T-test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the path on your disk\n",
    "pathName = r\"C:\\MyFiles\\Dropbox\\Teaching\\Urban_Data_Analsysis\\Spring 2021\\Data\\CSV_BlockGroups.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'C:\\\\MyFiles\\\\Dropbox\\\\Teaching\\\\Urban_Data_Analsysis\\\\Spring 2021\\\\Data\\\\CSV_BlockGroups.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-d0782cc53a54>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# read the csv as a dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpathName\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\.conda\\envs\\UrbanDataAnalysis\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    684\u001b[0m     )\n\u001b[0;32m    685\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 686\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\UrbanDataAnalysis\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    451\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 452\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    453\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\UrbanDataAnalysis\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    934\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    935\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 936\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    938\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\UrbanDataAnalysis\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1166\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1167\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1168\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1169\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1170\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\UrbanDataAnalysis\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1996\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1997\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1998\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1999\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2000\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\MyFiles\\\\Dropbox\\\\Teaching\\\\Urban_Data_Analsysis\\\\Spring 2021\\\\Data\\\\CSV_BlockGroups.csv'"
     ]
    }
   ],
   "source": [
    "# read the csv as a dataframe\n",
    "df = pd.read_csv(pathName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-9e112543b788>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        <Null>\n",
       "1         60800\n",
       "2        298200\n",
