Groupby.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathName_block_groups = r\"D:\\Dropbox\\Dropbox\\Teaching\\Urban_Data_Analsysis\\Fall2021\\Data\\BlockGroups.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(pathName_block_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>INTPTLAT</th>\n",
       "      <th>INTPTLON</th>\n",
       "      <th>GISJOIN</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>Median_age</th>\n",
       "      <th>Population</th>\n",
       "      <th>WhiteAlone</th>\n",
       "      <th>BlackAlone</th>\n",
       "      <th>NativeAlone</th>\n",
       "      <th>BlackAlonWithComb</th>\n",
       "      <th>...</th>\n",
       "      <th>pctCommutePTnsitOver60</th>\n",
       "      <th>pctCommuteWalkBike</th>\n",
       "      <th>pct_LivingAlone65Over</th>\n",
       "      <th>pct_LessThanHighSchool</th>\n",
       "      <th>pctMobileHomes</th>\n",
       "      <th>BlcokG_Area_SqMeter</th>\n",
       "      <th>WaterFront</th>\n",
       "      <th>NearParks</th>\n",
       "      <th>NearMajorParks</th>\n",
       "      <th>boro_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>40.673551</td>\n",
       "      <td>-73.927889</td>\n",
       "      <td>G36004700347001</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>41.1</td>\n",
       "      <td>799</td>\n",
       "      <td>17</td>\n",
       "      <td>768</td>\n",
       "      <td>0</td>\n",
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       "      <td>6.508135</td>\n",
       "      <td>8.635795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46498.42541</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>40.761234</td>\n",
       "      <td>-73.819913</td>\n",
       "      <td>G36008100863003</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>33.7</td>\n",
       "      <td>1485</td>\n",
       "      <td>156</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>7.744108</td>\n",
       "      <td>7.407407</td>\n",
       "      <td>3.367003</td>\n",
       "      <td>15.286200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16701.60113</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "      <td>Queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2616</th>\n",
       "      <td>40.665803</td>\n",
       "      <td>-73.837404</td>\n",
       "      <td>G36008100884001</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>54.4</td>\n",
       "      <td>824</td>\n",
       "      <td>817</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>7.402913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.820388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324779.76670</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "      <td>Queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>40.779083</td>\n",
       "      <td>-73.975717</td>\n",
       "      <td>G36006100161003</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>36.0</td>\n",
       "      <td>907</td>\n",
       "      <td>760</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>3.528115</td>\n",
       "      <td>24.696800</td>\n",
       "      <td>8.489526</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22096.39783</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4936</th>\n",
       "      <td>40.744603</td>\n",
       "      <td>-73.977457</td>\n",
       "      <td>G36006100070001</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>32.7</td>\n",
       "      <td>2159</td>\n",
       "      <td>1133</td>\n",
       "      <td>287</td>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "      <td>...</td>\n",
       "      <td>2.686429</td>\n",
       "      <td>32.561370</td>\n",
       "      <td>6.484484</td>\n",
       "      <td>0.555813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35669.63566</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4763</th>\n",
       "      <td>40.597002</td>\n",
       "      <td>-73.741111</td>\n",
       "      <td>G36008101010022</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>50.8</td>\n",
       "      <td>1913</td>\n",
       "      <td>1192</td>\n",
       "      <td>668</td>\n",
       "      <td>0</td>\n",
       "      <td>683</td>\n",
       "      <td>...</td>\n",
       "      <td>8.572922</td>\n",
       "      <td>2.665970</td>\n",
       "      <td>14.636700</td>\n",
       "      <td>5.750131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152409.68890</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2219</th>\n",
       "      <td>40.764765</td>\n",
       "      <td>-73.986157</td>\n",
       "      <td>G36006100133005</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>37.1</td>\n",
       "      <td>1033</td>\n",
       "      <td>648</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>2.807357</td>\n",
       "      <td>25.169410</td>\n",
       "      <td>1.355276</td>\n",
       "      <td>1.645692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43496.46574</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5631</th>\n",
       "      <td>40.634221</td>\n",
       "      <td>-73.983990</td>\n",
       "      <td>G36004700232001</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>17.5</td>\n",
       "      <td>1475</td>\n",
       "      <td>1475</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.474576</td>\n",
       "      <td>9.966102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.288136</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56509.86694</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3156</th>\n",
       "      <td>40.830792</td>\n",
       "      <td>-73.874156</td>\n",
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       "      <td>2012-2016</td>\n",
       "      <td>29.5</td>\n",
