  neighbourhood_Woodhaven  \\\n",
       "id                                                                   \n",
       "1433326.0                               0                        0   \n",
       "4642269.0                               0                        0   \n",
       "5260247.0                               0                        0   \n",
       "7070705.0                               0                        0   \n",
       "7649846.0                               0                        0   \n",
       "...                                   ...                      ...   \n",
       "38549920.0                              0                        0   \n",
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       "39062074.0                              0                        0   \n",
       "39251197.0                              0                        0   \n",
       "39251559.0                              0                        0   \n",
       "\n",
       "            neighbourhood_Woodlawn  neighbourhood_Woodside  \n",
       "id                                                          \n",
       "1433326.0                        0                       0  \n",
       "4642269.0                        0                       0  \n",
       "5260247.0                        0                       0  \n",
       "7070705.0                        0                       0  \n",
       "7649846.0                        0                       0  \n",
       "...                            ...                     ...  \n",
       "38549920.0                       0                       0  \n",
       "38752368.0                       0                       0  \n",
       "39062074.0                       0                       0  \n",
       "39251197.0                       0                       0  \n",
       "39251559.0                       0                       0  \n",
       "\n",
       "[3168 rows x 229 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[df_final.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.apply(lambda x: x.fillna(x.mean()), axis=0)\n",
    "\n",
    "\n",
    "#data_clean.fillna(data_clean.mean(), inplace=True) #Replace nan's with mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('Faizan_scratch/uncontrollable_cleaned_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idpop_local_sum</th>\n",
       "      <th>idpop_citywide_sum</th>\n",
       "      <th>idpop_local_food_sum</th>\n",
       "      <th>idpop_citywide_food_sum</th>\n",
       "      <th>idpop_local_nightlife_sum</th>\n",
       "      <th>idpop_citywide_nightlife_sum</th>\n",
       "      <th>idpop_local_travel_sum</th>\n",
       "      <th>idpop_citywide_travel_sum</th>\n",
       "      <th>venue_count_500m</th>\n",
       "      <th>sub_cnt_20m</th>\n",
       "      <th>sub__line_cnt_20m</th>\n",
       "      <th>comp</th>\n",
       "      <th>comp_hotel</th>\n",
       "      <th>Demo % - ACS 2011 Means of transportation: Public transportation - Subway - Zipcode</th>\n",
       "      <th>Demo % - Age - Age 00 - 14 - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Age - Age 15 - 29 - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Age - Age 30 - 44 - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Age - Age 45 - 59 - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Age - Age 60 + - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Education - Pop 25 yrs + Bachelors Degree - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Education - Pop 25 yrs + Masters degree - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Ethnicity - Non-White Population - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Households - Household Size: 1-2 Persons - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Households - Household Size: 3+ Persons - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Households - Household Size: 7 + Persons - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Income - Hhld Income $100,000 + % - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Population - Female Population - Zipcode - ACS 2011</th>\n",
       "      <th>Demo % - Population - Male Population - Zipcode - ACS 2011</th>\n",
       "      <th>Demo - Age - Median Age Total Population - Zipcode - ACS 2011</th>\n",
       "      <th>Demo - Households - Median Household Income - Zipcode - ACS 2011</th>\n",
       "      <th>Demo - Population Density - Total - Zipcode - ACS 2011</th>\n",
       "      <th>Label</th>\n",
       "      <th>neighbourhood_Allerton</th>\n",
       "      <th>neighbourhood_Alphabet City</th>\n",
       "      <th>neighbourhood_Annadale</th>\n",
       "      <th>neighbourhood_Arrochar</th>\n",
       "      <th>neighbourhood_Astoria</th>\n",
       "      <th>neighbourhood_Bath Beach</th>\n",
       "      <th>neighbourhood_Battery Park City</th>\n",
       "      <th>neighbourhood_Bay Ridge</th>\n",
       "      <th>neighbourhood_Bay Terrace</th>\n",
       "      <th>neighbourhood_Baychester</th>\n",
       "      <th>neighbourhood_Bayside</th>\n",
       "      <th>neighbourhood_Bedford Park</th>\n",
       "      <th>neighbourhood_Bedford-Stuyvesant</th>\n",
       "      <th>neighbourhood_Belmont</th>\n",
       "      <th>neighbourhood_Bensonhurst</th>\n",
       "      <th>neighbourhood_Bergen Beach</th>\n",
       "      <th>neighbourhood_Boerum Hill</th>\n",
       "      <th>neighbourhood_Borough Park</th>\n",
       "      <th>neighbourhood_Brighton Beach</th>\n",
       "      <th>neighbourhood_Bronxdale</th>\n",
       "      <th>neighbourhood_Brooklyn</th>\n",
       "      <th>neighbourhood_Brooklyn Heights</th>\n",
       "      <th>neighbourhood_Brooklyn Navy Yard</th>\n",
       "      <th>neighbourhood_Brownsville</th>\n",
       "      <th>neighbourhood_Bushwick</th>\n",
       "      <th>neighbourhood_Canarsie</th>\n",
       "      <th>neighbourhood_Carroll Gardens</th>\n",
       "      <th>neighbourhood_Castle Hill</th>\n",
       "      <th>neighbourhood_Castleton Corners</th>\n",
       "      <th>neighbourhood_Chelsea</th>\n",
       "      <th>neighbourhood_Chinatown</th>\n",
       "      <th>neighbourhood_City Island</th>\n",
       "      <th>neighbourhood_Civic Center</th>\n",
       "      <th>neighbourhood_Claremont</th>\n",
       "      <th>neighbourhood_Clifton</th>\n",
       "      <th>neighbourhood_Clinton Hill</th>\n",
       "      <th>neighbourhood_Cobble Hill</th>\n",
       "      <th>neighbourhood_College Point</th>\n",
       "      <th>neighbourhood_Columbia Street Waterfront</th>\n",
       "      <th>neighbourhood_Concord</th>\n",
       "      <th>neighbourhood_Concourse</th>\n",
       "      <th>neighbourhood_Concourse Village</th>\n",
       "      <th>neighbourhood_Coney Island</th>\n",
       "      <th>neighbourhood_Corona</th>\n",
       "      <th>neighbourhood_Country Club</th>\n",
       "      <th>neighbourhood_Crotona</th>\n",
       "      <th>neighbourhood_Crown Heights</th>\n",
       "      <th>neighbourhood_DUMBO</th>\n",
       "      <th>neighbourhood_Ditmars / Steinway</th>\n",
       "      <th>neighbourhood_Dongan Hills</th>\n",
       "      <th>neighbourhood_Downtown Brooklyn</th>\n",
       "      <th>neighbourhood_Dyker Heights</th>\n",
       "      <th>neighbourhood_East Elmhurst</th>\n",
       "      <th>neighbourhood_East Flatbush</th>\n",
       "      <th>neighbourhood_East Harlem</th>\n",
       "      <th>neighbourhood_East New York</th>\n",
       "      <th>neighbourhood_East Village</th>\n",
       "      <th>neighbourhood_Eastchester</th>\n",
       "      <th>neighbourhood_Edenwald</th>\n",
       "      <th>neighbourhood_Elm Park</th>\n",
       "      <th>neighbourhood_Elmhurst</th>\n",
       "      <th>neighbourhood_Eltingville</th>\n",
       "      <th>neighbourhood_Financial District</th>\n",
       "      <th>neighbourhood_Flatbush</th>\n",
       "      <th>neighbourhood_Flatiron District</th>\n",
       "      <th>neighbourhood_Flatlands</th>\n",
       "      <th>neighbourhood_Flushing</th>\n",
       "      <th>neighbourhood_Fordham</th>\n",
       "      <th>neighbourhood_Forest Hills</th>\n",
       "      <th>neighbourhood_Fort Greene</th>\n",
       "      <th>neighbourhood_Fort Hamilton</th>\n",
       "      <th>neighbourhood_Fort Wadsworth</th>\n",
       "      <th>neighbourhood_Fresh Meadows</th>\n",
       "      <th>neighbourhood_Gerritsen Beach</th>\n",
       "      <th>neighbourhood_Glendale</th>\n",
       "      <th>neighbourhood_Gowanus</th>\n",
       "      <th>neighbourhood_Gramercy Park</th>\n",
       "      <th>neighbourhood_Graniteville</th>\n",
       "      <th>neighbourhood_Grant City</th>\n",
       "      <th>neighbourhood_Grasmere</th>\n",
       "      <th>neighbourhood_Gravesend</th>\n",
       "      <th>neighbourhood_Great Kills</th>\n",
       "      <th>neighbourhood_Greenpoint</th>\n",
       "      <th>neighbourhood_Greenridge</th>\n",
       "      <th>neighbourhood_Greenwich Village</th>\n",
       "      <th>neighbourhood_Greenwood Heights</th>\n",
       "      <th>neighbourhood_Grymes Hill</th>\n",
       "      <th>neighbourhood_Hamilton Heights</th>\n",
       "      <th>neighbourhood_Harlem</th>\n",
       "      <th>neighbourhood_Hell's Kitchen</th>\n",
       "      <th>neighbourhood_Highbridge</th>\n",
       "      <th>neighbourhood_Hillcrest</th>\n",
       "      <th>neighbourhood_Howard Beach</th>\n",
       "      <th>neighbourhood_Hudson Square</th>\n",
       "      <th>neighbourhood_Huguenot</th>\n",
       "      <th>neighbourhood_Hunts Point</th>\n",
       "      <th>neighbourhood_Inwood</th>\n",
       "      <th>neighbourhood_Jackson Heights</th>\n",
       "      <th>neighbourhood_Jamaica</th>\n",
       "      <th>neighbourhood_Kensington</th>\n",
       "      <th>neighbourhood_Kew Garden Hills</th>\n",
       "      <th>neighbourhood_Kingsbridge</th>\n",
       "      <th>neighbourhood_Kingsbridge Heights</th>\n",
       "      <th>neighbourhood_Kips Bay</th>\n",
       "      <th>neighbourhood_Lefferts Garden</th>\n",
       "      <th>neighbourhood_Lighthouse HIll</th>\n",
       "      <th>neighbourhood_Lindenwood</th>\n",
       "      <th>neighbourhood_Little Italy</th>\n",
       "      <th>neighbourhood_Long Island City</th>\n",
       "      <th>neighbourhood_Longwood</th>\n",
       "      <th>neighbourhood_Lower East Side</th>\n",
       "      <th>neighbourhood_Manhattan</th>\n",
       "      <th>neighbourhood_Manhattan Beach</th>\n",
       "      <th>neighbourhood_Marble Hill</th>\n",
       "      <th>neighbourhood_Mariners Harbor</th>\n",
       "      <th>neighbourhood_Maspeth</th>\n",
       "      <th>neighbourhood_Meatpacking District</th>\n",
       "      <th>neighbourhood_Melrose</th>\n",
       "      <th>neighbourhood_Middle Village</th>\n",
       "      <th>neighbourhood_Midland Beach</th>\n",
       "      <th>neighbourhood_Midtown</th>\n",
       "      <th>neighbourhood_Midtown East</th>\n",
       "      <th>neighbourhood_Midwood</th>\n",
       "      <th>neighbourhood_Mill Basin</th>\n",
       "      <th>neighbourhood_Morningside Heights</th>\n",
       "      <th>neighbourhood_Morris Heights</th>\n",
       "      <th>neighbourhood_Morris Park</th>\n",
       "      <th>neighbourhood_Morrisania</th>\n",
       "      <th>neighbourhood_Mott Haven</th>\n",
       "      <th>neighbourhood_Mount Eden</th>\n",
       "      <th>neighbourhood_Murray Hill</th>\n",
       "      <th>neighbourhood_New Brighton</th>\n",
       "      <th>neighbourhood_New Dorp</th>\n",
       "      <th>neighbourhood_New Dorp Beach</th>\n",
       "      <th>neighbourhood_New Springville</th>\n",
       "      <th>neighbourhood_Noho</th>\n",
       "      <th>neighbourhood_Nolita</th>\n",
       "      <th>neighbourhood_Norwood</th>\n",
       "      <th>neighbourhood_Ozone Park</th>\n",
       "      <th>neighbourhood_Park Slope</th>\n",
       "      <th>neighbourhood_Park Versailles</th>\n",
       "      <th>neighbourhood_Parkchester</th>\n",
       "      <th>neighbourhood_Pelham Bay</th>\n",
       "      <th>neighbourhood_Pleasant Plains</th>\n",
       "      <th>neighbourhood_Port Morris</th>\n",
       "      <th>neighbourhood_Port Richmond</th>\n",
       "      <th>neighbourhood_Prospect Heights</th>\n",
       "      <th>neighbourhood_Queens</th>\n",
       "      <th>neighbourhood_Randall Manor</th>\n",
       "      <th>neighbourhood_Red Hook</th>\n",
       "      <th>neighbourhood_Rego Park</th>\n",
       "      <th>neighbourhood_Richmond Hill</th>\n",
       "      <th>neighbourhood_Ridgewood</th>\n",
       "      <th>neighbourhood_Riverdale</th>\n",
       "      <th>neighbourhood_Roosevelt Island</th>\n",
       "      <th>neighbourhood_Rosebank</th>\n",
       "      <th>neighbourhood_Rossville</th>\n",
       "      <th>neighbourhood_Sea Gate</th>\n",
       "      <th>neighbourhood_Sheepshead Bay</th>\n",
       "      <th>neighbourhood_Soho</th>\n",
       "      <th>neighbourhood_Soundview</th>\n",
       "      <th>neighbourhood_South Beach</th>\n",
       "      <th>neighbourhood_South Ozone Park</th>\n",
       "      <th>neighbourhood_South Street Seaport</th>\n",
       "      <th>neighbourhood_Spuyten Duyvil</th>\n",
       "      <th>neighbourhood_St. George</th>\n",
       "      <th>neighbourhood_Stapleton</th>\n",
       "      <th>neighbourhood_Staten Island</th>\n",
       "      <th>neighbourhood_Sunnyside</th>\n",
       "      <th>neighbourhood_Sunset Park</th>\n",
       "      <th>neighbourhood_The Bronx</th>\n",
       "      <th>neighbourhood_The Rockaways</th>\n",
       "      <th>neighbourhood_Throgs Neck</th>\n",
       "      <th>neighbourhood_Times Square/Theatre District</th>\n",
       "      <th>neighbourhood_Tompkinsville</th>\n",
       "      <th>neighbourhood_Tottenville</th>\n",
       "      <th>neighbourhood_Tremont</th>\n",
       "      <th>neighbourhood_Tribeca</th>\n",
       "      <th>neighbourhood_Union Square</th>\n",
       "      <th>neighbourhood_University Heights</th>\n",
       "      <th>neighbourhood_Upper East Side</th>\n",
       "      <th>neighbourhood_Upper West Side</th>\n",
       "      <th>neighbourhood_Utopia</th>\n",
       "      <th>neighbourhood_Van Nest</th>\n",
       "      <th>neighbourhood_Wakefield</th>\n",
       "      <th>neighbourhood_Washington Heights</th>\n",
       "      <th>neighbourhood_West Brighton</th>\n",
       "      <th>neighbourhood_West Village</th>\n",
       "      <th>neighbourhood_Westchester Village</th>\n",
       "      <th>neighbourhood_Westerleigh</th>\n",
       "      <th>neighbourhood_Whitestone</th>\n",
       "      <th>neighbourhood_Williamsbridge</th>\n",
       "      <th>neighbourhood_Williamsburg</th>\n",
       "      <th>neighbourhood_Windsor Terrace</th>\n",
       "      <th>neighbourhood_Woodhaven</th>\n",
       "      <th>neighbourhood_Woodlawn</th>\n",
