CS210_19_Hypt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [ New York City Airbnb Prices - Group 19]\n",
    "\n",
    "#### Group Members:\n",
    "\n",
    "\n",
    "İpek Nur Dolu - 27893\n",
    "\n",
    "Akmerey Kadyrzhanova - 28606"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First we import the libraries that are needed\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import reverse_geocoder as rg\n",
    "from geopy.distance import geodesic\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from os.path import join\n",
    "from os import listdir\n",
    "from scipy.stats import f_oneway\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import datetime\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our data is big, it takes more than 3 hours to run the data and repeat the changes we made in the first step.\n",
    "So we used the same data we had worked on and saved in the previous step of our project.\n",
    "Our manipulated data has the information of our main data as well as our additional datas such as our subway data, AOI data and crime data. After looking at the instructions we figured out that our data from first step would be useful and efficient.\n",
    "\n",
    "We can share the csv file if needed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the manipulated data from the first step of our project.\n",
    "df2 = pd.read_csv('tmp_main_data_with_prox_crime.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>number_subway</th>\n",
       "      <th>nearest_subway</th>\n",
       "      <th>long_nearest_subway</th>\n",
       "      <th>latitude_nearest_subway</th>\n",
       "      <th>number_aoi_df</th>\n",
       "      <th>nearest_aoi_df</th>\n",
       "      <th>long_nearest_aoi_df</th>\n",
       "      <th>latitude_nearest_aoi_df</th>\n",
       "      <th>n_of_crime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>2787</td>\n",
       "      <td>John</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "      <td>11.0</td>\n",
       "      <td>393.447551</td>\n",
       "      <td>-73.975776</td>\n",
       "      <td>40.650782</td>\n",
       "      <td>3.0</td>\n",
       "      <td>560.638794</td>\n",
       "      <td>-73.969087</td>\n",
       "      <td>40.661251</td>\n",
       "      <td>46511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>2845</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "      <td>35.0</td>\n",
       "      <td>93.263136</td>\n",
       "      <td>-73.984591</td>\n",
       "      <td>40.754184</td>\n",
       "      <td>4.0</td>\n",
       "      <td>258.915235</td>\n",
       "      <td>-73.981794</td>\n",
       "      <td>40.758021</td>\n",
       "      <td>40136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>4632</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>17.0</td>\n",
       "      <td>204.436840</td>\n",
       "      <td>-73.940770</td>\n",
       "      <td>40.814229</td>\n",
       "      <td>1.0</td>\n",
       "      <td>192.572103</td>\n",
       "      <td>-73.942680</td>\n",
       "      <td>40.803460</td>\n",
       "      <td>40136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>4869</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "      <td>14.0</td>\n",
       "      <td>118.486794</td>\n",
       "      <td>-73.959990</td>\n",
       "      <td>40.688889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>851.219144</td>\n",
       "      <td>-73.966125</td>\n",
       "      <td>40.669937</td>\n",
       "      <td>46511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>7192</td>\n",
       "      <td>Laura</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>111.622098</td>\n",
       "      <td>-73.944250</td>\n",
       "      <td>40.795020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>211.494984</td>\n",
       "      <td>-73.942680</td>\n",
       "      <td>40.803460</td>\n",
       "      <td>40136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48890</th>\n",
       "      <td>36484665</td>\n",
       "      <td>Charming one bedroom - newly renovated rowhouse</td>\n",
       "      <td>8232441</td>\n",
       "      <td>Sabrina</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>40.67853</td>\n",
       "      <td>-73.94995</td>\n",
       "      <td>Private room</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>79.322475</td>\n",
       "      <td>-73.950426</td>\n",
       "      <td>40.680438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>36485057</td>\n",
       "      <td>Affordable room in Bushwick/East Williamsburg</td>\n",
       "      <td>6570630</td>\n",
       "      <td>Marisol</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bushwick</td>\n",
       "      <td>40.70184</td>\n",
       "      <td>-73.93317</td>\n",
       "      <td>Private room</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>133.273731</td>\n",
       "      <td>-73.933147</td>\n",
       "      <td>40.706152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>36485431</td>\n",
       "      <td>Sunny Studio at Historical Neighborhood</td>\n",
       "      <td>23492952</td>\n",
       "      <td>Ilgar &amp; Aysel</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.81475</td>\n",