       "      <td>792</td>\n",
       "      <td>27</td>\n",
       "      <td>238</td>\n",
       "      <td>0</td>\n",
       "      <td>238</td>\n",
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       "      <td>6.944445</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.671717</td>\n",
       "      <td>5.555555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29726.65289</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>40.776747</td>\n",
       "      <td>-73.800791</td>\n",
       "      <td>G36008101017005</td>\n",
       "      <td>2012-2016</td>\n",
       "      <td>47.3</td>\n",
       "      <td>1051</td>\n",
       "      <td>703</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>1.712655</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.807802</td>\n",
       "      <td>7.611798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145682.90780</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "      <td>Queens</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       INTPTLAT   INTPTLON          GISJOIN       YEAR  Median_age  \\\n",
       "3581  40.673551 -73.927889  G36004700347001  2012-2016        41.1   \n",
       "2975  40.761234 -73.819913  G36008100863003  2012-2016        33.7   \n",
       "2616  40.665803 -73.837404  G36008100884001  2012-2016        54.4   \n",
       "2918  40.779083 -73.975717  G36006100161003  2012-2016        36.0   \n",
       "4936  40.744603 -73.977457  G36006100070001  2012-2016        32.7   \n",
       "4763  40.597002 -73.741111  G36008101010022  2012-2016        50.8   \n",
       "2219  40.764765 -73.986157  G36006100133005  2012-2016        37.1   \n",
       "5631  40.634221 -73.983990  G36004700232001  2012-2016        17.5   \n",
       "3156  40.830792 -73.874156  G36000500062005  2012-2016        29.5   \n",
       "1041  40.776747 -73.800791  G36008101017005  2012-2016        47.3   \n",
       "\n",
       "      Population  WhiteAlone  BlackAlone  NativeAlone  BlackAlonWithComb  ...  \\\n",
       "3581         799          17         768            0                768  ...   \n",
       "2975        1485         156          29            0                 29  ...   \n",
       "2616         824         817           3            0                  5  ...   \n",
       "2918         907         760          15            0                 15  ...   \n",
       "4936        2159        1133         287            0                287  ...   \n",
       "4763        1913        1192         668            0                683  ...   \n",
       "2219        1033         648          88            0                 88  ...   \n",
       "5631        1475        1475           0            0                  0  ...   \n",
       "3156         792          27         238            0                238  ...   \n",
       "1041        1051         703          16            0                 35  ...   \n",
       "\n",
       "      pctCommutePTnsitOver60  pctCommuteWalkBike  pct_LivingAlone65Over  \\\n",
       "3581               10.012520            0.000000               6.508135   \n",
       "2975                7.744108            7.407407               3.367003   \n",
       "2616                7.402913            0.000000               1.820388   \n",
       "2918                3.528115           24.696800               8.489526   \n",
       "4936                2.686429           32.561370               6.484484   \n",
       "4763                8.572922            2.665970              14.636700   \n",
       "2219                2.807357           25.169410               1.355276   \n",
       "5631                4.474576            9.966102               0.000000   \n",
       "3156                6.944445            0.000000               4.671717   \n",
       "1041                1.712655            0.000000               1.807802   \n",
       "\n",
       "     pct_LessThanHighSchool  pctMobileHomes  BlcokG_Area_SqMeter  WaterFront  \\\n",
       "3581               8.635795             0.0          46498.42541          No   \n",
       "2975              15.286200             0.0          16701.60113          No   \n",
       "2616               0.000000             0.0         324779.76670          No   \n",
       "2918               0.000000             0.0          22096.39783          No   \n",
       "4936               0.555813             0.0          35669.63566          No   \n",
       "4763               5.750131             0.0         152409.68890         Yes   \n",
       "2219               1.645692             0.0          43496.46574          No   \n",
       "5631               1.288136             0.0          56509.86694          No   \n",
       "3156               5.555555             0.0          29726.65289          No   \n",
       "1041               7.611798             0.0         145682.90780          No   \n",
       "\n",
       "      NearParks  NearMajorParks  boro_name  \n",
       "3581        Yes              NO   Brooklyn  \n",
       "2975         No              NO     Queens  \n",
       "2616        Yes              NO     Queens  \n",
       "2918        Yes             Yes  Manhattan  \n",
       "4936         No              NO  Manhattan  \n",
       "4763        Yes             Yes     Queens  \n",
       "2219         No              NO  Manhattan  \n",
       "5631         No              NO   Brooklyn  \n",
       "3156        Yes              NO      Bronx  \n",
       "1041        Yes              NO     Queens  \n",
       "\n",
       "[10 rows x 42 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5852 entries, 0 to 5851\n",