       "      <th>neighbourhood_Woodside</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>40135.000000</td>\n",
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       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
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       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
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       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
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       "      <td>40135.000000</td>\n",
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       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
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       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "      <td>40135.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.759485</td>\n",
       "      <td>0.952377</td>\n",
       "      <td>4.266722</td>\n",
       "      <td>1.111177</td>\n",
       "      <td>4.139267</td>\n",
       "      <td>1.104386</td>\n",
       "      <td>3.521197</td>\n",
       "      <td>1.008803</td>\n",
       "      <td>13.460396</td>\n",
       "      <td>1.940227</td>\n",
       "      <td>4.180491</td>\n",
       "      <td>4029.413778</td>\n",
       "      <td>325.146032</td>\n",
       "      <td>52.207324</td>\n",
       "      <td>15.437323</td>\n",
       "      <td>25.945890</td>\n",
       "      <td>24.537477</td>\n",
       "      <td>18.116110</td>\n",
       "      <td>15.963199</td>\n",
       "      <td>24.465075</td>\n",
       "      <td>12.128757</td>\n",
       "      <td>47.885977</td>\n",
       "      <td>67.557388</td>\n",
       "      <td>32.442612</td>\n",
       "      <td>1.733876</td>\n",
       "      <td>25.972948</td>\n",
       "      <td>52.345108</td>\n",
       "      <td>47.654892</td>\n",
       "      <td>35.230499</td>\n",
       "      <td>61775.020161</td>\n",
       "      <td>55715.428405</td>\n",
       "      <td>0.481350</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>0.006179</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.015548</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>0.002168</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000872</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.041834</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000972</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.001869</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.196761</td>\n",
       "      <td>0.001146</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.020880</td>\n",
       "      <td>0.002242</td>\n",
       "      <td>0.001744</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.012134</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.007275</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.001545</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.014003</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000947</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.001221</td>\n",
       "      <td>0.004211</td>\n",
       "      <td>0.010888</td>\n",
       "      <td>0.005058</td>\n",
       "      <td>0.016170</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.006902</td>\n",
       "      <td>0.011013</td>\n",
       "      <td>0.003015</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.005033</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.001545</td>\n",
       "      <td>0.004061</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.002068</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.008397</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.003314</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.007251</td>\n",
       "      <td>0.035505</td>\n",
       "      <td>0.017142</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.001694</td>\n",
       "      <td>0.003264</td>\n",
       "      <td>0.008671</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.004709</td>\n",
       "      <td>0.003164</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.002193</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.014003</td>\n",
       "      <td>0.190756</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.027084</td>\n",
       "      <td>0.006976</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.001395</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000922</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>0.006952</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.003015</td>\n",
       "      <td>0.061019</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>0.001022</td>\n",
       "      <td>0.001943</td>\n",
       "      <td>0.003712</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.002741</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>0.003688</td>\n",
       "      <td>0.003588</td>\n",
       "      <td>0.011686</td>\n",
       "      <td>0.005905</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.017840</td>\n",
       "      <td>0.017018</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.008571</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.007300</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.037075</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.001221</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.001719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.730527</td>\n",
       "      <td>0.537825</td>\n",
       "      <td>5.536643</td>\n",
       "      <td>0.953865</td>\n",
       "      <td>4.384485</td>\n",
       "      <td>0.917551</td>\n",
       "      <td>3.370224</td>\n",
       "      <td>0.794285</td>\n",
       "      <td>15.061070</td>\n",
       "      <td>1.958237</td>\n",
       "      <td>5.079378</td>\n",
       "      <td>2214.256730</td>\n",
       "      <td>466.708887</td>\n",
       "      <td>12.143882</td>\n",
       "      <td>5.595078</td>\n",
       "      <td>4.707478</td>\n",
       "      <td>3.719419</td>\n",
       "      <td>2.529350</td>\n",
       "      <td>4.054775</td>\n",
       "      <td>10.271722</td>\n",
       "      <td>6.927535</td>\n",
       "      <td>25.553324</td>\n",
       "      <td>13.362335</td>\n",
       "      <td>13.362335</td>\n",
       "      <td>1.737506</td>\n",
       "      <td>14.427413</td>\n",
       "      <td>2.650844</td>\n",
       "      <td>2.650844</td>\n",
       "      <td>3.864223</td>\n",
       "      <td>26114.113080</td>\n",
       "      <td>22581.860410</td>\n",
       "      <td>0.499658</td>\n",
       "      <td>0.017995</td>\n",
       "      <td>0.078365</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.123718</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>0.017995</td>\n",
       "      <td>0.046508</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.029518</td>\n",
       "      <td>0.026871</td>\n",
       "      <td>0.019963</td>\n",
       "      <td>0.200212</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.031158</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.043189</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>0.023932</td>\n",
       "      <td>0.014117</td>\n",
       "      <td>0.397555</td>\n",
       "      <td>0.033836</td>\n",
       "      <td>0.023406</td>\n",
       "      <td>0.021173</td>\n",
       "      <td>0.142983</td>\n",
       "      <td>0.047302</td>\n",
       "      <td>0.041727</td>\n",
       "      <td>0.012226</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.109486</td>\n",
       "      <td>0.065513</td>\n",
       "      <td>0.019329</td>\n",
       "      <td>0.012226</td>\n",
       "      <td>0.012226</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.084986</td>\n",
       "      <td>0.021753</td>\n",
       "      <td>0.019329</td>\n",
       "      <td>0.015783</td>\n",
       "      <td>0.011161</td>\n",
       "      <td>0.026404</td>\n",
       "      <td>0.019963</td>\n",
       "      <td>0.014117</td>\n",
       "      <td>0.039274</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>0.117503</td>\n",
       "      <td>0.023406</td>\n",
       "      <td>0.043475</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.030756</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>0.034920</td>\n",
       "      <td>0.064755</td>\n",
       "      <td>0.103778</td>\n",
       "      <td>0.070940</td>\n",
       "      <td>0.126132</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>0.046241</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.082790</td>\n",
       "      <td>0.104364</td>\n",
       "      <td>0.054825</td>\n",
       "      <td>0.026404</td>\n",
       "      <td>0.070766</td>\n",
       "      <td>0.023932</td>\n",
       "      <td>0.039274</td>\n",
       "      <td>0.063600</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.015783</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.023932</td>\n",
       "      <td>0.045429</td>\n",
       "      <td>0.047823</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>0.025444</td>\n",
       "      <td>0.011161</td>\n",
       "      <td>0.091249</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.057471</td>\n",
       "      <td>0.046241</td>\n",
       "      <td>0.016553</td>\n",
       "      <td>0.084842</td>\n",
       "      <td>0.185055</td>\n",
       "      <td>0.129803</td>\n",
       "      <td>0.021173</td>\n",
       "      <td>0.016553</td>\n",
       "      <td>0.011161</td>\n",
       "      <td>0.016553</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>0.041127</td>\n",
       "      <td>0.057039</td>\n",
       "      <td>0.092713</td>\n",
       "      <td>0.035972</td>\n",
       "      <td>0.019329</td>\n",
       "      <td>0.012226</td>\n",
       "      <td>0.019329</td>\n",
       "      <td>0.068462</td>\n",
       "      <td>0.056164</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.011161</td>\n",
       "      <td>0.015783</td>\n",
       "      <td>0.046774</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.117503</td>\n",
       "      <td>0.392902</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.036656</td>\n",
       "      <td>0.017289</td>\n",
       "      <td>0.012226</td>\n",
       "      <td>0.021173</td>\n",
       "      <td>0.009983</td>\n",
       "      <td>0.162329</td>\n",
       "      <td>0.083234</td>\n",
       "      <td>0.035625</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.037328</td>\n",
       "      <td>0.019329</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>0.017289</td>\n",
       "      <td>0.030349</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.048849</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.020577</td>\n",
       "      <td>0.040211</td>\n",
       "      <td>0.014117</td>\n",
       "      <td>0.025444</td>\n",
       "      <td>0.083087</td>\n",
       "      <td>0.012226</td>\n",
       "      <td>0.018674</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.027330</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>0.054825</td>\n",
       "      <td>0.239368</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.026871</td>\n",
       "      <td>0.031946</td>\n",
       "      <td>0.044042</td>\n",
       "      <td>0.060818</td>\n",
       "      <td>0.017289</td>\n",
       "      <td>0.035625</td>\n",
       "      <td>0.009983</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.040211</td>\n",
       "      <td>0.052281</td>\n",
       "      <td>0.016553</td>\n",
       "      <td>0.013206</td>\n",
       "      <td>0.020577</td>\n",
       "      <td>0.009983</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.024950</td>\n",
       "      <td>0.016553</td>\n",
       "      <td>0.066076</td>\n",
       "      <td>0.060614</td>\n",
       "      <td>0.059792</td>\n",
       "      <td>0.107468</td>\n",
       "      <td>0.076618</td>\n",
       "      <td>0.017289</td>\n",
       "      <td>0.033466</td>\n",
       "      <td>0.017289</td>\n",
       "      <td>0.008645</td>\n",
       "      <td>0.023932</td>\n",
       "      <td>0.035625</td>\n",
       "      <td>0.019963</td>\n",
       "      <td>0.014973</td>\n",
       "      <td>0.132371</td>\n",
       "      <td>0.129338</td>\n",
       "      <td>0.015783</td>\n",
       "      <td>0.011161</td>\n",
       "      <td>0.023932</td>\n",
       "      <td>0.092184</td>\n",
       "      <td>0.019963</td>\n",
       "      <td>0.085131</td>\n",
       "      <td>0.011161</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.012226</td>\n",
       "      <td>0.018674</td>\n",
       "      <td>0.188948</td>\n",
       "      <td>0.036656</td>\n",
       "      <td>0.034920</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.041428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.131065</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.136615</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.129417</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.129629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600359</td>\n",
       "      <td>3.826904</td>\n",
       "      <td>8.257411</td>\n",
       "      <td>14.844480</td>\n",
       "      <td>8.905327</td>\n",
       "      <td>4.209481</td>\n",
       "      <td>6.078333</td>\n",
       "      <td>1.708955</td>\n",
       "      <td>4.333679</td>\n",
       "      <td>26.772641</td>\n",
       "      <td>6.049279</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.551923</td>\n",
       "      <td>37.511039</td>\n",
       "      <td>41.171675</td>\n",
       "      <td>27.360211</td>\n",
       "      <td>21605.552109</td>\n",
       "      <td>332.767700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.004188</td>\n",
       "      <td>0.515908</td>\n",
       "      <td>0.004236</td>\n",
       "      <td>0.521160</td>\n",
       "      <td>0.003815</td>\n",
       "      <td>0.492367</td>\n",
       "      <td>0.004295</td>\n",
       "      <td>0.481122</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2313.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>47.585114</td>\n",
       "      <td>12.142244</td>\n",
       "      <td>23.153624</td>\n",
       "      <td>21.786779</td>\n",
       "      <td>17.252987</td>\n",
       "      <td>13.746737</td>\n",
       "      <td>15.041992</td>\n",
       "      <td>6.779428</td>\n",
       "      <td>22.708971</td>\n",
       "      <td>57.502817</td>\n",
       "      <td>22.989461</td>\n",
       "      <td>0.253230</td>\n",
       "      <td>13.838064</td>\n",
       "      <td>51.298969</td>\n",
       "      <td>45.825098</td>\n",
       "      <td>32.854203</td>\n",
       "      <td>40255.916832</td>\n",
       "      <td>38064.582400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.704429</td>\n",
       "      <td>0.780023</td>\n",
       "      <td>3.883138</td>\n",
       "      <td>0.758586</td>\n",
       "      <td>3.819075</td>\n",
       "      <td>0.763951</td>\n",
       "      <td>3.563196</td>\n",