       "      <td>-73.94867</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>17.0</td>\n",
       "      <td>159.827727</td>\n",
       "      <td>-73.947535</td>\n",
       "      <td>40.817906</td>\n",
       "      <td>1.0</td>\n",
       "      <td>753.940988</td>\n",
       "      <td>-73.942680</td>\n",
       "      <td>40.803460</td>\n",
       "      <td>40136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>36485609</td>\n",
       "      <td>43rd St. Time Square-cozy single bed</td>\n",
       "      <td>30985759</td>\n",
       "      <td>Taz</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>40.75751</td>\n",
       "      <td>-73.99112</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>154.702322</td>\n",
       "      <td>-73.989735</td>\n",
       "      <td>40.757308</td>\n",
       "      <td>2.0</td>\n",
       "      <td>582.245970</td>\n",
       "      <td>-73.988067</td>\n",
       "      <td>40.742181</td>\n",
       "      <td>40136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>36487245</td>\n",
       "      <td>Trendy duplex in the very heart of Hell's Kitchen</td>\n",
       "      <td>68119814</td>\n",
       "      <td>Christophe</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>40.76404</td>\n",
       "      <td>-73.98933</td>\n",
       "      <td>Private room</td>\n",
       "      <td>90</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>32.0</td>\n",
       "      <td>212.207217</td>\n",
       "      <td>-73.989735</td>\n",
       "      <td>40.757308</td>\n",
       "      <td>4.0</td>\n",
       "      <td>687.858883</td>\n",
       "      <td>-73.988067</td>\n",
       "      <td>40.742181</td>\n",
       "      <td>40136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48895 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                               name   host_id  \\\n",
       "0          2539                 Clean & quiet apt home by the park      2787   \n",
       "1          2595                              Skylit Midtown Castle      2845   \n",
       "2          3647                THE VILLAGE OF HARLEM....NEW YORK !      4632   \n",
       "3          3831                    Cozy Entire Floor of Brownstone      4869   \n",
       "4          5022   Entire Apt: Spacious Studio/Loft by central park      7192   \n",
       "...         ...                                                ...       ...   \n",
       "48890  36484665    Charming one bedroom - newly renovated rowhouse   8232441   \n",
       "48891  36485057      Affordable room in Bushwick/East Williamsburg   6570630   \n",
       "48892  36485431            Sunny Studio at Historical Neighborhood  23492952   \n",
       "48893  36485609               43rd St. Time Square-cozy single bed  30985759   \n",
       "48894  36487245  Trendy duplex in the very heart of Hell's Kitchen  68119814   \n",
       "\n",
       "           host_name neighbourhood_group       neighbourhood  latitude  \\\n",
       "0               John            Brooklyn          Kensington  40.64749   \n",
       "1           Jennifer           Manhattan             Midtown  40.75362   \n",
       "2          Elisabeth           Manhattan              Harlem  40.80902   \n",
       "3        LisaRoxanne            Brooklyn        Clinton Hill  40.68514   \n",
       "4              Laura           Manhattan         East Harlem  40.79851   \n",
       "...              ...                 ...                 ...       ...   \n",
       "48890        Sabrina            Brooklyn  Bedford-Stuyvesant  40.67853   \n",
       "48891        Marisol            Brooklyn            Bushwick  40.70184   \n",
       "48892  Ilgar & Aysel           Manhattan              Harlem  40.81475   \n",
       "48893            Taz           Manhattan      Hell's Kitchen  40.75751   \n",
       "48894     Christophe           Manhattan      Hell's Kitchen  40.76404   \n",
       "\n",
       "       longitude        room_type  price  minimum_nights  number_of_reviews  \\\n",
       "0      -73.97237     Private room    149               1                  9   \n",
       "1      -73.98377  Entire home/apt    225               1                 45   \n",
       "2      -73.94190     Private room    150               3                  0   \n",
       "3      -73.95976  Entire home/apt     89               1                270   \n",
       "4      -73.94399  Entire home/apt     80              10                  9   \n",
       "...          ...              ...    ...             ...                ...   \n",
       "48890  -73.94995     Private room     70               2                  0   \n",
       "48891  -73.93317     Private room     40               4                  0   \n",
       "48892  -73.94867  Entire home/apt    115              10                  0   \n",
       "48893  -73.99112      Shared room     55               1                  0   \n",
       "48894  -73.98933     Private room     90               7                  0   \n",
       "\n",
       "      last_review  reviews_per_month  calculated_host_listings_count  \\\n",
       "0      2018-10-19               0.21                               6   \n",
       "1      2019-05-21               0.38                               2   \n",