      "Data columns (total 42 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   INTPTLAT                5852 non-null   float64\n",
      " 1   INTPTLON                5852 non-null   float64\n",
      " 2   GISJOIN                 5852 non-null   object \n",
      " 3   YEAR                    5852 non-null   object \n",
      " 4   Median_age              5852 non-null   float64\n",
      " 5   Population              5852 non-null   int64  \n",
      " 6   WhiteAlone              5852 non-null   int64  \n",
      " 7   BlackAlone              5852 non-null   int64  \n",
      " 8   NativeAlone             5852 non-null   int64  \n",
      " 9   BlackAlonWithComb       5852 non-null   int64  \n",
      " 10  Hisp_Latino             5852 non-null   int64  \n",
      " 11  MedHouseholdIncome      5852 non-null   int64  \n",
      " 12  PerCapitaIncome         5852 non-null   int64  \n",
      " 13  MedYearBuilt            5852 non-null   object \n",
      " 14  MedianValue             5852 non-null   object \n",
      " 15  PctNonWhite             5852 non-null   float64\n",
      " 16  PctBlackOnly            5852 non-null   float64\n",
      " 17  PctHisLatOnly           5852 non-null   float64\n",
      " 18  HsngUnits               5852 non-null   int64  \n",
      " 19  HsngDenPerHac           5852 non-null   float64\n",
      " 20  pop_65above             5852 non-null   int64  \n",
      " 21  Fem_65Above             5852 non-null   int64  \n",
      " 22  Commute30Over           5852 non-null   int64  \n",
      " 23  commute60Over           5852 non-null   int64  \n",
      " 24  commutePtransitOver60   5852 non-null   int64  \n",
      " 25  commuteWalkBike         5852 non-null   int64  \n",
      " 26  LivingAloneOver65       5852 non-null   int64  \n",
      " 27  LessThanHighSchool      5852 non-null   int64  \n",
      " 28  TotPopulation           5852 non-null   int64  \n",
      " 29  pct_fem65Over           5852 non-null   float64\n",
      " 30  pct_popOver65           5852 non-null   float64\n",
      " 31  pctCommute60Over        5852 non-null   float64\n",
      " 32  pctCommutePTnsitOver60  5852 non-null   float64\n",
      " 33  pctCommuteWalkBike      5852 non-null   float64\n",
      " 34  pct_LivingAlone65Over   5852 non-null   float64\n",
      " 35  pct_LessThanHighSchool  5852 non-null   float64\n",
      " 36  pctMobileHomes          5852 non-null   float64\n",
      " 37  BlcokG_Area_SqMeter     5852 non-null   float64\n",
      " 38  WaterFront              5852 non-null   object \n",
      " 39  NearParks               5852 non-null   object \n",
      " 40  NearMajorParks          5852 non-null   object \n",
      " 41  boro_name               5852 non-null   object \n",
      "dtypes: float64(16), int64(18), object(8)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MedianValue'] = pd.to_numeric(df['MedianValue'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>INTPTLAT</th>\n",
       "      <th>INTPTLON</th>\n",
       "      <th>Median_age</th>\n",
       "      <th>Population</th>\n",
       "      <th>WhiteAlone</th>\n",
       "      <th>BlackAlone</th>\n",
       "      <th>NativeAlone</th>\n",
       "      <th>BlackAlonWithComb</th>\n",
       "      <th>Hisp_Latino</th>\n",
       "      <th>MedHouseholdIncome</th>\n",
       "      <th>...</th>\n",
       "      <th>TotPopulation</th>\n",
       "      <th>pct_fem65Over</th>\n",
       "      <th>pct_popOver65</th>\n",
       "      <th>pctCommute60Over</th>\n",
       "      <th>pctCommutePTnsitOver60</th>\n",
       "      <th>pctCommuteWalkBike</th>\n",
       "      <th>pct_LivingAlone65Over</th>\n",
       "      <th>pct_LessThanHighSchool</th>\n",
       "      <th>pctMobileHomes</th>\n",
       "      <th>BlcokG_Area_SqMeter</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boro_name</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>Bronx</th>\n",
       "      <td>40.851021</td>\n",
       "      <td>-73.881231</td>\n",
       "      <td>34.973161</td>\n",
       "      <td>1318.750497</td>\n",
       "      <td>269.713718</td>\n",
       "      <td>437.056660</td>\n",
       "      <td>7.479125</td>\n",
       "      <td>466.890656</td>\n",
       "      <td>731.420477</td>\n",
       "      <td>41625.888668</td>\n",
       "      <td>...</td>\n",
       "      <td>1318.750497</td>\n",
       "      <td>7.266134</td>\n",
       "      <td>11.918698</td>\n",
       "      <td>12.724378</td>\n",
       "      <td>11.147376</td>\n",
       "      <td>3.161765</td>\n",
       "      <td>3.960107</td>\n",
       "      <td>9.119549</td>\n",
       "      <td>0.146485</td>\n",
       "      <td>76409.891223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>40.651654</td>\n",
       "      <td>-73.955784</td>\n",
       "      <td>35.871941</td>\n",
       "      <td>1294.089606</td>\n",
       "      <td>560.923195</td>\n",
       "      <td>431.806964</td>\n",
       "      <td>4.263697</td>\n",
       "      <td>447.405018</td>\n",
       "      <td>250.955453</td>\n",
       "      <td>58363.509985</td>\n",
       "      <td>...</td>\n",
       "      <td>1294.089606</td>\n",
       "      <td>7.475663</td>\n",
       "      <td>12.588282</td>\n",
       "      <td>11.641904</td>\n",
       "      <td>9.697070</td>\n",
       "      <td>4.702780</td>\n",
       "      <td>3.708737</td>\n",
       "      <td>6.387424</td>\n",
       "      <td>0.150596</td>\n",
       "      <td>76964.245851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>40.776325</td>\n",
       "      <td>-73.967005</td>\n",
       "      <td>38.932891</td>\n",
       "      <td>1555.629213</td>\n",
       "      <td>888.312564</td>\n",
       "      <td>229.593463</td>\n",
       "      <td>5.922370</td>\n",