       "      <td>0.740996</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4132.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>54.570794</td>\n",
       "      <td>15.437323</td>\n",
       "      <td>25.864389</td>\n",
       "      <td>23.639531</td>\n",
       "      <td>18.116110</td>\n",
       "      <td>15.547050</td>\n",
       "      <td>24.465075</td>\n",
       "      <td>10.349727</td>\n",
       "      <td>47.885977</td>\n",
       "      <td>67.557388</td>\n",
       "      <td>32.442612</td>\n",
       "      <td>1.491215</td>\n",
       "      <td>22.239929</td>\n",
       "      <td>52.530694</td>\n",
       "      <td>47.469306</td>\n",
       "      <td>35.111377</td>\n",
       "      <td>56519.419504</td>\n",
       "      <td>55715.428405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.402250</td>\n",
       "      <td>1.395617</td>\n",
       "      <td>6.028989</td>\n",
       "      <td>1.397294</td>\n",
       "      <td>6.083634</td>\n",
       "      <td>1.647880</td>\n",
       "      <td>5.015743</td>\n",
       "      <td>1.343083</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5851.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>59.171680</td>\n",
       "      <td>19.753417</td>\n",
       "      <td>29.103550</td>\n",
       "      <td>26.572190</td>\n",
       "      <td>19.690835</td>\n",
       "      <td>17.855065</td>\n",
       "      <td>33.344511</td>\n",
       "      <td>18.950489</td>\n",
       "      <td>71.028636</td>\n",
       "      <td>77.010539</td>\n",
       "      <td>42.497183</td>\n",
       "      <td>2.476485</td>\n",
       "      <td>36.918338</td>\n",
       "      <td>54.174902</td>\n",
       "      <td>48.701031</td>\n",
       "      <td>38.035393</td>\n",
       "      <td>79242.056856</td>\n",
       "      <td>77508.924700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>159.984962</td>\n",
       "      <td>7.874911</td>\n",
       "      <td>603.299985</td>\n",
       "      <td>23.935382</td>\n",
       "      <td>122.773632</td>\n",
       "      <td>31.432507</td>\n",
       "      <td>82.806557</td>\n",
       "      <td>22.579330</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>8368.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>78.044872</td>\n",
       "      <td>28.803587</td>\n",
       "      <td>44.995665</td>\n",
       "      <td>43.743380</td>\n",
       "      <td>27.827212</td>\n",
       "      <td>32.505210</td>\n",
       "      <td>48.579083</td>\n",
       "      <td>28.136134</td>\n",
       "      <td>96.076897</td>\n",
       "      <td>93.950721</td>\n",
       "      <td>73.227359</td>\n",
       "      <td>9.760688</td>\n",
       "      <td>75.295377</td>\n",
       "      <td>58.828325</td>\n",
       "      <td>62.488961</td>\n",
       "      <td>48.282991</td>\n",
       "      <td>198430.411256</td>\n",
       "      <td>102129.524400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       idpop_local_sum  idpop_citywide_sum  idpop_local_food_sum  \\\n",
       "count     40135.000000        40135.000000          40135.000000   \n",
       "mean          3.759485            0.952377              4.266722   \n",
       "std           3.730527            0.537825              5.536643   \n",
       "min           0.000281            0.131065              0.000281   \n",
       "25%           0.004188            0.515908              0.004236   \n",
       "50%           3.704429            0.780023              3.883138   \n",
       "75%           5.402250            1.395617              6.028989   \n",
       "max         159.984962            7.874911            603.299985   \n",
       "\n",
       "       idpop_citywide_food_sum  idpop_local_nightlife_sum  \\\n",
       "count             40135.000000               40135.000000   \n",
       "mean                  1.111177                   4.139267   \n",
       "std                   0.953865                   4.384485   \n",
       "min                   0.136615                   0.000162   \n",
       "25%                   0.521160                   0.003815   \n",
       "50%                   0.758586                   3.819075   \n",
       "75%                   1.397294                   6.083634   \n",
       "max                  23.935382                 122.773632   \n",
       "\n",
       "       idpop_citywide_nightlife_sum  idpop_local_travel_sum  \\\n",
       "count                  40135.000000            40135.000000   \n",
       "mean                       1.104386                3.521197   \n",
       "std                        0.917551                3.370224   \n",
       "min                        0.129417                0.000177   \n",
       "25%                        0.492367                0.004295   \n",
       "50%                        0.763951                3.563196   \n",
       "75%                        1.647880                5.015743   \n",
       "max                       31.432507               82.806557   \n",
       "\n",
       "       idpop_citywide_travel_sum  venue_count_500m   sub_cnt_20m  \\\n",
       "count               40135.000000      40135.000000  40135.000000   \n",
       "mean                    1.008803         13.460396      1.940227   \n",
       "std                     0.794285         15.061070      1.958237   \n",
       "min                     0.129629          0.000000      0.000000   \n",
       "25%                     0.481122          3.000000      1.000000   \n",
       "50%                     0.740996          8.000000      1.000000   \n",
       "75%                     1.343083         17.000000      3.000000   \n",
       "max                    22.579330         91.000000     14.000000   \n",
       "\n",
       "       sub__line_cnt_20m          comp    comp_hotel  \\\n",
       "count       40135.000000  40135.000000  40135.000000   \n",
       "mean            4.180491   4029.413778    325.146032   \n",
       "std             5.079378   2214.256730    466.708887   \n",
       "min             0.000000      3.000000      0.000000   \n",
       "25%             1.000000   2313.000000     27.000000   \n",
       "50%             3.000000   4132.000000     47.000000   \n",
       "75%             5.000000   5851.000000    582.000000   \n",
       "max            32.000000   8368.000000   1567.000000   \n",
       "\n",
       "       Demo % - ACS 2011 Means of transportation: Public transportation - Subway - Zipcode  \\\n",
       "count                                       40135.000000                                     \n",
       "mean                                           52.207324                                     \n",
       "std                                            12.143882                                     \n",
       "min                                             1.600359                                     \n",
       "25%                                            47.585114                                     \n",
       "50%                                            54.570794                                     \n",
       "75%                                            59.171680                                     \n",
       "max                                            78.044872                                     \n",
       "\n",
       "       Demo % - Age - Age 00 - 14 - Zipcode - ACS 2011  \\\n",
       "count                                     40135.000000   \n",
       "mean                                         15.437323   \n",
       "std                                           5.595078   \n",
       "min                                           3.826904   \n",
       "25%                                          12.142244   \n",
       "50%                                          15.437323   \n",
       "75%                                          19.753417   \n",
       "max                                          28.803587   \n",
       "\n",
       "       Demo % - Age - Age 15 - 29 - Zipcode - ACS 2011  \\\n",
       "count                                     40135.000000   \n",
       "mean                                         25.945890   \n",
       "std                                           4.707478   \n",
       "min                                           8.257411   \n",
       "25%                                          23.153624   \n",
       "50%                                          25.864389   \n",
       "75%                                          29.103550   \n",
       "max                                          44.995665   \n",
       "\n",
       "       Demo % - Age - Age 30 - 44 - Zipcode - ACS 2011  \\\n",
       "count                                     40135.000000   \n",
       "mean                                         24.537477   \n",
       "std                                           3.719419   \n",
       "min                                          14.844480   \n",
       "25%                                          21.786779   \n",
       "50%                                          23.639531   \n",
       "75%                                          26.572190   \n",
       "max                                          43.743380   \n",
       "\n",
       "       Demo % - Age - Age 45 - 59 - Zipcode - ACS 2011  \\\n",
       "count                                     40135.000000   \n",
       "mean                                         18.116110   \n",
       "std                                           2.529350   \n",
       "min                                           8.905327   \n",
       "25%                                          17.252987   \n",
       "50%                                          18.116110   \n",
       "75%                                          19.690835   \n",
       "max                                          27.827212   \n",
       "\n",
       "       Demo % - Age - Age 60 + - Zipcode - ACS 2011  \\\n",
       "count                                  40135.000000   \n",
       "mean                                      15.963199   \n",
       "std                                        4.054775   \n",
       "min                                        4.209481   \n",
       "25%                                       13.746737   \n",
       "50%                                       15.547050   \n",
       "75%                                       17.855065   \n",
       "max                                       32.505210   \n",
       "\n",
       "       Demo % - Education - Pop 25 yrs + Bachelors Degree - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000                         \n",
       "mean                                           24.465075                         \n",
       "std                                            10.271722                         \n",
       "min                                             6.078333                         \n",
       "25%                                            15.041992                         \n",
       "50%                                            24.465075                         \n",
       "75%                                            33.344511                         \n",
       "max                                            48.579083                         \n",
       "\n",
       "       Demo % - Education - Pop 25 yrs + Masters degree - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000                       \n",
       "mean                                           12.128757                       \n",
       "std                                             6.927535                       \n",
       "min                                             1.708955                       \n",
       "25%                                             6.779428                       \n",
       "50%                                            10.349727                       \n",
       "75%                                            18.950489                       \n",
       "max                                            28.136134                       \n",
       "\n",
       "       Demo % - Ethnicity - Non-White Population - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000                \n",
       "mean                                           47.885977                \n",
       "std                                            25.553324                \n",
       "min                                             4.333679                \n",
       "25%                                            22.708971                \n",
       "50%                                            47.885977                \n",
       "75%                                            71.028636                \n",
       "max                                            96.076897                \n",
       "\n",
       "       Demo % - Households - Household Size: 1-2 Persons - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000                        \n",
       "mean                                           67.557388                        \n",
       "std                                            13.362335                        \n",
       "min                                            26.772641                        \n",
       "25%                                            57.502817                        \n",
       "50%                                            67.557388                        \n",
       "75%                                            77.010539                        \n",
       "max                                            93.950721                        \n",
       "\n",
       "       Demo % - Households - Household Size: 3+ Persons - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000                       \n",
       "mean                                           32.442612                       \n",
       "std                                            13.362335                       \n",
       "min                                             6.049279                       \n",
       "25%                                            22.989461                       \n",
       "50%                                            32.442612                       \n",
       "75%                                            42.497183                       \n",
       "max                                            73.227359                       \n",
       "\n",
       "       Demo % - Households - Household Size: 7 + Persons - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000                        \n",
       "mean                                            1.733876                        \n",
       "std                                             1.737506                        \n",
       "min                                             0.000000                        \n",
       "25%                                             0.253230                        \n",