       "2             NaN                NaN                               1   \n",
       "3      2019-07-05               4.64                               1   \n",
       "4      2018-11-19               0.10                               1   \n",
       "...           ...                ...                             ...   \n",
       "48890         NaN                NaN                               2   \n",
       "48891         NaN                NaN                               2   \n",
       "48892         NaN                NaN                               1   \n",
       "48893         NaN                NaN                               6   \n",
       "48894         NaN                NaN                               1   \n",
       "\n",
       "       availability_365  number_subway  nearest_subway  long_nearest_subway  \\\n",
       "0                   365           11.0      393.447551           -73.975776   \n",
       "1                   355           35.0       93.263136           -73.984591   \n",
       "2                   365           17.0      204.436840           -73.940770   \n",
       "3                   194           14.0      118.486794           -73.959990   \n",
       "4                     0           13.0      111.622098           -73.944250   \n",
       "...                 ...            ...             ...                  ...   \n",
       "48890                 9           17.0       79.322475           -73.950426   \n",
       "48891                36            8.0      133.273731           -73.933147   \n",
       "48892                27           17.0      159.827727           -73.947535   \n",
       "48893                 2           30.0      154.702322           -73.989735   \n",
       "48894                23           32.0      212.207217           -73.989735   \n",
       "\n",
       "       latitude_nearest_subway  number_aoi_df  nearest_aoi_df  \\\n",
       "0                    40.650782            3.0      560.638794   \n",
       "1                    40.754184            4.0      258.915235   \n",
       "2                    40.814229            1.0      192.572103   \n",
       "3                    40.688889            1.0      851.219144   \n",
       "4                    40.795020            1.0      211.494984   \n",
       "...                        ...            ...             ...   \n",
       "48890                40.680438            0.0             NaN   \n",
       "48891                40.706152            0.0             NaN   \n",
       "48892                40.817906            1.0      753.940988   \n",
       "48893                40.757308            2.0      582.245970   \n",
       "48894                40.757308            4.0      687.858883   \n",
       "\n",
       "       long_nearest_aoi_df  latitude_nearest_aoi_df  n_of_crime  \n",
       "0               -73.969087                40.661251       46511  \n",
       "1               -73.981794                40.758021       40136  \n",
       "2               -73.942680                40.803460       40136  \n",
       "3               -73.966125                40.669937       46511  \n",
       "4               -73.942680                40.803460       40136  \n",
       "...                    ...                      ...         ...  \n",
       "48890                  NaN                      NaN       46511  \n",
       "48891                  NaN                      NaN       46511  \n",
       "48892           -73.942680                40.803460       40136  \n",
       "48893           -73.988067                40.742181       40136  \n",
       "48894           -73.988067                40.742181       40136  \n",
       "\n",
       "[48895 rows x 25 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Our manipulated data:\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  int64\n",
       "name                               object\n",
       "host_id                             int64\n",
       "host_name                          object\n",
       "neighbourhood_group                object\n",
       "neighbourhood                      object\n",
       "latitude                          float64\n",
       "longitude                         float64\n",
       "room_type                          object\n",
       "price                               int64\n",
       "minimum_nights                      int64\n",
       "number_of_reviews                   int64\n",
       "last_review                        object\n",
       "reviews_per_month                 float64\n",
       "calculated_host_listings_count      int64\n",
       "availability_365                    int64\n",
       "number_subway                     float64\n",
       "nearest_subway                    float64\n",
       "long_nearest_subway               float64\n",
       "latitude_nearest_subway           float64\n",
       "number_aoi_df                     float64\n",
       "nearest_aoi_df                    float64\n",
       "long_nearest_aoi_df               float64\n",
       "latitude_nearest_aoi_df           float64\n",
       "n_of_crime                          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Types in our data:\n",
    "\n",