       "      <td>263.369765</td>\n",
       "      <td>401.961185</td>\n",
       "      <td>92582.541369</td>\n",
       "      <td>...</td>\n",
       "      <td>1555.629213</td>\n",
       "      <td>9.112200</td>\n",
       "      <td>15.564234</td>\n",
       "      <td>5.022380</td>\n",
       "      <td>3.911630</td>\n",
       "      <td>13.006015</td>\n",
       "      <td>6.904280</td>\n",
       "      <td>4.586874</td>\n",
       "      <td>0.101553</td>\n",
       "      <td>47228.965636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>40.721401</td>\n",
       "      <td>-73.830334</td>\n",
       "      <td>39.362569</td>\n",
       "      <td>1388.333743</td>\n",
       "      <td>560.627535</td>\n",
       "      <td>254.878304</td>\n",
       "      <td>5.252612</td>\n",
       "      <td>273.805163</td>\n",
       "      <td>389.372465</td>\n",
       "      <td>65806.046097</td>\n",
       "      <td>...</td>\n",
       "      <td>1388.333743</td>\n",
       "      <td>8.321015</td>\n",
       "      <td>14.281511</td>\n",
       "      <td>13.611428</td>\n",
       "      <td>10.530144</td>\n",
       "      <td>2.813793</td>\n",
       "      <td>3.443814</td>\n",
       "      <td>7.023623</td>\n",
       "      <td>0.180753</td>\n",
       "      <td>135245.781653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>40.590772</td>\n",
       "      <td>-74.134036</td>\n",
       "      <td>40.632056</td>\n",
       "      <td>1571.477352</td>\n",
       "      <td>1206.815331</td>\n",
       "      <td>139.191638</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>154.519164</td>\n",
       "      <td>276.905923</td>\n",
       "      <td>79051.554007</td>\n",
       "      <td>...</td>\n",
       "      <td>1571.477352</td>\n",
       "      <td>8.743065</td>\n",
       "      <td>15.122779</td>\n",
       "      <td>14.503721</td>\n",
       "      <td>9.411207</td>\n",
       "      <td>1.185398</td>\n",
       "      <td>3.634816</td>\n",
       "      <td>3.160043</td>\n",
       "      <td>0.292687</td>\n",
       "      <td>453386.214238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                INTPTLAT   INTPTLON  Median_age   Population   WhiteAlone  \\\n",
       "boro_name                                                                   \n",
       "Bronx          40.851021 -73.881231   34.973161  1318.750497   269.713718   \n",
       "Brooklyn       40.651654 -73.955784   35.871941  1294.089606   560.923195   \n",
       "Manhattan      40.776325 -73.967005   38.932891  1555.629213   888.312564   \n",
       "Queens         40.721401 -73.830334   39.362569  1388.333743   560.627535   \n",
       "Staten Island  40.590772 -74.134036   40.632056  1571.477352  1206.815331   \n",
       "\n",
       "               BlackAlone  NativeAlone  BlackAlonWithComb  Hisp_Latino  \\\n",
       "boro_name                                                                \n",
       "Bronx          437.056660     7.479125         466.890656   731.420477   \n",
       "Brooklyn       431.806964     4.263697         447.405018   250.955453   \n",
       "Manhattan      229.593463     5.922370         263.369765   401.961185   \n",
       "Queens         254.878304     5.252612         273.805163   389.372465   \n",
       "Staten Island  139.191638     3.571429         154.519164   276.905923   \n",
       "\n",
       "               MedHouseholdIncome  ...  TotPopulation  pct_fem65Over  \\\n",
       "boro_name                          ...                                 \n",
       "Bronx                41625.888668  ...    1318.750497       7.266134   \n",
       "Brooklyn             58363.509985  ...    1294.089606       7.475663   \n",
       "Manhattan            92582.541369  ...    1555.629213       9.112200   \n",
       "Queens               65806.046097  ...    1388.333743       8.321015   \n",
       "Staten Island        79051.554007  ...    1571.477352       8.743065   \n",
       "\n",
       "               pct_popOver65  pctCommute60Over  pctCommutePTnsitOver60  \\\n",
       "boro_name                                                                \n",
       "Bronx              11.918698         12.724378               11.147376   \n",
       "Brooklyn           12.588282         11.641904                9.697070   \n",
       "Manhattan          15.564234          5.022380                3.911630   \n",
       "Queens             14.281511         13.611428               10.530144   \n",
       "Staten Island      15.122779         14.503721                9.411207   \n",
       "\n",
       "               pctCommuteWalkBike  pct_LivingAlone65Over  \\\n",
       "boro_name                                                  \n",
       "Bronx                    3.161765               3.960107   \n",
       "Brooklyn                 4.702780               3.708737   \n",
       "Manhattan               13.006015               6.904280   \n",
       "Queens                   2.813793               3.443814   \n",
       "Staten Island            1.185398               3.634816   \n",
       "\n",
       "               pct_LessThanHighSchool  pctMobileHomes  BlcokG_Area_SqMeter  \n",
       "boro_name                                                                   \n",
       "Bronx                        9.119549        0.146485         76409.891223  \n",
       "Brooklyn                     6.387424        0.150596         76964.245851  \n",
       "Manhattan                    4.586874        0.101553         47228.965636  \n",
       "Queens                       7.023623        0.180753        135245.781653  \n",