       "50%                                             1.491215                        \n",
       "75%                                             2.476485                        \n",
       "max                                             9.760688                        \n",
       "\n",
       "       Demo % - Income - Hhld Income $100,000 + % - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000                 \n",
       "mean                                           25.972948                 \n",
       "std                                            14.427413                 \n",
       "min                                             1.551923                 \n",
       "25%                                            13.838064                 \n",
       "50%                                            22.239929                 \n",
       "75%                                            36.918338                 \n",
       "max                                            75.295377                 \n",
       "\n",
       "       Demo % - Population - Female Population - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000              \n",
       "mean                                           52.345108              \n",
       "std                                             2.650844              \n",
       "min                                            37.511039              \n",
       "25%                                            51.298969              \n",
       "50%                                            52.530694              \n",
       "75%                                            54.174902              \n",
       "max                                            58.828325              \n",
       "\n",
       "       Demo % - Population - Male Population - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000            \n",
       "mean                                           47.654892            \n",
       "std                                             2.650844            \n",
       "min                                            41.171675            \n",
       "25%                                            45.825098            \n",
       "50%                                            47.469306            \n",
       "75%                                            48.701031            \n",
       "max                                            62.488961            \n",
       "\n",
       "       Demo - Age - Median Age Total Population - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000               \n",
       "mean                                           35.230499               \n",
       "std                                             3.864223               \n",
       "min                                            27.360211               \n",
       "25%                                            32.854203               \n",
       "50%                                            35.111377               \n",
       "75%                                            38.035393               \n",
       "max                                            48.282991               \n",
       "\n",
       "       Demo - Households - Median Household Income - Zipcode - ACS 2011  \\\n",
       "count                                       40135.000000                  \n",
       "mean                                        61775.020161                  \n",
       "std                                         26114.113080                  \n",
       "min                                         21605.552109                  \n",
       "25%                                         40255.916832                  \n",
       "50%                                         56519.419504                  \n",
       "75%                                         79242.056856                  \n",
       "max                                        198430.411256                  \n",
       "\n",
       "       Demo - Population Density - Total - Zipcode - ACS 2011         Label  \\\n",
       "count                                       40135.000000       40135.000000   \n",
       "mean                                        55715.428405           0.481350   \n",
       "std                                         22581.860410           0.499658   \n",
       "min                                           332.767700           0.000000   \n",
       "25%                                         38064.582400           0.000000   \n",
       "50%                                         55715.428405           0.000000   \n",
       "75%                                         77508.924700           1.000000   \n",
       "max                                        102129.524400           1.000000   \n",
       "\n",
       "       neighbourhood_Allerton  neighbourhood_Alphabet City  \\\n",
       "count            40135.000000                 40135.000000   \n",
       "mean                 0.000324                     0.006179   \n",
       "std                  0.017995                     0.078365   \n",
       "min                  0.000000                     0.000000   \n",
       "25%                  0.000000                     0.000000   \n",
       "50%                  0.000000                     0.000000   \n",
       "75%                  0.000000                     0.000000   \n",
       "max                  1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_Annadale  neighbourhood_Arrochar  neighbourhood_Astoria  \\\n",
       "count            40135.000000            40135.000000           40135.000000   \n",
       "mean                 0.000075                0.000025               0.015548   \n",
       "std                  0.008645                0.004992               0.123718   \n",
       "min                  0.000000                0.000000               0.000000   \n",
       "25%                  0.000000                0.000000               0.000000   \n",
       "50%                  0.000000                0.000000               0.000000   \n",
       "75%                  0.000000                0.000000               0.000000   \n",
       "max                  1.000000                1.000000               1.000000   \n",
       "\n",
       "       neighbourhood_Bath Beach  neighbourhood_Battery Park City  \\\n",
       "count              40135.000000                     40135.000000   \n",
       "mean                   0.000224                         0.000324   \n",
       "std                    0.014973                         0.017995   \n",
       "min                    0.000000                         0.000000   \n",
       "25%                    0.000000                         0.000000   \n",
       "50%                    0.000000                         0.000000   \n",
       "75%                    0.000000                         0.000000   \n",
       "max                    1.000000                         1.000000   \n",
       "\n",
       "       neighbourhood_Bay Ridge  neighbourhood_Bay Terrace  \\\n",
       "count             40135.000000               40135.000000   \n",
       "mean                  0.002168                   0.000025   \n",
       "std                   0.046508                   0.004992   \n",
       "min                   0.000000                   0.000000   \n",
       "25%                   0.000000                   0.000000   \n",
       "50%                   0.000000                   0.000000   \n",
       "75%                   0.000000                   0.000000   \n",
       "max                   1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Baychester  neighbourhood_Bayside  \\\n",
       "count              40135.000000           40135.000000   \n",
       "mean                   0.000872               0.000723   \n",
       "std                    0.029518               0.026871   \n",
       "min                    0.000000               0.000000   \n",
       "25%                    0.000000               0.000000   \n",
       "50%                    0.000000               0.000000   \n",
       "75%                    0.000000               0.000000   \n",
       "max                    1.000000               1.000000   \n",
       "\n",
       "       neighbourhood_Bedford Park  neighbourhood_Bedford-Stuyvesant  \\\n",
       "count                40135.000000                      40135.000000   \n",
       "mean                     0.000399                          0.041834   \n",
       "std                      0.019963                          0.200212   \n",
       "min                      0.000000                          0.000000   \n",
       "25%                      0.000000                          0.000000   \n",
       "50%                      0.000000                          0.000000   \n",
       "75%                      0.000000                          0.000000   \n",
       "max                      1.000000                          1.000000   \n",
       "\n",
       "       neighbourhood_Belmont  neighbourhood_Bensonhurst  \\\n",
       "count           40135.000000               40135.000000   \n",
       "mean                0.000075                   0.000972   \n",
       "std                 0.008645                   0.031158   \n",
       "min                 0.000000                   0.000000   \n",
       "25%                 0.000000                   0.000000   \n",
       "50%                 0.000000                   0.000000   \n",
       "75%                 0.000000                   0.000000   \n",
       "max                 1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Bergen Beach  neighbourhood_Boerum Hill  \\\n",
       "count                40135.000000               40135.000000   \n",
       "mean                     0.000075                   0.001869   \n",
       "std                      0.008645                   0.043189   \n",
       "min                      0.000000                   0.000000   \n",
       "25%                      0.000000                   0.000000   \n",
       "50%                      0.000000                   0.000000   \n",
       "75%                      0.000000                   0.000000   \n",
       "max                      1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Borough Park  neighbourhood_Brighton Beach  \\\n",
       "count                40135.000000                  40135.000000   \n",
       "mean                     0.000523                      0.000573   \n",
       "std                      0.022869                      0.023932   \n",
       "min                      0.000000                      0.000000   \n",
       "25%                      0.000000                      0.000000   \n",
       "50%                      0.000000                      0.000000   \n",
       "75%                      0.000000                      0.000000   \n",
       "max                      1.000000                      1.000000   \n",
       "\n",
       "       neighbourhood_Bronxdale  neighbourhood_Brooklyn  \\\n",
       "count             40135.000000            40135.000000   \n",
       "mean                  0.000199                0.196761   \n",
       "std                   0.014117                0.397555   \n",
       "min                   0.000000                0.000000   \n",
       "25%                   0.000000                0.000000   \n",
       "50%                   0.000000                0.000000   \n",
       "75%                   0.000000                0.000000   \n",
       "max                   1.000000                1.000000   \n",
       "\n",
       "       neighbourhood_Brooklyn Heights  neighbourhood_Brooklyn Navy Yard  \\\n",
       "count                    40135.000000                      40135.000000   \n",
       "mean                         0.001146                          0.000548   \n",
       "std                          0.033836                          0.023406   \n",
       "min                          0.000000                          0.000000   \n",
       "25%                          0.000000                          0.000000   \n",
       "50%                          0.000000                          0.000000   \n",
       "75%                          0.000000                          0.000000   \n",
       "max                          1.000000                          1.000000   \n",
       "\n",
       "       neighbourhood_Brownsville  neighbourhood_Bushwick  \\\n",
       "count               40135.000000            40135.000000   \n",
       "mean                    0.000448                0.020880   \n",
       "std                     0.021173                0.142983   \n",
       "min                     0.000000                0.000000   \n",
       "25%                     0.000000                0.000000   \n",
       "50%                     0.000000                0.000000   \n",
       "75%                     0.000000                0.000000   \n",
       "max                     1.000000                1.000000   \n",
       "\n",
       "       neighbourhood_Canarsie  neighbourhood_Carroll Gardens  \\\n",
       "count            40135.000000                   40135.000000   \n",
       "mean                 0.002242                       0.001744   \n",
       "std                  0.047302                       0.041727   \n",
       "min                  0.000000                       0.000000   \n",
       "25%                  0.000000                       0.000000   \n",
       "50%                  0.000000                       0.000000   \n",
       "75%                  0.000000                       0.000000   \n",
       "max                  1.000000                       1.000000   \n",
       "\n",
       "       neighbourhood_Castle Hill   neighbourhood_Castleton Corners  \\\n",
       "count                40135.000000                     40135.000000   \n",
       "mean                     0.000149                         0.000025   \n",
       "std                      0.012226                         0.004992   \n",
       "min                      0.000000                         0.000000   \n",
       "25%                      0.000000                         0.000000   \n",
       "50%                      0.000000                         0.000000   \n",
       "75%                      0.000000                         0.000000   \n",
       "max                      1.000000                         1.000000   \n",
       "\n",
       "       neighbourhood_Chelsea  neighbourhood_Chinatown  \\\n",
       "count           40135.000000             40135.000000   \n",
       "mean                0.012134                 0.004310   \n",
       "std                 0.109486                 0.065513   \n",
       "min                 0.000000                 0.000000   \n",