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    0\n",
       "name                                 16\n",
       "host_id                               0\n",
       "host_name                            21\n",
       "neighbourhood_group                   0\n",
       "neighbourhood                         0\n",
       "latitude                              0\n",
       "longitude                             0\n",
       "room_type                             0\n",
       "price                                 0\n",
       "minimum_nights                        0\n",
       "number_of_reviews                     0\n",
       "last_review                       10052\n",
       "reviews_per_month                 10052\n",
       "calculated_host_listings_count        0\n",
       "availability_365                      0\n",
       "number_subway                         0\n",
       "nearest_subway                     1309\n",
       "long_nearest_subway                1309\n",
       "latitude_nearest_subway            1309\n",
       "number_aoi_df                         0\n",
       "nearest_aoi_df                    18316\n",
       "long_nearest_aoi_df               18316\n",
       "latitude_nearest_aoi_df           18316\n",
       "n_of_crime                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we are looking into our data and observe empty places.\n",
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['name'].fillna(df2['name'].mode()[0], inplace=True)\n",
    "df2['host_name'].fillna(df2['host_name'].mode()[0], inplace=True)\n",
    "df2['last_review'].fillna(df2['last_review'].mode()[0], inplace=True)\n",
    "df2['reviews_per_month'].fillna(df2['reviews_per_month'].mode()[0], inplace=True)\n",
    "df2['nearest_subway'].fillna(df2['nearest_subway'].mode()[0], inplace=True)\n",
    "df2['long_nearest_subway'].fillna(df2['long_nearest_subway'].mode()[0], inplace=True)\n",
    "df2['latitude_nearest_subway'].fillna(df2['latitude_nearest_subway'].mode()[0], inplace=True)\n",
    "df2['nearest_aoi_df'].fillna(df2['nearest_aoi_df'].mode()[0], inplace=True)\n",
    "df2['long_nearest_aoi_df'].fillna(df2['long_nearest_aoi_df'].mode()[0], inplace=True)\n",
    "df2['latitude_nearest_aoi_df'].fillna(df2['latitude_nearest_aoi_df'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                0\n",
       "name                              0\n",
       "host_id                           0\n",
       "host_name                         0\n",
       "neighbourhood_group               0\n",
       "neighbourhood                     0\n",
       "latitude                          0\n",
       "longitude                         0\n",
       "room_type                         0\n",
       "price                             0\n",
       "minimum_nights                    0\n",
       "number_of_reviews                 0\n",
       "last_review                       0\n",
       "reviews_per_month                 0\n",
       "calculated_host_listings_count    0\n",
       "availability_365                  0\n",
       "number_subway                     0\n",
       "nearest_subway                    0\n",
       "long_nearest_subway               0\n",
       "latitude_nearest_subway           0\n",
       "number_aoi_df                     0\n",
       "nearest_aoi_df                    0\n",
       "long_nearest_aoi_df               0\n",
       "latitude_nearest_aoi_df           0\n",
       "n_of_crime                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features That Affect Airbnb Prices\n",
    "\n",
    "We discussed the features that would affect Airbnb prices and then we analyzed these features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Room Types Hypothesis\n",
    "\n",
    "First, we thought that the type of the room would play a significant role in the pricing. While we were working on our data we observed that there were three room types in our data. These three room types are Entire home/apt, Private room and Shared room. Our guess was that the more space one has on their own would increase the price as it was more secure, hygenic and comfortable. Moreover, we expected that Private rooms would be in the middle price wise since although it does not provide as much security, hygine and comfort as Entire house/apt., it still provides more than Shared rooms. Although one still has to share bathrooms, the kitchen and living areas of the house, Private rooms provide more space and comfort than Shared rooms. In addition, we agreed that shared rooms would be cheaper since it meant less of everything that increased the price. We also expected that Shared rooms would be more common since they are cheaper.\n",
    "\n",
    "So we decided on our null hypothesis.\n",
    "\n",
    "**Null Hypothesis (H₀):** Room type affects the price.\n",
    "\n",
    "**Alternative Hypothesis (Hᴬ):** Room type does not affect the price.\n",
    "\n",
    "**Significance level**: As most of hypothesis tests assume significance level as 0.05, we are setting it as *0.05* for our test too.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>number_subway</th>\n",
       "      <th>nearest_subway</th>\n",
       "      <th>long_nearest_subway</th>\n",
       "      <th>latitude_nearest_subway</th>\n",