       "3        452900\n",
       "4        440800\n",
       "         ...   \n",
       "5847     598100\n",
       "5848     <Null>\n",
       "5849    1273800\n",
       "5850     163800\n",
       "5851     331600\n",
       "Name: MedianValue, Length: 5852, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MedianValue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MedianValue'] = pd.to_numeric(df['MedianValue'], errors='coerce')\n",
    "df.replace([np.inf, -np.inf,0], np.nan)\n",
    "# drop nan values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "621488.1213235294"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MedianValue'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T test\n",
    "### The differences between two groups (we need one categorical variable and one numerical variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the unique values of a categorical variable\n",
    "df['WaterFront'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NO', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NearMajorParks'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's have a simple boxplot first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the size\n",
    "fig = plt.gcf()\n",
    "# Changing Seaborn Plot size\n",
    "fig.set_size_inches(5, 8)\n",
    "\n",
    "\n",
    "# plot\n",
    "sns.boxplot(data=df, x='NearMajorParks', y='MedianValue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the size\n",
    "fig = plt.gcf()\n",
    "# Changing Seaborn Plot size\n",
    "fig.set_size_inches(5, 8)\n",
    "\n",
    "\n",
    "# plot\n",
    "sns.boxplot(data=df, x='WaterFront', y='MedianValue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MedianValue', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x=\"MedianValue\",bins=60,hue='WaterFront')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfQueens = df.loc[df['boro_name']=='Queens']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MedianValue', ylabel='Count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=dfQueens, x=\"MedianValue\",bins=30,hue='NearMajorParks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MedianValue', ylabel='Count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgOklEQVR4nO3de5gU1bnv8e8LInCQGLmIhJkBTBABg4oTEsHsBzUBjiagUWA8qCgkROMNYUdFNxETJ9FEjTHqBuIFEomIG7ODGI2Kt0OCwnCRa4gcFGjgQWTHu4gD7/mjirJnmEsN09XdM/P7PM883b1qrep3mjW8vVZVrTJ3R0REBKBZrgMQEZH8oaQgIiIRJQUREYkoKYiISERJQUREIoflOoD66NChg3fr1i3XYYiINCjLli17x907VrWtQSeFbt26UVZWluswREQaFDPbXN02TR+JiEhESUFERCJKCiIiEmnQxxREpPH47