       "25%                 0.000000                 0.000000   \n",
       "50%                 0.000000                 0.000000   \n",
       "75%                 0.000000                 0.000000   \n",
       "max                 1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_City Island  neighbourhood_Civic Center  \\\n",
       "count               40135.000000                40135.000000   \n",
       "mean                    0.000374                    0.000149   \n",
       "std                     0.019329                    0.012226   \n",
       "min                     0.000000                    0.000000   \n",
       "25%                     0.000000                    0.000000   \n",
       "50%                     0.000000                    0.000000   \n",
       "75%                     0.000000                    0.000000   \n",
       "max                     1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_Claremont  neighbourhood_Clifton  \\\n",
       "count             40135.000000           40135.000000   \n",
       "mean                  0.000149               0.000050   \n",
       "std                   0.012226               0.007059   \n",
       "min                   0.000000               0.000000   \n",
       "25%                   0.000000               0.000000   \n",
       "50%                   0.000000               0.000000   \n",
       "75%                   0.000000               0.000000   \n",
       "max                   1.000000               1.000000   \n",
       "\n",
       "       neighbourhood_Clinton Hill  neighbourhood_Cobble Hill  \\\n",
       "count                40135.000000               40135.000000   \n",
       "mean                     0.007275                   0.000473   \n",
       "std                      0.084986                   0.021753   \n",
       "min                      0.000000                   0.000000   \n",
       "25%                      0.000000                   0.000000   \n",
       "50%                      0.000000                   0.000000   \n",
       "75%                      0.000000                   0.000000   \n",
       "max                      1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_College Point  neighbourhood_Columbia Street Waterfront  \\\n",
       "count                 40135.000000                              40135.000000   \n",
       "mean                      0.000374                                  0.000249   \n",
       "std                       0.019329                                  0.015783   \n",
       "min                       0.000000                                  0.000000   \n",
       "25%                       0.000000                                  0.000000   \n",
       "50%                       0.000000                                  0.000000   \n",
       "75%                       0.000000                                  0.000000   \n",
       "max                       1.000000                                  1.000000   \n",
       "\n",
       "       neighbourhood_Concord  neighbourhood_Concourse  \\\n",
       "count           40135.000000             40135.000000   \n",
       "mean                0.000125                 0.000698   \n",
       "std                 0.011161                 0.026404   \n",
       "min                 0.000000                 0.000000   \n",
       "25%                 0.000000                 0.000000   \n",
       "50%                 0.000000                 0.000000   \n",
       "75%                 0.000000                 0.000000   \n",
       "max                 1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_Concourse Village  neighbourhood_Coney Island  \\\n",
       "count                     40135.000000                40135.000000   \n",
       "mean                          0.000399                    0.000199   \n",
       "std                           0.019963                    0.014117   \n",
       "min                           0.000000                    0.000000   \n",
       "25%                           0.000000                    0.000000   \n",
       "50%                           0.000000                    0.000000   \n",
       "75%                           0.000000                    0.000000   \n",
       "max                           1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_Corona  neighbourhood_Country Club  \\\n",
       "count          40135.000000                40135.000000   \n",
       "mean               0.001545                    0.000050   \n",
       "std                0.039274                    0.007059   \n",
       "min                0.000000                    0.000000   \n",
       "25%                0.000000                    0.000000   \n",
       "50%                0.000000                    0.000000   \n",
       "75%                0.000000                    0.000000   \n",
       "max                1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_Crotona  neighbourhood_Crown Heights  \\\n",
       "count           40135.000000                 40135.000000   \n",
       "mean                0.000224                     0.014003   \n",
       "std                 0.014973                     0.117503   \n",
       "min                 0.000000                     0.000000   \n",
       "25%                 0.000000                     0.000000   \n",
       "50%                 0.000000                     0.000000   \n",
       "75%                 0.000000                     0.000000   \n",
       "max                 1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_DUMBO  neighbourhood_Ditmars / Steinway  \\\n",
       "count         40135.000000                      40135.000000   \n",
       "mean              0.000548                          0.001894   \n",
       "std               0.023406                          0.043475   \n",
       "min               0.000000                          0.000000   \n",
       "25%               0.000000                          0.000000   \n",
       "50%               0.000000                          0.000000   \n",
       "75%               0.000000                          0.000000   \n",
       "max               1.000000                          1.000000   \n",
       "\n",
       "       neighbourhood_Dongan Hills  neighbourhood_Downtown Brooklyn  \\\n",
       "count                40135.000000                     40135.000000   \n",
       "mean                     0.000050                         0.000947   \n",
       "std                      0.007059                         0.030756   \n",
       "min                      0.000000                         0.000000   \n",
       "25%                      0.000000                         0.000000   \n",
       "50%                      0.000000                         0.000000   \n",
       "75%                      0.000000                         0.000000   \n",
       "max                      1.000000                         1.000000   \n",
       "\n",
       "       neighbourhood_Dyker Heights  neighbourhood_East Elmhurst  \\\n",
       "count                 40135.000000                 40135.000000   \n",
       "mean                      0.000174                     0.001221   \n",
       "std                       0.013206                     0.034920   \n",
       "min                       0.000000                     0.000000   \n",
       "25%                       0.000000                     0.000000   \n",
       "50%                       0.000000                     0.000000   \n",
       "75%                       0.000000                     0.000000   \n",
       "max                       1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_East Flatbush  neighbourhood_East Harlem  \\\n",
       "count                 40135.000000               40135.000000   \n",
       "mean                      0.004211                   0.010888   \n",
       "std                       0.064755                   0.103778   \n",
       "min                       0.000000                   0.000000   \n",
       "25%                       0.000000                   0.000000   \n",
       "50%                       0.000000                   0.000000   \n",
       "75%                       0.000000                   0.000000   \n",
       "max                       1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_East New York  neighbourhood_East Village  \\\n",
       "count                 40135.000000                40135.000000   \n",
       "mean                      0.005058                    0.016170   \n",
       "std                       0.070940                    0.126132   \n",
       "min                       0.000000                    0.000000   \n",
       "25%                       0.000000                    0.000000   \n",
       "50%                       0.000000                    0.000000   \n",
       "75%                       0.000000                    0.000000   \n",
       "max                       1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_Eastchester  neighbourhood_Edenwald  \\\n",
       "count               40135.000000            40135.000000   \n",
       "mean                    0.000075                0.000174   \n",
       "std                     0.008645                0.013206   \n",
       "min                     0.000000                0.000000   \n",
       "25%                     0.000000                0.000000   \n",
       "50%                     0.000000                0.000000   \n",
       "75%                     0.000000                0.000000   \n",
       "max                     1.000000                1.000000   \n",
       "\n",
       "       neighbourhood_Elm Park  neighbourhood_Elmhurst  \\\n",
       "count            40135.000000            40135.000000   \n",
       "mean                 0.000174                0.002143   \n",
       "std                  0.013206                0.046241   \n",
       "min                  0.000000                0.000000   \n",
       "25%                  0.000000                0.000000   \n",
       "50%                  0.000000                0.000000   \n",
       "75%                  0.000000                0.000000   \n",
       "max                  1.000000                1.000000   \n",
       "\n",
       "       neighbourhood_Eltingville  neighbourhood_Financial District  \\\n",
       "count               40135.000000                      40135.000000   \n",
       "mean                    0.000025                          0.006902   \n",
       "std                     0.004992                          0.082790   \n",
       "min                     0.000000                          0.000000   \n",
       "25%                     0.000000                          0.000000   \n",
       "50%                     0.000000                          0.000000   \n",
       "75%                     0.000000                          0.000000   \n",
       "max                     1.000000                          1.000000   \n",
       "\n",
       "       neighbourhood_Flatbush  neighbourhood_Flatiron District  \\\n",
       "count            40135.000000                     40135.000000   \n",
       "mean                 0.011013                         0.003015   \n",
       "std                  0.104364                         0.054825   \n",
       "min                  0.000000                         0.000000   \n",
       "25%                  0.000000                         0.000000   \n",
       "50%                  0.000000                         0.000000   \n",
       "75%                  0.000000                         0.000000   \n",
       "max                  1.000000                         1.000000   \n",
       "\n",
       "       neighbourhood_Flatlands  neighbourhood_Flushing  neighbourhood_Fordham  \\\n",
       "count             40135.000000            40135.000000           40135.000000   \n",
       "mean                  0.000698                0.005033               0.000573   \n",
       "std                   0.026404                0.070766               0.023932   \n",
       "min                   0.000000                0.000000               0.000000   \n",
       "25%                   0.000000                0.000000               0.000000   \n",
       "50%                   0.000000                0.000000               0.000000   \n",
       "75%                   0.000000                0.000000               0.000000   \n",
       "max                   1.000000                1.000000               1.000000   \n",
       "\n",
       "       neighbourhood_Forest Hills  neighbourhood_Fort Greene  \\\n",
       "count                40135.000000               40135.000000   \n",
       "mean                     0.001545                   0.004061   \n",
       "std                      0.039274                   0.063600   \n",
       "min                      0.000000                   0.000000   \n",
       "25%                      0.000000                   0.000000   \n",
       "50%                      0.000000                   0.000000   \n",
       "75%                      0.000000                   0.000000   \n",
       "max                      1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Fort Hamilton  neighbourhood_Fort Wadsworth  \\\n",
       "count                 40135.000000                  40135.000000   \n",
       "mean                      0.000025                      0.000050   \n",
       "std                       0.004992                      0.007059   \n",
       "min                       0.000000                      0.000000   \n",
       "25%                       0.000000                      0.000000   \n",
       "50%                       0.000000                      0.000000   \n",
       "75%                       0.000000                      0.000000   \n",
       "max                       1.000000                      1.000000   \n",
       "\n",
       "       neighbourhood_Fresh Meadows  neighbourhood_Gerritsen Beach  \\\n",
       "count                 40135.000000                   40135.000000   \n",
       "mean                      0.000249                       0.000050   \n",
       "std                       0.015783                       0.007059   \n",
       "min                       0.000000                       0.000000   \n",
       "25%                       0.000000                       0.000000   \n",
       "50%                       0.000000                       0.000000   \n",
       "75%                       0.000000                       0.000000   \n",
       "max                       1.000000                       1.000000   \n",
       "\n",
       "       neighbourhood_Glendale  neighbourhood_Gowanus  \\\n",
       "count            40135.000000           40135.000000   \n",
       "mean                 0.000573               0.002068   \n",