       "Staten Island                3.160043        0.292687        453386.214238  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('boro_name').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>MedHouseholdIncome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boro_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>41625.888668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>58363.509985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>92582.541369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>65806.046097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>79051.554007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MedHouseholdIncome\n",
       "boro_name                        \n",
       "Bronx                41625.888668\n",
       "Brooklyn             58363.509985\n",
       "Manhattan            92582.541369\n",
       "Queens               65806.046097\n",
       "Staten Island        79051.554007"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['MedHouseholdIncome','boro_name']].groupby('boro_name').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boro_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>1326663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>2527357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>1522961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>2258819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>451014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Population\n",
       "boro_name                \n",
       "Bronx             1326663\n",
       "Brooklyn          2527357\n",
       "Manhattan         1522961\n",
       "Queens            2258819\n",
       "Staten Island      451014"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Population','boro_name']].groupby('boro_name').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "boro_name\n",
       "Bronx            656.743731\n",
       "Brooklyn         530.850782\n",
       "Manhattan        786.337681\n",
       "Queens           610.934183\n",
       "Staten Island    663.999609\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('boro_name')['Population'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "boro_name\n",
       "Bronx            35482\n",
       "Brooklyn         52105\n",
       "Manhattan        84688\n",
       "Queens           63113\n",
       "Staten Island    79031\n",
       "Name: MedHouseholdIncome, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('boro_name')['MedHouseholdIncome'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "boro_name\n",
       "Bronx             74419.0\n",
       "Brooklyn          98491.6\n",
       "Manhattan        163984.6\n",
       "Queens            94108.2\n",
       "Staten Island    105679.4\n",
       "Name: MedHouseholdIncome, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('boro_name')['MedHouseholdIncome'].quantile(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">MedHouseholdIncome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boro_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>41625.888668</td>\n",
       "      <td>1006</td>\n",
       "      <td>35482</td>\n",
       "      <td>23912.188296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>58363.509985</td>\n",
       "      <td>1953</td>\n",
       "      <td>52105</td>\n",
       "      <td>29954.121614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>92582.541369</td>\n",
       "      <td>979</td>\n",
       "      <td>84688</td>\n",
       "      <td>55555.622138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>65806.046097</td>\n",
       "      <td>1627</td>\n",
       "      <td>63113</td>\n",
       "      <td>22976.800453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>79051.554007</td>\n",
       "      <td>287</td>\n",
       "      <td>79031</td>\n",
       "      <td>26019.553548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MedHouseholdIncome                           \n",
       "                            mean count median           std\n",
       "boro_name                                                  \n",
       "Bronx               41625.888668  1006  35482  23912.188296\n",
       "Brooklyn            58363.509985  1953  52105  29954.121614\n",
       "Manhattan           92582.541369   979  84688  55555.622138\n",
       "Queens              65806.046097  1627  63113  22976.800453\n",
       "Staten Island       79051.554007   287  79031  26019.553548"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['MedHouseholdIncome','boro_name']].groupby('boro_name').agg(['mean','count','median','std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "grdf =df.groupby(['boro_name', 'NearMajorParks'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "boro_name      NearMajorParks\n",
       "Bronx          NO                35313.0\n",
       "               Yes               35781.0\n",
       "Brooklyn       NO                50928.0\n",
       "               Yes               64063.0\n",
       "Manhattan      NO                92986.0\n",
       "               Yes               68587.0\n",
       "Queens         NO                61518.5\n",
       "               Yes               71653.0\n",
       "Staten Island  NO                74034.0\n",
       "               Yes               84024.5\n",
       "Name: MedHouseholdIncome, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grdf['MedHouseholdIncome'].agg('median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>boro_name</th>\n",
       "      <th>NearMajorParks</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Bronx</th>\n",
       "      <th>NO</th>\n",
       "      <td>41055.563135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>42865.492114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Brooklyn</th>\n",
       "      <th>NO</th>\n",