LPPSKVS7NmzJ9ehNBqtWrWioKCAFi1axG6jpCAieSGVStG2bVu6deuGmeU6nAbP3dm9ezepVIru3bvHbqfpIxHJC3v27KF9+/ZKCBliZrRv377OIy8lBRHJG0oImXUon6eSgoiIRJQURCRvmRmTJk2KXt9xxx1MnTo1I/t+6623MDOmTJkSlb3zzju0aNGCK6+8ssa28+fP57bbbqvT+82cOZOOHTty0kkn0bt3b373u9/Vqf2gQYOycrGukkITUljUFTOL9VNY1DXX4YrQsmVLnnjiCd55552M7re8vByAY489lgULFkTljz/+OH369Km1/bBhw7jhhhvq/H6jRo1i5cqVvPTSS9x4443s3LkzVvt9+/bFfq/60tlHTUhq6xbuenZDrLoTB/dMOBqR2h122GGMHz+eX//615SWllbYtmvXLi677DK2bNkCwN13383AgQNZsmQJEyZM4JNPPqF169Y8/PDD9OzZk5kzZ/LUU0+xZ88ePvroIx566CFat25Nr169KCsro7i4mMcee4yRI0eyfft2AJ588kluvfVW9u7dS/v27Zk9ezadOnVi5syZlJWVce+997J582bGjh3Lrl276NixIw8//DBFRUVccskltGvXjhUrVtCvXz+++tWvRrEfffTRfPnLX2bz5s1MnTqVpUuX8sknn3D++edzyy23AMEyPmPHjuXZZ5+tMHLZv38/l156KYWFhdxyyy2MGzeOsrIyzIyxY8dy7bXX1usz10hBRPLaFVdcwezZs3nvvfcqlF9zzTVce+21LF26lHnz5vH9738fgOOPP55XXnmFFStW8NOf/pQbb7wxarN48WJmzZrFCy+8EJWVlJQwZ84cUqkUzZs350tf+lK07bTTTuPVV19lxYoVlJSU8Mtf/vKg+K688kouvvhiVq1axejRo7n66qujbf/85z95/vnnufPOOyu02bRpE5s2beIrX/kKpaWllJWVsWrVKl5++WVWrVoV1WvVqhWLFi2ipKQECEYco0eP5rjjjuPWW29l5cqVbNu2jTVr1rB69WouvfTSQ/mIK9BIQUTy2he+8AUuvvhi7rnnHlq3bh2VP//886xbty56/f777/PBBx/w3nvvMWbMGN544w3MjM8++yyq8+1vf5t27dpV2P/QoUOZMmUKnTp1YtSoURW2pVIpRo0axY4dO9i7d2+V5/svXryYJ554AoCLLrqI6667Lto2YsQImjdvHr1+7LHHWLRoES1btmT69Om0a9eOadOmMWPGDMrLy9mxYwfr1q2jb9++AAfF88Mf/pCRI0dy0003AcH016ZNm7jqqqs4++yzGTx4cLwPtQYaKYhI3pswYQIPPvggH330UVS2f/9+Fi9ezMqVK6NvzG3btmXKlCmcfvrprFmzhieffLLCefpt2rQ5aN+HH344p5xyCnfeeSfnnXdehW1XXXUVV155JatXr2b69OmxzvlPPw208vsdOKbw2muvce655/Lmm29yxx13sHDhQlatWsXZZ59dY7wDBgzgxRdfjOocddRRvP766wwaNIj77rsvGi3Vh5KCiOS9du3aMXLkSB588MGobPDgwdx7773R65UrVwLw3nvv0aVLFyA44yeOSZMmcfvtt9O+ffsK5en7mjVrVpVtBwwYwJw5cwCYPXs2p512Wqz3hGB006ZNG4488kh27tzJ008/XWP9cePGcdZZZzFixAjKy8t555132L9/P+eddx4/+9nPWL58eez3ro6Sgog0CJMmTapwFtI999xDWVkZffv2pXfv3kybNg2A6667jsmTJzNw4MDYZ+306dOHMWPGHFQ+depURowYwTe/+U06dOhQYduBEcE999zDww8/TN++ffnDH/7Ab37zm9i/04knnsjJJ59Mnz59GDt2LAMHDqy1zcSJE+nXrx8XXXQR27ZtY9CgQZx00klccskl/OIXv4j93tUxd6/3TnKluLjYdZOd+MysTmcfNeS+IQ3P+vXr6dWrV67DiOXOO+/k/fffj84UymdVfa5mtszdi6uqrwPNIiJ1MG3aNGbOnBkdXG5sNH0kIlIHl112GatXr6ZHjx65DiURSgoiIhJRUhARkYiSgoiIRJQUREQkoqQgIo1WXVYGztTqwbUt9z1jxgyOP/54jj/+ePr378+iRYuS+NUPmU5JFZFGqy4rA8cRZ/XgA8t9T548+aAL3hYsWMD06dNZtGgRHTp0YPny5ZxzzjksWbKEY445JmNx1odGCiIiGZS+3Hdlt99+O7/61a+iZNGvXz/GjBnDfffdl+0wq6WkICKSYdUt97127VpOOeWUCmXFxcWsXbs2m+HVSElBRCTD0pf7ro27V1hZNdeUFEREElDVct+9e/dm2bJlFeotX76c3r17Zzu8aiWaFMzsLTNbbWYrzawsLGtnZs+Z2Rvh41Fp9Seb2UYz22BmQ5KMTUQkSVUt933ddddx/fXXs3v3biBY7nvmzJn86Ec/ylWYB8nG2Uenu3v6XbdvABa6+21mdkP4+noz6w2UAH2ALwHPm9lx7p69O1aLSKNSUFiU0fuNFxQW1an