       "std                  0.023932               0.045429   \n",
       "min                  0.000000               0.000000   \n",
       "25%                  0.000000               0.000000   \n",
       "50%                  0.000000               0.000000   \n",
       "75%                  0.000000               0.000000   \n",
       "max                  1.000000               1.000000   \n",
       "\n",
       "       neighbourhood_Gramercy Park  neighbourhood_Graniteville  \\\n",
       "count                 40135.000000                40135.000000   \n",
       "mean                      0.002292                    0.000050   \n",
       "std                       0.047823                    0.007059   \n",
       "min                       0.000000                    0.000000   \n",
       "25%                       0.000000                    0.000000   \n",
       "50%                       0.000000                    0.000000   \n",
       "75%                       0.000000                    0.000000   \n",
       "max                       1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_Grant City  neighbourhood_Grasmere  \\\n",
       "count              40135.000000            40135.000000   \n",
       "mean                   0.000075                0.000224   \n",
       "std                    0.008645                0.014973   \n",
       "min                    0.000000                0.000000   \n",
       "25%                    0.000000                0.000000   \n",
       "50%                    0.000000                0.000000   \n",
       "75%                    0.000000                0.000000   \n",
       "max                    1.000000                1.000000   \n",
       "\n",
       "       neighbourhood_Gravesend  neighbourhood_Great Kills  \\\n",
       "count             40135.000000               40135.000000   \n",
       "mean                  0.000648                   0.000125   \n",
       "std                   0.025444                   0.011161   \n",
       "min                   0.000000                   0.000000   \n",
       "25%                   0.000000                   0.000000   \n",
       "50%                   0.000000                   0.000000   \n",
       "75%                   0.000000                   0.000000   \n",
       "max                   1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Greenpoint  neighbourhood_Greenridge  \\\n",
       "count              40135.000000              40135.000000   \n",
       "mean                   0.008397                  0.000025   \n",
       "std                    0.091249                  0.004992   \n",
       "min                    0.000000                  0.000000   \n",
       "25%                    0.000000                  0.000000   \n",
       "50%                    0.000000                  0.000000   \n",
       "75%                    0.000000                  0.000000   \n",
       "max                    1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Greenwich Village  neighbourhood_Greenwood Heights  \\\n",
       "count                     40135.000000                     40135.000000   \n",
       "mean                          0.003314                         0.002143   \n",
       "std                           0.057471                         0.046241   \n",
       "min                           0.000000                         0.000000   \n",
       "25%                           0.000000                         0.000000   \n",
       "50%                           0.000000                         0.000000   \n",
       "75%                           0.000000                         0.000000   \n",
       "max                           1.000000                         1.000000   \n",
       "\n",
       "       neighbourhood_Grymes Hill  neighbourhood_Hamilton Heights  \\\n",
       "count               40135.000000                    40135.000000   \n",
       "mean                    0.000274                        0.007251   \n",
       "std                     0.016553                        0.084842   \n",
       "min                     0.000000                        0.000000   \n",
       "25%                     0.000000                        0.000000   \n",
       "50%                     0.000000                        0.000000   \n",
       "75%                     0.000000                        0.000000   \n",
       "max                     1.000000                        1.000000   \n",
       "\n",
       "       neighbourhood_Harlem  neighbourhood_Hell's Kitchen  \\\n",
       "count          40135.000000                  40135.000000   \n",
       "mean               0.035505                      0.017142   \n",
       "std                0.185055                      0.129803   \n",
       "min                0.000000                      0.000000   \n",
       "25%                0.000000                      0.000000   \n",
       "50%                0.000000                      0.000000   \n",
       "75%                0.000000                      0.000000   \n",
       "max                1.000000                      1.000000   \n",
       "\n",
       "       neighbourhood_Highbridge  neighbourhood_Hillcrest  \\\n",
       "count              40135.000000             40135.000000   \n",
       "mean                   0.000448                 0.000274   \n",
       "std                    0.021173                 0.016553   \n",
       "min                    0.000000                 0.000000   \n",
       "25%                    0.000000                 0.000000   \n",
       "50%                    0.000000                 0.000000   \n",
       "75%                    0.000000                 0.000000   \n",
       "max                    1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_Howard Beach  neighbourhood_Hudson Square  \\\n",
       "count                40135.000000                 40135.000000   \n",
       "mean                     0.000125                     0.000274   \n",
       "std                      0.011161                     0.016553   \n",
       "min                      0.000000                     0.000000   \n",
       "25%                      0.000000                     0.000000   \n",
       "50%                      0.000000                     0.000000   \n",
       "75%                      0.000000                     0.000000   \n",
       "max                      1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_Huguenot  neighbourhood_Hunts Point  \\\n",
       "count            40135.000000               40135.000000   \n",
       "mean                 0.000025                   0.000174   \n",
       "std                  0.004992                   0.013206   \n",
       "min                  0.000000                   0.000000   \n",
       "25%                  0.000000                   0.000000   \n",
       "50%                  0.000000                   0.000000   \n",
       "75%                  0.000000                   0.000000   \n",
       "max                  1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Inwood  neighbourhood_Jackson Heights  \\\n",
       "count          40135.000000                   40135.000000   \n",
       "mean               0.001694                       0.003264   \n",
       "std                0.041127                       0.057039   \n",
       "min                0.000000                       0.000000   \n",
       "25%                0.000000                       0.000000   \n",
       "50%                0.000000                       0.000000   \n",
       "75%                0.000000                       0.000000   \n",
       "max                1.000000                       1.000000   \n",
       "\n",
       "       neighbourhood_Jamaica  neighbourhood_Kensington  \\\n",
       "count           40135.000000              40135.000000   \n",
       "mean                0.008671                  0.001296   \n",
       "std                 0.092713                  0.035972   \n",
       "min                 0.000000                  0.000000   \n",
       "25%                 0.000000                  0.000000   \n",
       "50%                 0.000000                  0.000000   \n",
       "75%                 0.000000                  0.000000   \n",
       "max                 1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Kew Garden Hills  neighbourhood_Kingsbridge  \\\n",
       "count                    40135.000000               40135.000000   \n",
       "mean                         0.000374                   0.000149   \n",
       "std                          0.019329                   0.012226   \n",
       "min                          0.000000                   0.000000   \n",
       "25%                          0.000000                   0.000000   \n",
       "50%                          0.000000                   0.000000   \n",
       "75%                          0.000000                   0.000000   \n",
       "max                          1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Kingsbridge Heights  neighbourhood_Kips Bay  \\\n",
       "count                       40135.000000            40135.000000   \n",
       "mean                            0.000374                0.004709   \n",
       "std                             0.019329                0.068462   \n",
       "min                             0.000000                0.000000   \n",
       "25%                             0.000000                0.000000   \n",
       "50%                             0.000000                0.000000   \n",
       "75%                             0.000000                0.000000   \n",
       "max                             1.000000                1.000000   \n",
       "\n",
       "       neighbourhood_Lefferts Garden  neighbourhood_Lighthouse HIll  \\\n",
       "count                   40135.000000                   40135.000000   \n",
       "mean                        0.003164                       0.000025   \n",
       "std                         0.056164                       0.004992   \n",
       "min                         0.000000                       0.000000   \n",
       "25%                         0.000000                       0.000000   \n",
       "50%                         0.000000                       0.000000   \n",
       "75%                         0.000000                       0.000000   \n",
       "max                         1.000000                       1.000000   \n",
       "\n",
       "       neighbourhood_Lindenwood  neighbourhood_Little Italy  \\\n",
       "count              40135.000000                40135.000000   \n",
       "mean                   0.000125                    0.000249   \n",
       "std                    0.011161                    0.015783   \n",
       "min                    0.000000                    0.000000   \n",
       "25%                    0.000000                    0.000000   \n",
       "50%                    0.000000                    0.000000   \n",
       "75%                    0.000000                    0.000000   \n",
       "max                    1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_Long Island City  neighbourhood_Longwood  \\\n",
       "count                    40135.000000            40135.000000   \n",
       "mean                         0.002193                0.000075   \n",
       "std                          0.046774                0.008645   \n",
       "min                          0.000000                0.000000   \n",
       "25%                          0.000000                0.000000   \n",
       "50%                          0.000000                0.000000   \n",
       "75%                          0.000000                0.000000   \n",
       "max                          1.000000                1.000000   \n",
       "\n",
       "       neighbourhood_Lower East Side  neighbourhood_Manhattan  \\\n",
       "count                   40135.000000             40135.000000   \n",
       "mean                        0.014003                 0.190756   \n",
       "std                         0.117503                 0.392902   \n",
       "min                         0.000000                 0.000000   \n",
       "25%                         0.000000                 0.000000   \n",
       "50%                         0.000000                 0.000000   \n",
       "75%                         0.000000                 0.000000   \n",
       "max                         1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_Manhattan Beach  neighbourhood_Marble Hill  \\\n",
       "count                   40135.000000               40135.000000   \n",
       "mean                        0.000224                   0.000050   \n",
       "std                         0.014973                   0.007059   \n",
       "min                         0.000000                   0.000000   \n",
       "25%                         0.000000                   0.000000   \n",
       "50%                         0.000000                   0.000000   \n",
       "75%                         0.000000                   0.000000   \n",
       "max                         1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Mariners Harbor  neighbourhood_Maspeth  \\\n",
       "count                   40135.000000           40135.000000   \n",
       "mean                        0.000050               0.001345   \n",
       "std                         0.007059               0.036656   \n",
       "min                         0.000000               0.000000   \n",
       "25%                         0.000000               0.000000   \n",
       "50%                         0.000000               0.000000   \n",
       "75%                         0.000000               0.000000   \n",
       "max                         1.000000               1.000000   \n",
       "\n",
       "       neighbourhood_Meatpacking District  neighbourhood_Melrose  \\\n",
       "count                        40135.000000           40135.000000   \n",
       "mean                             0.000299               0.000149   \n",
       "std                              0.017289               0.012226   \n",
       "min                              0.000000               0.000000   \n",
       "25%                              0.000000               0.000000   \n",
       "50%                              0.000000               0.000000   \n",
       "75%                              0.000000               0.000000   \n",
       "max                              1.000000               1.000000   \n",
       "\n",
       "       neighbourhood_Middle Village  neighbourhood_Midland Beach  \\\n",
       "count                  40135.000000                 40135.000000   \n",