       "      <td>56196.329840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>68983.347432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Manhattan</th>\n",
       "      <th>NO</th>\n",
       "      <td>97277.688925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>84684.402740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Queens</th>\n",
       "      <th>NO</th>\n",
       "      <td>63963.137558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>72967.378378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Staten Island</th>\n",
       "      <th>NO</th>\n",
       "      <td>73733.120567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>84187.849315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              MedHouseholdIncome\n",
       "boro_name     NearMajorParks                    \n",
       "Bronx         NO                    41055.563135\n",
       "              Yes                   42865.492114\n",
       "Brooklyn      NO                    56196.329840\n",
       "              Yes                   68983.347432\n",
       "Manhattan     NO                    97277.688925\n",
       "              Yes                   84684.402740\n",
       "Queens        NO                    63963.137558\n",
       "              Yes                   72967.378378\n",
       "Staten Island NO                    73733.120567\n",
       "              Yes                   84187.849315"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['boro_name','NearMajorParks','MedHouseholdIncome']].groupby(['boro_name','NearMajorParks']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">MedHouseholdIncome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boro_name</th>\n",
       "      <th>NearMajorParks</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Bronx</th>\n",
       "      <th>NO</th>\n",
       "      <td>41055.563135</td>\n",
       "      <td>689</td>\n",
       "      <td>23187.920444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>42865.492114</td>\n",
       "      <td>317</td>\n",
       "      <td>25409.113980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Brooklyn</th>\n",
       "      <th>NO</th>\n",
       "      <td>56196.329840</td>\n",
       "      <td>1622</td>\n",
       "      <td>28017.966769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>68983.347432</td>\n",
       "      <td>331</td>\n",
       "      <td>36264.469019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Manhattan</th>\n",
       "      <th>NO</th>\n",
       "      <td>97277.688925</td>\n",
       "      <td>614</td>\n",
       "      <td>53670.613460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>84684.402740</td>\n",
       "      <td>365</td>\n",
       "      <td>57809.220331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Queens</th>\n",
       "      <th>NO</th>\n",
       "      <td>63963.137558</td>\n",
       "      <td>1294</td>\n",
       "      <td>21068.648093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>72967.378378</td>\n",
       "      <td>333</td>\n",
       "      <td>28145.486869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Staten Island</th>\n",
       "      <th>NO</th>\n",
       "      <td>73733.120567</td>\n",
       "      <td>141</td>\n",
       "      <td>26800.021206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>84187.849315</td>\n",
       "      <td>146</td>\n",
       "      <td>24244.894059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             MedHouseholdIncome                    \n",
       "                                           mean count           std\n",
       "boro_name     NearMajorParks                                       \n",
       "Bronx         NO                   41055.563135   689  23187.920444\n",
       "              Yes                  42865.492114   317  25409.113980\n",
       "Brooklyn      NO                   56196.329840  1622  28017.966769\n",
       "              Yes                  68983.347432   331  36264.469019\n",
       "Manhattan     NO                   97277.688925   614  53670.613460\n",
       "              Yes                  84684.402740   365  57809.220331\n",
       "Queens        NO                   63963.137558  1294  21068.648093\n",
       "              Yes                  72967.378378   333  28145.486869\n",
       "Staten Island NO                   73733.120567   141  26800.021206\n",
       "              Yes                  84187.849315   146  24244.894059"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['boro_name','NearMajorParks','MedHouseholdIncome']].groupby(['boro_name','NearMajorParks']).agg(['mean','count','std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>MedHouseholdIncome</th>\n",
       "      <th>Population</th>\n",
       "      <th>MedianValue</th>\n",
       "      <th>HsngDenPerHac</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boro_name</th>\n",
       "      <th>NearMajorParks</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Bronx</th>\n",
       "      <th>NO</th>\n",
       "      <td>41055.563135</td>\n",
       "      <td>900860</td>\n",
       "      <td>3.830960e+05</td>\n",
       "      <td>103.705700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>42865.492114</td>\n",
       "      <td>425803</td>\n",
       "      <td>3.787795e+05</td>\n",
       "      <td>87.265170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Brooklyn</th>\n",
       "      <th>NO</th>\n",
       "      <td>56196.329840</td>\n",
       "      <td>2068260</td>\n",
       "      <td>6.824072e+05</td>\n",
       "      <td>80.646695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>68983.347432</td>\n",
       "      <td>459097</td>\n",