+pEmTKtzzYdiwYWzbto0BAwZgZrRt25ZHHnmEzp07ZyzG+srFKanDgUHh81nAS8D1Yfkcd/8UeNPMNgL9gcU5iFFEGoGtWzZn/T0//PDD6HmnTp34+OOPK2y//PLLufzyy7MdVmxJH1Nw4FkzW2Zm48OyTu6+AyB8PDos7wJsTWubCssqMLPxZlZmZmW7du1KMHQRkaYn6ZHCQHffbmZHA8+Z2T9qqFvV4feD7vLi7jOAGRDcZCczYYqICCQ8UnD37eHj28CfCKaDdppZZ4Dw8e2wegooTGteAGxPMj4REakosaRgZm3MrO2B58BgYA0wHzhwM9QxwJ/D5/OBEjNraWbdgR7AkqTiExGRgyU5fdQJ+FN4UcZhwB/d/RkzWwrMNbNxwBZgBIC7rzWzucA6oBy4QmceiYhkV2JJwd03ASdWUb4bOLOaNqVAaVIxiYhIzXRFs4g0Wt2KCjK6dHa3ooIa38/dOe2003j66aejsrlz5zJ06NCkf9WM0dLZItJobd66DX/h5xnbn51xY83bzZg2bRojRozg9NNPZ9++fdx0000888wzGYshaUoKIiIZdMIJJ/Dd736X22+/nY8++ogLL7yQ0tJSVq9eTXl5OVOnTmX48OGsXbuWSy+9lL1797J//37mzZtHjx49ch2+koKISKbdfPPN9OvXj8MPP5zvfOc7nHHGGTz00EO8++679O/fn29961tMmzaNa665htGjR7N371727cuP82qUFEREMqxNmzaMGjWKI444grlz5/Lkk09yxx13ALBnzx62bNnCqaeeSmlpKalUiu9973t5MUoAJQURkUQ0a9aMZs2a4e7MmzePnj0rLszXq1cvvv71r/PUU08xZMgQHnjgAc4444wcRfs5nX0kIpKgIUOG8Nvf/hb3YFWeFStWALBp0yaOPfZYrr76aoYNG8aqVatyGWZEIwURabS6Fnap9Yyhuu6vrqZMmcKECRPo27cv7k63bt1YsGABjz32GI888ggtWrTgmGOO4Sc/+UnG4qwPO5C9GqLi4mIvKyvLdRgNhplx17MbYtWdOLgnDblvSMOzfv16evXqleswGp2qPlczW+buxVXV1/SRiIhElBRERCSipCAieUNTlpl1KJ+nkoKI5IVWrVqxe/duJYYMcXd2795Nq1at6tROZx+JSF4oKCgglUqh2+xmTqtWrSgoqHkRv8qUFEQkL7Ro0YLu3bvnOowmT9NHIiISUVIQEZGIkoKIiESUFEREJKKkICIiESUFERGJKCk0AoVFXWPddFxEpDa6TqERSG3dEmv104mDe9ZaR0SaNo0UREQkoqQgIiIRJQUREYkoKYiISERJQUREIoknBTNrbmYrzGxB+LqdmT1nZm+Ej0el1Z1sZhvNbIOZDUk6NhERqSgbI4VrgPVpr28AFrp7D2Bh+Boz6w2UAH2AocD9ZtY8C/GJiEgo0aRgZgXA2cADacXDgVnh81nAOWnlc9z9U3d/E9gI9E8yPhERqSjpkcLdwHXA/rSyTu6+AyB8PDos7wJsTauXCssqMLPxZlZmZmW6Q5OISGYllhTM7DvA2+6+LG6TKsoOulmru89w92J3L+7YsWO9YhQRkYqSXOZiIDDMzM4CWgFfMLNHgJ1m1tndd5hZZ+DtsH4KKExrXwBsTzA+ERGpJLGRgrtPdvcCd+9GcAD5BXe/EJgPjAmrjQH+HD6fD5SYWUsz6w70AJYkFZ+IiBwsFwvi3QbMNbNxwBZgBIC7rzWzucA6oBy4wt335SA+EZEmKytJwd1fAl4Kn+8GzqymXilQmo2YRETkYLqiWUREIkoKIiISUVIQEZGIkoKIiESUFEREJKKkICIiESUFERGJKCmIiEhESUFERCJKCiIiElFSEBGRiJKCiIhElBRERCSipCAiIhElBamXwqKumFmtP4VFXXMdqojEkIub7Egjktq6hbue3VBrvYlDemFW1W24KyooLGLrls2ZCE1EDoGSgmSH74+XPAb3zEIwIlIdTR+JiEhESUFERCJKCiIiEomVFMxsYJwyERFp2OKOFH4bs0xERBqwGs8+MrNTgQFARzObmLbpC0DzJAMTEZHsq+2U1MOBI8J6bdPK3wfOTyooERHJjRqTgru/DLxsZjPdXVcUiYg0cnEvXmtpZjOAbult3P2MJIKSPGDNYl2BLCKNS9yk8DgwDXgA2JdcOJI3dAWySJMUNymUu/t/1mXHZtYKeAVoGb7Pf7n7zWbWDniMYNTxFjDS3f8VtpkMjCNIPFe7+1/r8p4iIlI/cU9JfdLMfmRmnc2s3YGfWtp8Cpzh7icCJwFDzewbwA3AQnfvASwMX2NmvYESoA8wFLjfzHSGk4hIFsUdKYwJH3+cVubAsdU1cHcHPgxftgh/HBgODArLZwEvAdeH5XPc/VPgTTPbCPQHFseMUURE6ilWUnD37oey8/Cb/jLgK8B97v6amXVy9x3hfneY2dFh9S7Aq2nNU2FZ5X2OB8YDFBUVHUpYIiJSjVhJwcwurqrc3X9fUzt33wecZGZfBP5kZifU9DZV7aKKfc4AZgAUFxcftF1ERA5d3Omjr6U9bwWcCSwHakwKB7j7u2b2EsGxgp1m1jkcJXQG3g6rpYDCtGYFwPaY8YmISAbEOtDs7lel/fwAOJngaudqmVnHcISAmbUGvgX8A5jP58coxgB/Dp/PB0rMrKWZdQd6AEvq+PuIiEg9HOqd1z4m+E+7Jp2BWeFxhWbAXHdfYGaLgblmNg7YAowAcPe1ZjYXWAeUA1eE008iIpIlcY8pPMnn8/vNgV7A3JrauPsqghFF5fLdBNNPVbUpBUrjxCQiIpkXd6RwR9rzcmCzu6cSiEdERHIo7jGFlwmOB7QFjgL2JhmUiIjkRtw7r40kOOg7AhgJvGZmWjpbRKSRiTt9dBPwNXd/G4Izi4Dngf9KKjAREcm+uGsfNTuQEEK769BWREQaiLgjhWfM7K/Ao+HrUcBfkglJRERypbZ7NH8F6OTuPzaz7wGnESxHsRiYnYX4REQki2qbArob+ADA3Z9w94nufi3BKOHuZEMTEZFsqy0pdAsvQqvA3csIbpIjIiKNSG1JoVUN21pnMhAREcm92pLCUjP7QeXCcN2iZcmEJCIiuVLb2UcTCO6DMJrPk0AxwQqp5yYYl4iI5ECNScHddwIDzOx04MANcp5y9xcSj0xERLIu7u04XwReTDgWERHJMV2VLCIiESUFERGJKCmIiEhESUFERCJKCiIiElFSEBGRiJKCiIhElBRERCSipCAiIhElBRERiSgpiIhIRElBREQiSgqSX6wZZlbrT2FR11xHKtIoxVolVSRrfD93Pbuh1moTB/fMQjAiTU9iIwUzKzSzF81svZmtNbNrwvJ2Zvacmb0RPh6V1maymW00sw1mNiSp2EREpGpJTh+VA5PcvRfwDeAKM+sN3AAsdPcewMLwNeG2EqAPMBS438yaJxifiIhUklhScPcd7r48fP4BsB7oAgwHZoXVZgHnhM+HA3Pc/VN3fxPYCPRPKj4RETlYVg40m1k34GTgNaCTu++AIHEAR4fVugBb05qlwrLK+xpvZmVmVrZr165E4xYRaWoSTwpmdgQwD5jg7u/XVLWKMj+owH2Guxe7e3HHjh0zFaaIiJBwUjCzFgQJYba7PxEW7zSzzuH2zsDbYXkKKExrXgBsTzI+ERGpKMmzjwx4EFjv7nelbZoPjAmfjwH+nFZeYmYtzaw70ANYklR8IiJysCSvUxgIXASsNrOVYdmNwG3AXDMbB2wBRgC4+1ozmwusIzhz6Qp335dgfCIiUkliScHdF1H1cQKAM6tpUwqUJhWTiIjUTMtciIhIRElBREQiSgoiIhJRUpBGrbCoq1ZdFakDrZIqjVpq6xatuipSBxopiIhIRElBREQiSgoiIhLRMYUm5pZbbsl1CCKSx5QUmpibx1R5MflBJv7tjwlHUk/hvZxFJLOUFKRh0r2cRRKhYwoiIhJRUhARkYiSgoiIRJQUREQkoqQgIiIRJQUREYkoKeSxuCt8iohkiq5TyGNa4VNEsk0jBRERiSgpiIhIRElBREQiSgoiIhLRgeZGQktii0gmKCk0EnGWxM775bBFJOc0fSQiIhElhRzQRWkikq80fZQDuihNRPJVYiMFM3vIzN42szVpZe3M7DkzeyN8PCpt22Qz22hmG8xsSFJxiYhI9ZKcPpoJDK1UdgOw0N17AAvD15hZb6AE6BO2ud/MmicYm4iIVCGxpODurwD/U6l4ODArfD4LOCetfI67f+rubwIbgf5JxSYiIlXL9oHmTu6+AyB8PDos7wJsTauXCssOYmbjzazMzMp27dqVaLAiIk1NvhxorupUG6+qorvPAGYAFBcXV1lHMiPuBXGZriciuZPtpLDTzDq7+w4z6wy8HZangMK0egXA9izHJpXEvSAu0/VEJHeyPX00HxgTPh8D/DmtvMTMWppZd6AHsCTLsYmINHmJjRTM7FFgENDBzFLAzcBtwFwzGwdsAUYAuPtaM5sLrAPKgSvcfV9SsYmISNUSSwrufkE1m6qcQ3D3UqA0qXhEamTNYl9FXlBYxNYtmxMOSCQ38uVAs0hu+f5YV5mDrjSXxk1rH4mISERJQUREIkoKIiISUVIQEZGIkoKIiESUFEREJKKkICIiESUFEZE8Evd2vYVFXRN5f128lue0sqhI05Lr2/UqKeQ5rSyah2IuiaHlMKQhUlIQqauYS2LE/SZXWNSV1NYttdZTkpFsUFLIEU0LVa+pfTa5ni4QSaekkCOaFqpe3M+mqSUPkWxQUpAGK+8Tax2W4xZJl8svPEoKGRR3bliaiAwfe5CmI5dfeJQUMkhzwyLS0OniNRERiWikII2eDkiLxKekII1e3h+QjksXzUkWKClkmL6VNlx5/2+nA9eSBUoKGdZovpU2QXH+7UD/ftK4KSmIHIK8H1WIHCIlBZFDoBGhNFZN+pTUbkUFsdYt71ZUkOtQRUSyokmPFDZv3Ya/8PNa69mZ/6HlCuSQaJpJGpomnRRi01kfcogyvbhfJpNMQ1iyuyHE2Ng07aRgzbAzbsx1FCKxk0esen+fE3tkm6svO3VZJyxWjEN6xfqdmx