       "mean                       0.000448                     0.000100   \n",
       "std                        0.021173                     0.009983   \n",
       "min                        0.000000                     0.000000   \n",
       "25%                        0.000000                     0.000000   \n",
       "50%                        0.000000                     0.000000   \n",
       "75%                        0.000000                     0.000000   \n",
       "max                        1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_Midtown  neighbourhood_Midtown East  \\\n",
       "count           40135.000000                40135.000000   \n",
       "mean                0.027084                    0.006976   \n",
       "std                 0.162329                    0.083234   \n",
       "min                 0.000000                    0.000000   \n",
       "25%                 0.000000                    0.000000   \n",
       "50%                 0.000000                    0.000000   \n",
       "75%                 0.000000                    0.000000   \n",
       "max                 1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_Midwood  neighbourhood_Mill Basin  \\\n",
       "count           40135.000000              40135.000000   \n",
       "mean                0.001271                  0.000050   \n",
       "std                 0.035625                  0.007059   \n",
       "min                 0.000000                  0.000000   \n",
       "25%                 0.000000                  0.000000   \n",
       "50%                 0.000000                  0.000000   \n",
       "75%                 0.000000                  0.000000   \n",
       "max                 1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Morningside Heights  neighbourhood_Morris Heights  \\\n",
       "count                       40135.000000                  40135.000000   \n",
       "mean                            0.001395                      0.000374   \n",
       "std                             0.037328                      0.019329   \n",
       "min                             0.000000                      0.000000   \n",
       "25%                             0.000000                      0.000000   \n",
       "50%                             0.000000                      0.000000   \n",
       "75%                             0.000000                      0.000000   \n",
       "max                             1.000000                      1.000000   \n",
       "\n",
       "       neighbourhood_Morris Park  neighbourhood_Morrisania  \\\n",
       "count               40135.000000              40135.000000   \n",
       "mean                    0.000174                  0.000299   \n",
       "std                     0.013206                  0.017289   \n",
       "min                     0.000000                  0.000000   \n",
       "25%                     0.000000                  0.000000   \n",
       "50%                     0.000000                  0.000000   \n",
       "75%                     0.000000                  0.000000   \n",
       "max                     1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Mott Haven  neighbourhood_Mount Eden  \\\n",
       "count              40135.000000              40135.000000   \n",
       "mean                   0.000922                  0.000050   \n",
       "std                    0.030349                  0.007059   \n",
       "min                    0.000000                  0.000000   \n",
       "25%                    0.000000                  0.000000   \n",
       "50%                    0.000000                  0.000000   \n",
       "75%                    0.000000                  0.000000   \n",
       "max                    1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Murray Hill  neighbourhood_New Brighton  \\\n",
       "count               40135.000000                40135.000000   \n",
       "mean                    0.002392                    0.000025   \n",
       "std                     0.048849                    0.004992   \n",
       "min                     0.000000                    0.000000   \n",
       "25%                     0.000000                    0.000000   \n",
       "50%                     0.000000                    0.000000   \n",
       "75%                     0.000000                    0.000000   \n",
       "max                     1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_New Dorp  neighbourhood_New Dorp Beach  \\\n",
       "count            40135.000000                  40135.000000   \n",
       "mean                 0.000025                      0.000075   \n",
       "std                  0.004992                      0.008645   \n",
       "min                  0.000000                      0.000000   \n",
       "25%                  0.000000                      0.000000   \n",
       "50%                  0.000000                      0.000000   \n",
       "75%                  0.000000                      0.000000   \n",
       "max                  1.000000                      1.000000   \n",
       "\n",
       "       neighbourhood_New Springville  neighbourhood_Noho  \\\n",
       "count                   40135.000000        40135.000000   \n",
       "mean                        0.000075            0.000424   \n",
       "std                         0.008645            0.020577   \n",
       "min                         0.000000            0.000000   \n",
       "25%                         0.000000            0.000000   \n",
       "50%                         0.000000            0.000000   \n",
       "75%                         0.000000            0.000000   \n",
       "max                         1.000000            1.000000   \n",
       "\n",
       "       neighbourhood_Nolita  neighbourhood_Norwood  neighbourhood_Ozone Park  \\\n",
       "count          40135.000000           40135.000000              40135.000000   \n",
       "mean               0.001620               0.000199                  0.000648   \n",
       "std                0.040211               0.014117                  0.025444   \n",
       "min                0.000000               0.000000                  0.000000   \n",
       "25%                0.000000               0.000000                  0.000000   \n",
       "50%                0.000000               0.000000                  0.000000   \n",
       "75%                0.000000               0.000000                  0.000000   \n",
       "max                1.000000               1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Park Slope  neighbourhood_Park Versailles  \\\n",
       "count              40135.000000                   40135.000000   \n",
       "mean                   0.006952                       0.000149   \n",
       "std                    0.083087                       0.012226   \n",
       "min                    0.000000                       0.000000   \n",
       "25%                    0.000000                       0.000000   \n",
       "50%                    0.000000                       0.000000   \n",
       "75%                    0.000000                       0.000000   \n",
       "max                    1.000000                       1.000000   \n",
       "\n",
       "       neighbourhood_Parkchester  neighbourhood_Pelham Bay  \\\n",
       "count               40135.000000              40135.000000   \n",
       "mean                    0.000349                  0.000224   \n",
       "std                     0.018674                  0.014973   \n",
       "min                     0.000000                  0.000000   \n",
       "25%                     0.000000                  0.000000   \n",
       "50%                     0.000000                  0.000000   \n",
       "75%                     0.000000                  0.000000   \n",
       "max                     1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Pleasant Plains  neighbourhood_Port Morris  \\\n",
       "count                   40135.000000               40135.000000   \n",
       "mean                        0.000050                   0.000747   \n",
       "std                         0.007059                   0.027330   \n",
       "min                         0.000000                   0.000000   \n",
       "25%                         0.000000                   0.000000   \n",
       "50%                         0.000000                   0.000000   \n",
       "75%                         0.000000                   0.000000   \n",
       "max                         1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Port Richmond  neighbourhood_Prospect Heights  \\\n",
       "count                 40135.000000                    40135.000000   \n",
       "mean                      0.000174                        0.003015   \n",
       "std                       0.013206                        0.054825   \n",
       "min                       0.000000                        0.000000   \n",
       "25%                       0.000000                        0.000000   \n",
       "50%                       0.000000                        0.000000   \n",
       "75%                       0.000000                        0.000000   \n",
       "max                       1.000000                        1.000000   \n",
       "\n",
       "       neighbourhood_Queens  neighbourhood_Randall Manor  \\\n",
       "count          40135.000000                 40135.000000   \n",
       "mean               0.061019                     0.000025   \n",
       "std                0.239368                     0.004992   \n",
       "min                0.000000                     0.000000   \n",
       "25%                0.000000                     0.000000   \n",
       "50%                0.000000                     0.000000   \n",
       "75%                0.000000                     0.000000   \n",
       "max                1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_Red Hook  neighbourhood_Rego Park  \\\n",
       "count            40135.000000             40135.000000   \n",
       "mean                 0.000723                 0.001022   \n",
       "std                  0.026871                 0.031946   \n",
       "min                  0.000000                 0.000000   \n",
       "25%                  0.000000                 0.000000   \n",
       "50%                  0.000000                 0.000000   \n",
       "75%                  0.000000                 0.000000   \n",
       "max                  1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_Richmond Hill  neighbourhood_Ridgewood  \\\n",
       "count                 40135.000000             40135.000000   \n",
       "mean                      0.001943                 0.003712   \n",
       "std                       0.044042                 0.060818   \n",
       "min                       0.000000                 0.000000   \n",
       "25%                       0.000000                 0.000000   \n",
       "50%                       0.000000                 0.000000   \n",
       "75%                       0.000000                 0.000000   \n",
       "max                       1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_Riverdale  neighbourhood_Roosevelt Island  \\\n",
       "count             40135.000000                    40135.000000   \n",
       "mean                  0.000299                        0.001271   \n",
       "std                   0.017289                        0.035625   \n",
       "min                   0.000000                        0.000000   \n",
       "25%                   0.000000                        0.000000   \n",
       "50%                   0.000000                        0.000000   \n",
       "75%                   0.000000                        0.000000   \n",
       "max                   1.000000                        1.000000   \n",
       "\n",
       "       neighbourhood_Rosebank  neighbourhood_Rossville  \\\n",
       "count            40135.000000             40135.000000   \n",
       "mean                 0.000100                 0.000025   \n",
       "std                  0.009983                 0.004992   \n",
       "min                  0.000000                 0.000000   \n",
       "25%                  0.000000                 0.000000   \n",
       "50%                  0.000000                 0.000000   \n",
       "75%                  0.000000                 0.000000   \n",
       "max                  1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_Sea Gate  neighbourhood_Sheepshead Bay  \\\n",
       "count            40135.000000                  40135.000000   \n",
       "mean                 0.000025                      0.001620   \n",
       "std                  0.004992                      0.040211   \n",
       "min                  0.000000                      0.000000   \n",
       "25%                  0.000000                      0.000000   \n",
       "50%                  0.000000                      0.000000   \n",
       "75%                  0.000000                      0.000000   \n",
       "max                  1.000000                      1.000000   \n",
       "\n",
       "       neighbourhood_Soho  neighbourhood_Soundview  neighbourhood_South Beach  \\\n",
       "count        40135.000000             40135.000000               40135.000000   \n",
       "mean             0.002741                 0.000274                   0.000174   \n",
       "std              0.052281                 0.016553                   0.013206   \n",
       "min              0.000000                 0.000000                   0.000000   \n",
       "25%              0.000000                 0.000000                   0.000000   \n",
       "50%              0.000000                 0.000000                   0.000000   \n",
       "75%              0.000000                 0.000000                   0.000000   \n",
       "max              1.000000                 1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_South Ozone Park  neighbourhood_South Street Seaport  \\\n",
       "count                    40135.000000                        40135.000000   \n",
       "mean                         0.000424                            0.000100   \n",
       "std                          0.020577                            0.009983   \n",
       "min                          0.000000                            0.000000   \n",
       "25%                          0.000000                            0.000000   \n",
       "50%                          0.000000                            0.000000   \n",
       "75%                          0.000000                            0.000000   \n",
       "max                          1.000000                            1.000000   \n",
       "\n",