       "      <td>6.744608e+05</td>\n",
       "      <td>76.060460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Manhattan</th>\n",
       "      <th>NO</th>\n",
       "      <td>97277.688925</td>\n",
       "      <td>939154</td>\n",
       "      <td>1.020486e+06</td>\n",
       "      <td>226.061550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>84684.402740</td>\n",
       "      <td>583807</td>\n",
       "      <td>1.132584e+06</td>\n",
       "      <td>217.282400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Queens</th>\n",
       "      <th>NO</th>\n",
       "      <td>63963.137558</td>\n",
       "      <td>1808824</td>\n",
       "      <td>5.007435e+05</td>\n",
       "      <td>45.515580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>72967.378378</td>\n",
       "      <td>449995</td>\n",
       "      <td>5.231071e+05</td>\n",
       "      <td>32.747210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Staten Island</th>\n",
       "      <th>NO</th>\n",
       "      <td>73733.120567</td>\n",
       "      <td>202118</td>\n",
       "      <td>4.230341e+05</td>\n",
       "      <td>22.947500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>84187.849315</td>\n",
       "      <td>248896</td>\n",
       "      <td>4.808168e+05</td>\n",
       "      <td>17.461895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              MedHouseholdIncome  Population   MedianValue  \\\n",
       "boro_name     NearMajorParks                                                 \n",
       "Bronx         NO                    41055.563135      900860  3.830960e+05   \n",
       "              Yes                   42865.492114      425803  3.787795e+05   \n",
       "Brooklyn      NO                    56196.329840     2068260  6.824072e+05   \n",
       "              Yes                   68983.347432      459097  6.744608e+05   \n",
       "Manhattan     NO                    97277.688925      939154  1.020486e+06   \n",
       "              Yes                   84684.402740      583807  1.132584e+06   \n",
       "Queens        NO                    63963.137558     1808824  5.007435e+05   \n",
       "              Yes                   72967.378378      449995  5.231071e+05   \n",
       "Staten Island NO                    73733.120567      202118  4.230341e+05   \n",
       "              Yes                   84187.849315      248896  4.808168e+05   \n",
       "\n",
       "                              HsngDenPerHac  \n",
       "boro_name     NearMajorParks                 \n",
       "Bronx         NO                 103.705700  \n",
       "              Yes                 87.265170  \n",
       "Brooklyn      NO                  80.646695  \n",
       "              Yes                 76.060460  \n",
       "Manhattan     NO                 226.061550  \n",
       "              Yes                217.282400  \n",
       "Queens        NO                  45.515580  \n",
       "              Yes                 32.747210  \n",
       "Staten Island NO                  22.947500  \n",
       "              Yes                 17.461895  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['boro_name','NearMajorParks']).agg({'MedHouseholdIncome':'mean','Population':'sum','MedianValue':'mean','HsngDenPerHac':'median'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>boro_name</th>\n",
       "      <th>NearMajorParks</th>\n",
       "      <th>MedHouseholdIncome</th>\n",
       "      <th>Population</th>\n",
       "      <th>MedianValue</th>\n",
       "      <th>HsngDenPerHac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>NO</td>\n",
       "      <td>41055.563135</td>\n",
       "      <td>900860</td>\n",
       "      <td>3.830960e+05</td>\n",
       "      <td>103.705700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Yes</td>\n",
       "      <td>42865.492114</td>\n",
       "      <td>425803</td>\n",
       "      <td>3.787795e+05</td>\n",
       "      <td>87.265170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NO</td>\n",
       "      <td>56196.329840</td>\n",
       "      <td>2068260</td>\n",
       "      <td>6.824072e+05</td>\n",
       "      <td>80.646695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Yes</td>\n",
       "      <td>68983.347432</td>\n",
       "      <td>459097</td>\n",
       "      <td>6.744608e+05</td>\n",
       "      <td>76.060460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NO</td>\n",
       "      <td>97277.688925</td>\n",
       "      <td>939154</td>\n",
       "      <td>1.020486e+06</td>\n",
       "      <td>226.061550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>84684.402740</td>\n",
       "      <td>583807</td>\n",
       "      <td>1.132584e+06</td>\n",
       "      <td>217.282400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Queens</td>\n",
       "      <td>NO</td>\n",
       "      <td>63963.137558</td>\n",
       "      <td>1808824</td>\n",
       "      <td>5.007435e+05</td>\n",
       "      <td>45.515580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Queens</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72967.378378</td>\n",
       "      <td>449995</td>\n",
       "      <td>5.231071e+05</td>\n",
       "      <td>32.747210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Staten Island</td>\n",
       "      <td>NO</td>\n",
       "      <td>73733.120567</td>\n",
       "      <td>202118</td>\n",
       "      <td>4.230341e+05</td>\n",
       "      <td>22.947500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Yes</td>\n",
       "      <td>84187.849315</td>\n",
       "      <td>248896</td>\n",
       "      <td>4.808168e+05</td>\n",