/Wgn3ln9VaryknmbxLCmY2FPgN0Bx4wN1vS+zNfD93zXiw1moTx4/TNIA0HHXo17HUYbXXuP+Z1mWdsFh/e3UYzWcyyTTG5JFXScHMmgP3Ad8GUsBSM5vv7utyG5nONpEmLOZ/uBD/P9O6yPTfXkaTTB1+34aSQPIqKQD9gY3uvgnAzOYAw4GcJwWRxijjxzJyOPqOu7+MHufx/Rw58P/Eet9UHab1csncPdcxRMzsfGCou38/fH0R8HV3vzKtznhgfPiyJxDvK0xFHYB36hluEhRX3eVrbIqrbvI1Lsjf2OoTV1d371jVhnwbKVSVRitkLXefAcyo15uYlbl7cX32kQTFVXf5Gpviqpt8jQvyN7ak4sq3i9dSQGHa6wJge45iERFpcvItKSwFephZdzM7HCgB5uc4JhGRJiOvpo/cvdzMrgT+SnBK6kPuvjaBt6rX9FOCFFfd5Wtsiqtu8jUuyN/YEokrrw40i4hIbuXb9JGIiOSQkoKIiEQaVVIws6FmtsHMNprZDVVsNzO7J9y+ysz6xW2bhdhGhzGtMrO/m9mJadveMrPVZrbSzMqyHNcgM3svfO+VZvaTuG0TjuvHaTGtMbN9ZtYu3Jbk5/WQmb1tZmuq2Z6TPhYjrlz1r9riykn/ihlb1vuYmRWa2Ytmtt7M1prZNVXUSbaPuXuj+CE4MP3/gGOBw4HXgd6V6pwFPE1wPcQ3gNfits1CbAOAo8Ln//tAbOHrt4AOOfrMBgELDqVtknFVqv9d4IWkP69w3/8G9APWVLM9V32striy3r9ixpX1/hU3tlz0MaAz0C983hb4Z7b/H2tMI4VoiQx33wscWCIj3XDg9x54FfiimXWO2TbR2Nz97+7+r/DlqwTXaCStPr93kp9ZXfd9AfBoht67Ru7+CvA/NVTJSR+rLa4c9a84n1d1kv6brGtsWelj7r7D3ZeHzz8A1gNdKlVLtI81pqTQBdia9jrFwR9mdXXitE06tnTjCL4JHODAs2a2zIJlPrId16lm9rqZPW1mferYNsm4MLP/BQwF5qUVJ/V5xZGrPlYX2epfcWW7f9VJrvqYmXUDTgZeq7Qp0T6WV9cp1FOtS2TUUCdO2/qIvX8zO53gj/a0tOKB7r7dzI4GnjOzf4TfcrIR13KCdVI+NLOzgP8GesRsm2RcB3wX+Ju7p3/jS+rziiNXfSyWLPevOHLRv+oq633MzI4gSEIT3P39ypuraJKxPtaYRgpxlsiork7Sy2vE2r+Z9QUeAIa7++4D5e6+PXx8G/gTwTAxK3G5+/vu/mH4/C9ACzPrEKdtknGlKaHSsD7BzyuOXPWxWuWgf9UqR/2rrrLax8ysBUFCmO3uT1RRJdk+lukDJbn6IRj1bAK68/lBlj6V6pxNxQM0S+K2zUJsRcBGYECl8jZA27TnfydYSTZbcR3D5xc59ge2hJ9fYp9Z3H0DRxLMCbfJxueV9h7dqP7AaU76WIy4st6/YsaV9f4VN7Zc9LHwd/89cHcNdRLtY41m+sirWSLDzC4Lt08D/kJw5H4j8DFwaU1tsxzbT4D2wP0WrLle7sEKiJ2AP4VlhwF/dPdnshjX+cDlZlYOfAKUeNADE/vMYsYFcC7wrLt/lNY8sc8LwMweJThjpoOZpYCbgRZpceWkj8WIK+v9K2ZcWe9fdYgNst/HBgIXAavNbGVYdiNBUs9KH9MyFyIiEmlMxxRERKSelBRERCSipCAiIhElBRERiSgpiIg0ELUt4ldF/ZFmti5cXO+PcdooKUijZmZuZn9Ie32Yme0yswV13M9LZlYcPv+LmX3xEGIZZGaLK5UdZmY7w7VrqmtTp1ilUZtJsORGrcysBzCZ4OrrPsCEOO2UFKSx+wg4wcxah6+/DWyrzw7d/Sx3f/cQmr4CFIRr2hzwLYKLp3bUJyZpGryKRfzM7Mtm9ky4DtP/NbPjw00/AO7zcCFED66+rpWSgjQFTxNcBQqVVrs0szbhkHypma0ws+FheWszmxOuV/8Y0DqtzVvhUgyY2X+Hf4xr0xdGM7MPzaw0XOjtVTPr5O77gceBUWmxlQCPmll/C+5zsCJ87Fn5lzCzqWb272mv1xxIMGZ2oZktsWB9/+lm1rzen5o0FDOAq9z9FODfgfvD8uOA48zsb2EfjDXCUFKQpmAOUGJmrYC+VFx18iaCdfK/BpwO/MrM2gCXAx+7e1+gFDilmn2PDf8Yi4Grzax9WN4GeNXdTyQYIfwgLH+UIBFgZi0JrkydB/wD+Dd3P5ng6uOfx/3lzKwXQaIZ6O4nAfuA0XHbS8MVLpw3AHg8vAJ6OsE9GSC42roHwVXbFwAPxJn2bDTLXIhUx91Xhd+oLyBYIiDdYGBY2jfwVgRLCvwbcE9a+1XV7P5qMzs3fF5I8Ee4G9gLHDgWsIxg2gp3X2pmR4QjgV4EieNfZlYIzArngZ1wuYWYziRIWkvDpRdaA7GmCqTBawa8G34ZqCxF0L8+A940sw0E/XNpTTtUUpCmYj5wB8G3pvZp5Qac5+4b0iuH/7nWuAaMmQ0iOCZwqrt/bGYvESQVgM/88zVk9lHxb20OwWihF59PZf0MeNHdzw0T2EtVvGU5FUf3B97LgFnuPrmmeKXxcff3zexNMxvh7o9b0HH7uvvrBMuQXwDMDKc7jyNYMK9Gmj6SpuIh4KfuvrpS+V+Bq8I/Jszs5LD8FcIpGDM7gWDaqbIjgX+FCeF4ghUr43gUuBA4gyBZHdjXgQPgl1TT7i2C20diwX15u4flC4HzLVjbHzNrZ2ZdY8YiDUi4iN9ioKeZpcxsHEE/HWdmrwNr+fxua38FdpvZOuBF4MeetmR6dTRSkCbB3VPAb6rY9DPgbmBVmBjeAr4D/CfwcDhttBJYUkXbZ4DLwjobCG5zGSeWdWb2MbAsbfXNXxJMH00EXqim6Tzg4nDueCnB/XsP7O8/CO4E1gz4DLgC2BwnHmk43P2CajYddBA5HKlODH9i0yqpIiIS0fSRiIhElBRERCSipCAiIhElBRERiSgpiIhIRElBREQiSgoiIhL5/9RVIhDjchw+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x=\"MedianValue\",bins=30,hue='NearMajorParks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the unique values to split the dataframe and create two separate dataframes for each group.\n",
    "df_group_1 = df.loc[df['WaterFront']=='Yes']\n",
    "df_group_2 = df.loc[df['WaterFront']=='No']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "661936.0383141763"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_1['MedianValue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "618907.6015644097"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_2['MedianValue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_1.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.9304001639782817, 0.053622200475733385, 4350.0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we need to use statmodels and pass each group (with a column) as an argument\n",
    "# the test returns:\n",
    "## 1) tstat : test statistic\n",
    "## 2) pvalue of the t-test\n",
    "## 3) degrees of freedom used in the t-test\n",
    "sm.stats.ttest_ind(df_group_1['MedianValue'],df_group_2['MedianValue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NO', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NearMajorParks'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the unique values to split the dataframe and create two separate dataframes for each group.\n",
    "df_group_1 = df.loc[df['NearMajorParks']=='Yes']\n",
    "df_group_2 = df.loc[df['NearMajorParks']=='NO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.208969005817115, 0.027228731029343572, 4350.0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we need to use statmodels and pass each group (with a column) as an argument\n",
    "# the test returns:\n",
    "## 1) tstat : test statistic\n",
    "## 2) pvalue of the t-test\n",
    "## 3) degrees of freedom used in the t-test\n",
    "sm.stats.ttest_ind(df_group_1['MedianValue'],df_group_2['MedianValue'])"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}