       "       neighbourhood_Spuyten Duyvil  neighbourhood_St. George  \\\n",
       "count                  40135.000000              40135.000000   \n",
       "mean                       0.000050                  0.000623   \n",
       "std                        0.007059                  0.024950   \n",
       "min                        0.000000                  0.000000   \n",
       "25%                        0.000000                  0.000000   \n",
       "50%                        0.000000                  0.000000   \n",
       "75%                        0.000000                  0.000000   \n",
       "max                        1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Stapleton  neighbourhood_Staten Island  \\\n",
       "count             40135.000000                 40135.000000   \n",
       "mean                  0.000274                     0.004385   \n",
       "std                   0.016553                     0.066076   \n",
       "min                   0.000000                     0.000000   \n",
       "25%                   0.000000                     0.000000   \n",
       "50%                   0.000000                     0.000000   \n",
       "75%                   0.000000                     0.000000   \n",
       "max                   1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_Sunnyside  neighbourhood_Sunset Park  \\\n",
       "count             40135.000000               40135.000000   \n",
       "mean                  0.003688                   0.003588   \n",
       "std                   0.060614                   0.059792   \n",
       "min                   0.000000                   0.000000   \n",
       "25%                   0.000000                   0.000000   \n",
       "50%                   0.000000                   0.000000   \n",
       "75%                   0.000000                   0.000000   \n",
       "max                   1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_The Bronx  neighbourhood_The Rockaways  \\\n",
       "count             40135.000000                 40135.000000   \n",
       "mean                  0.011686                     0.005905   \n",
       "std                   0.107468                     0.076618   \n",
       "min                   0.000000                     0.000000   \n",
       "25%                   0.000000                     0.000000   \n",
       "50%                   0.000000                     0.000000   \n",
       "75%                   0.000000                     0.000000   \n",
       "max                   1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_Throgs Neck  neighbourhood_Times Square/Theatre District  \\\n",
       "count               40135.000000                                 40135.000000   \n",
       "mean                    0.000299                                     0.001121   \n",
       "std                     0.017289                                     0.033466   \n",
       "min                     0.000000                                     0.000000   \n",
       "25%                     0.000000                                     0.000000   \n",
       "50%                     0.000000                                     0.000000   \n",
       "75%                     0.000000                                     0.000000   \n",
       "max                     1.000000                                     1.000000   \n",
       "\n",
       "       neighbourhood_Tompkinsville  neighbourhood_Tottenville  \\\n",
       "count                 40135.000000               40135.000000   \n",
       "mean                      0.000299                   0.000075   \n",
       "std                       0.017289                   0.008645   \n",
       "min                       0.000000                   0.000000   \n",
       "25%                       0.000000                   0.000000   \n",
       "50%                       0.000000                   0.000000   \n",
       "75%                       0.000000                   0.000000   \n",
       "max                       1.000000                   1.000000   \n",
       "\n",
       "       neighbourhood_Tremont  neighbourhood_Tribeca  \\\n",
       "count           40135.000000           40135.000000   \n",
       "mean                0.000573               0.001271   \n",
       "std                 0.023932               0.035625   \n",
       "min                 0.000000               0.000000   \n",
       "25%                 0.000000               0.000000   \n",
       "50%                 0.000000               0.000000   \n",
       "75%                 0.000000               0.000000   \n",
       "max                 1.000000               1.000000   \n",
       "\n",
       "       neighbourhood_Union Square  neighbourhood_University Heights  \\\n",
       "count                40135.000000                      40135.000000   \n",
       "mean                     0.000399                          0.000224   \n",
       "std                      0.019963                          0.014973   \n",
       "min                      0.000000                          0.000000   \n",
       "25%                      0.000000                          0.000000   \n",
       "50%                      0.000000                          0.000000   \n",
       "75%                      0.000000                          0.000000   \n",
       "max                      1.000000                          1.000000   \n",
       "\n",
       "       neighbourhood_Upper East Side  neighbourhood_Upper West Side  \\\n",
       "count                   40135.000000                   40135.000000   \n",
       "mean                        0.017840                       0.017018   \n",
       "std                         0.132371                       0.129338   \n",
       "min                         0.000000                       0.000000   \n",
       "25%                         0.000000                       0.000000   \n",
       "50%                         0.000000                       0.000000   \n",
       "75%                         0.000000                       0.000000   \n",
       "max                         1.000000                       1.000000   \n",
       "\n",
       "       neighbourhood_Utopia  neighbourhood_Van Nest  neighbourhood_Wakefield  \\\n",
       "count          40135.000000            40135.000000             40135.000000   \n",
       "mean               0.000249                0.000125                 0.000573   \n",
       "std                0.015783                0.011161                 0.023932   \n",
       "min                0.000000                0.000000                 0.000000   \n",
       "25%                0.000000                0.000000                 0.000000   \n",
       "50%                0.000000                0.000000                 0.000000   \n",
       "75%                0.000000                0.000000                 0.000000   \n",
       "max                1.000000                1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_Washington Heights  neighbourhood_West Brighton  \\\n",
       "count                      40135.000000                 40135.000000   \n",
       "mean                           0.008571                     0.000399   \n",
       "std                            0.092184                     0.019963   \n",
       "min                            0.000000                     0.000000   \n",
       "25%                            0.000000                     0.000000   \n",
       "50%                            0.000000                     0.000000   \n",
       "75%                            0.000000                     0.000000   \n",
       "max                            1.000000                     1.000000   \n",
       "\n",
       "       neighbourhood_West Village  neighbourhood_Westchester Village  \\\n",
       "count                40135.000000                       40135.000000   \n",
       "mean                     0.007300                           0.000125   \n",
       "std                      0.085131                           0.011161   \n",
       "min                      0.000000                           0.000000   \n",
       "25%                      0.000000                           0.000000   \n",
       "50%                      0.000000                           0.000000   \n",
       "75%                      0.000000                           0.000000   \n",
       "max                      1.000000                           1.000000   \n",
       "\n",
       "       neighbourhood_Westerleigh  neighbourhood_Whitestone  \\\n",
       "count               40135.000000              40135.000000   \n",
       "mean                    0.000050                  0.000149   \n",
       "std                     0.007059                  0.012226   \n",
       "min                     0.000000                  0.000000   \n",
       "25%                     0.000000                  0.000000   \n",
       "50%                     0.000000                  0.000000   \n",
       "75%                     0.000000                  0.000000   \n",
       "max                     1.000000                  1.000000   \n",
       "\n",
       "       neighbourhood_Williamsbridge  neighbourhood_Williamsburg  \\\n",
       "count                  40135.000000                40135.000000   \n",
       "mean                       0.000349                    0.037075   \n",
       "std                        0.018674                    0.188948   \n",
       "min                        0.000000                    0.000000   \n",
       "25%                        0.000000                    0.000000   \n",
       "50%                        0.000000                    0.000000   \n",
       "75%                        0.000000                    0.000000   \n",
       "max                        1.000000                    1.000000   \n",
       "\n",
       "       neighbourhood_Windsor Terrace  neighbourhood_Woodhaven  \\\n",
       "count                   40135.000000             40135.000000   \n",
       "mean                        0.001345                 0.001221   \n",
       "std                         0.036656                 0.034920   \n",
       "min                         0.000000                 0.000000   \n",
       "25%                         0.000000                 0.000000   \n",
       "50%                         0.000000                 0.000000   \n",
       "75%                         0.000000                 0.000000   \n",
       "max                         1.000000                 1.000000   \n",
       "\n",
       "       neighbourhood_Woodlawn  neighbourhood_Woodside  \n",
       "count            40135.000000            40135.000000  \n",
       "mean                 0.000050                0.001719  \n",
       "std                  0.007059                0.041428  \n",
       "min                  0.000000                0.000000  \n",
       "25%                  0.000000                0.000000  \n",
       "50%                  0.000000                0.000000  \n",
       "75%                  0.000000                0.000000  \n",
       "max                  1.000000                1.000000  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df_final.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_controllable_factors = df_final.sample(frac=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_controllable_factors.to_csv('datasets/uncontrollable_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_controllable_factors.drop(columns=['Label'])\n",
    "Y = df_controllable_factors['Label']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=.80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the decision tree module from sklearn\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(criterion = 'entropy', min_samples_leaf=50, min_samples_split=100)\n",
    "#dt = DecisionTreeClassifier(criterion = 'entropy')\n",
    "fit = dt.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Get feature importances\n",
    "feature_mi = dt.feature_importances_\n",
    "\n",
    "#Sort indices\n",
    "featureinds = feature_mi.argsort()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"Feature Importance\")\n",
    "\n",
    "#Plot features in order of importance\n",
    "plt.bar(X_train.columns.values[featureinds[::-1]],feature_mi[featureinds[::-1]])\n",
    "plt.xticks(X_train.columns.values[featureinds[::-1]],rotation=\"90\")\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy is 0.6428927370125825\n",
      "Test Accuracy is 0.5426008968609866\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#Make predictions for both training and test data\n",
    "predictions_train=dt.predict(X_train)\n",
    "predictions_test=dt.predict(X_test)\n",
    "\n",
    "#Compute accuracies\n",
    "train_accuracy=accuracy_score(Y_train,predictions_train)\n",
    "test_accuracy=accuracy_score(Y_test,predictions_test)\n",
    "\n",
    "print(\"Training Accuracy is \" + str(train_accuracy))\n",
    "print(\"Test Accuracy is \" + str(test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import linear_model, svm, metrics\n",
    "\n",
    "#lr_model = linear_model.LogisticRegression(C=1e30)\n",
    "#lr_model.fit(X_train, Y_train)\n",
    "#predictions_lr = lr_model.predict_proba(X_test)[:,1]\n",
    "predictions_dt = dt.predict(X_test)\n",
    "\n",
    "#svm_model = svm.SVC(kernel=\"linear\")\n",
    "#svm_model.fit(X_train, Y_train)\n",
    "#predictions_svm = svm_model.decision_function(X_test)\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "#Plot LR ROC/AUC\n",
    "fpr, tpr, thresholds = metrics.roc_curve(Y_test, predictions_dt)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "label = \"LR AUC = %.3f\"%auc\n",
    "plt.plot(fpr,tpr,label = label)\n",
    "\n",
    "\n",
    "#Plot 45 degree line\n",
    "plt.plot([0,1],[0,1])\n",
    "\n",
    "plt.title('ROC')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardize Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bakeoff\n",
    "\n",
    "Models:\n",
    "- Decision Tree\n",
    "- Logistic Regression\n",
    "- SVM\n",
    "- GB\n",
    "- RF\n",
    "- NB\n",
    "\n",
    "Features:\n",
    "- All\n",
    "- All - Neighbourhoods\n",
    "- Dimensionality Reduction\n",
    "\n",
    "Hyperparameters:\n",
    "- Decision Trees (Faizan)\n",
    " - min sample split, min leaf size\n",
    "- Logistic (Connor)\n",
    " - Regularization weight\n",
    " - L1 vs. L2\n",
    "- SVM (Connor)\n",
    " - Weight\n",
    " - Kernel\n",
    "- GB (Abbas/Audrey)\n",
    " - Max Depth / min sample split / min leaf\n",
    " - number of trees\n",
    "- RF (Abbas/Audrey)\n",
    " - number of trees\n",
    " - number of features\n",
    "    \n",
    "\n",
    "Evaluation metrics:\n",
    "- Accuracy\n",
    "- AUC\n",
    "- Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}