       "      <td>17.461895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       boro_name NearMajorParks  MedHouseholdIncome  Population   MedianValue  \\\n",
       "0          Bronx             NO        41055.563135      900860  3.830960e+05   \n",
       "1          Bronx            Yes        42865.492114      425803  3.787795e+05   \n",
       "2       Brooklyn             NO        56196.329840     2068260  6.824072e+05   \n",
       "3       Brooklyn            Yes        68983.347432      459097  6.744608e+05   \n",
       "4      Manhattan             NO        97277.688925      939154  1.020486e+06   \n",
       "5      Manhattan            Yes        84684.402740      583807  1.132584e+06   \n",
       "6         Queens             NO        63963.137558     1808824  5.007435e+05   \n",
       "7         Queens            Yes        72967.378378      449995  5.231071e+05   \n",
       "8  Staten Island             NO        73733.120567      202118  4.230341e+05   \n",
       "9  Staten Island            Yes        84187.849315      248896  4.808168e+05   \n",
       "\n",
       "   HsngDenPerHac  \n",
       "0     103.705700  \n",
       "1      87.265170  \n",
       "2      80.646695  \n",
       "3      76.060460  \n",
       "4     226.061550  \n",
       "5     217.282400  \n",
       "6      45.515580  \n",
       "7      32.747210  \n",
       "8      22.947500  \n",
       "9      17.461895  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['boro_name','NearMajorParks']).agg({'MedHouseholdIncome':'mean','Population':'sum','MedianValue':'mean','HsngDenPerHac':'median'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfgroups = df.groupby(['boro_name','NearMajorParks']).agg({'MedHouseholdIncome':'mean','Population':'sum','MedianValue':'mean','HsngDenPerHac':'median'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfgroups = dfgroups.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro_name</th>\n",
       "      <th>NearMajorParks</th>\n",
       "      <th>MedHouseholdIncome</th>\n",
       "      <th>Population</th>\n",
       "      <th>MedianValue</th>\n",
       "      <th>HsngDenPerHac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>NO</td>\n",
       "      <td>41055.563135</td>\n",
       "      <td>900860</td>\n",
       "      <td>3.830960e+05</td>\n",
       "      <td>103.705700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Yes</td>\n",
       "      <td>42865.492114</td>\n",
       "      <td>425803</td>\n",
       "      <td>3.787795e+05</td>\n",
       "      <td>87.265170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NO</td>\n",
       "      <td>56196.329840</td>\n",
       "      <td>2068260</td>\n",
       "      <td>6.824072e+05</td>\n",
       "      <td>80.646695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Yes</td>\n",
       "      <td>68983.347432</td>\n",
       "      <td>459097</td>\n",
       "      <td>6.744608e+05</td>\n",
       "      <td>76.060460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NO</td>\n",
       "      <td>97277.688925</td>\n",
       "      <td>939154</td>\n",
       "      <td>1.020486e+06</td>\n",
       "      <td>226.061550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>84684.402740</td>\n",
       "      <td>583807</td>\n",
       "      <td>1.132584e+06</td>\n",
       "      <td>217.282400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Queens</td>\n",
       "      <td>NO</td>\n",
       "      <td>63963.137558</td>\n",
       "      <td>1808824</td>\n",
       "      <td>5.007435e+05</td>\n",
       "      <td>45.515580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Queens</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72967.378378</td>\n",
       "      <td>449995</td>\n",
       "      <td>5.231071e+05</td>\n",
       "      <td>32.747210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Staten Island</td>\n",
       "      <td>NO</td>\n",
       "      <td>73733.120567</td>\n",
       "      <td>202118</td>\n",
       "      <td>4.230341e+05</td>\n",
       "      <td>22.947500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Yes</td>\n",
       "      <td>84187.849315</td>\n",
       "      <td>248896</td>\n",
       "      <td>4.808168e+05</td>\n",
       "      <td>17.461895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       boro_name NearMajorParks  MedHouseholdIncome  Population   MedianValue  \\\n",
       "0          Bronx             NO        41055.563135      900860  3.830960e+05   \n",
       "1          Bronx            Yes        42865.492114      425803  3.787795e+05   \n",
       "2       Brooklyn             NO        56196.329840     2068260  6.824072e+05   \n",
       "3       Brooklyn            Yes        68983.347432      459097  6.744608e+05   \n",
       "4      Manhattan             NO        97277.688925      939154  1.020486e+06   \n",
       "5      Manhattan            Yes        84684.402740      583807  1.132584e+06   \n",
       "6         Queens             NO        63963.137558     1808824  5.007435e+05   \n",
       "7         Queens            Yes        72967.378378      449995  5.231071e+05   \n",
       "8  Staten Island             NO        73733.120567      202118  4.230341e+05   \n",
       "9  Staten Island            Yes        84187.849315      248896  4.808168e+05   \n",
       "\n",
       "   HsngDenPerHac  \n",
       "0     103.705700  \n",
       "1      87.265170  \n",
       "2      80.646695  \n",
       "3      76.060460  \n",
       "4     226.061550  \n",
       "5     217.282400  \n",
       "6      45.515580  \n",
       "7      32.747210  \n",
       "8      22.947500  \n",
       "9      17.461895  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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