       "      <th>number_aoi_df</th>\n",
       "      <th>nearest_aoi_df</th>\n",
       "      <th>long_nearest_aoi_df</th>\n",
       "      <th>latitude_nearest_aoi_df</th>\n",
       "      <th>n_of_crime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.889500e+04</td>\n",
       "      <td>4.889500e+04</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "      <td>48895.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.901714e+07</td>\n",
       "      <td>6.762001e+07</td>\n",
       "      <td>40.728949</td>\n",
       "      <td>-73.952170</td>\n",
       "      <td>152.720687</td>\n",
       "      <td>7.029962</td>\n",
       "      <td>23.274466</td>\n",
       "      <td>1.095022</td>\n",
       "      <td>7.143982</td>\n",
       "      <td>112.781327</td>\n",
       "      <td>15.462788</td>\n",
       "      <td>219.550153</td>\n",
       "      <td>-73.954689</td>\n",
       "      <td>40.730351</td>\n",
       "      <td>0.973944</td>\n",
       "      <td>519.211191</td>\n",
       "      <td>-73.952967</td>\n",
       "      <td>40.731127</td>\n",
       "      <td>39895.081685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.098311e+07</td>\n",
       "      <td>7.861097e+07</td>\n",
       "      <td>0.054530</td>\n",
       "      <td>0.046157</td>\n",
       "      <td>240.154170</td>\n",
       "      <td>20.510550</td>\n",
       "      <td>44.550582</td>\n",
       "      <td>1.594493</td>\n",
       "      <td>32.952519</td>\n",
       "      <td>131.622289</td>\n",
       "      <td>9.964604</td>\n",
       "      <td>145.099985</td>\n",
       "      <td>0.037732</td>\n",
       "      <td>0.052465</td>\n",
       "      <td>0.987971</td>\n",
       "      <td>239.249198</td>\n",
       "      <td>0.034789</td>\n",
       "      <td>0.041822</td>\n",
       "      <td>9408.284347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.539000e+03</td>\n",
       "      <td>2.438000e+03</td>\n",
       "      <td>40.499790</td>\n",
       "      <td>-74.244420</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.119726</td>\n",
       "      <td>-74.030876</td>\n",
       "      <td>40.576034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.389715</td>\n",
       "      <td>-74.248565</td>\n",
       "      <td>40.499566</td>\n",
       "      <td>5134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.471945e+06</td>\n",
       "      <td>7.822033e+06</td>\n",
       "      <td>40.690100</td>\n",
       "      <td>-73.983070</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>122.055142</td>\n",
       "      <td>-73.981929</td>\n",
       "      <td>40.693172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>313.884476</td>\n",
       "      <td>-73.966125</td>\n",
       "      <td>40.720900</td>\n",
       "      <td>40136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.967728e+07</td>\n",
       "      <td>3.079382e+07</td>\n",
       "      <td>40.723070</td>\n",
       "      <td>-73.955680</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>191.452253</td>\n",
       "      <td>-73.956848</td>\n",
       "      <td>40.728251</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>431.061169</td>\n",
       "      <td>-73.951981</td>\n",
       "      <td>40.720900</td>\n",
       "      <td>40136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.915218e+07</td>\n",
       "      <td>1.074344e+08</td>\n",
       "      <td>40.763115</td>\n",
       "      <td>-73.936275</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>276.889799</td>\n",
       "      <td>-73.939793</td>\n",
       "      <td>40.759600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>727.637277</td>\n",
       "      <td>-73.951981</td>\n",
       "      <td>40.742181</td>\n",
       "      <td>46511.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.648724e+07</td>\n",
       "      <td>2.743213e+08</td>\n",
       "      <td>40.913060</td>\n",
       "      <td>-73.712990</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>629.000000</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>999.397111</td>\n",
       "      <td>-73.755405</td>\n",
       "      <td>40.903125</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>999.997873</td>\n",
       "      <td>-73.732287</td>\n",
       "      <td>40.896210</td>\n",
       "      <td>46511.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id       host_id      latitude     longitude         price  \\\n",
       "count  4.889500e+04  4.889500e+04  48895.000000  48895.000000  48895.000000   \n",
       "mean   1.901714e+07  6.762001e+07     40.728949    -73.952170    152.720687   \n",
       "std    1.098311e+07  7.861097e+07      0.054530      0.046157    240.154170   \n",
       "min    2.539000e+03  2.438000e+03     40.499790    -74.244420      0.000000   \n",
       "25%    9.471945e+06  7.822033e+06     40.690100    -73.983070     69.000000   \n",
       "50%    1.967728e+07  3.079382e+07     40.723070    -73.955680    106.000000   \n",
       "75%    2.915218e+07  1.074344e+08     40.763115    -73.936275    175.000000   \n",
       "max    3.648724e+07  2.743213e+08     40.913060    -73.712990  10000.000000   \n",
       "\n",
       "       minimum_nights  number_of_reviews  reviews_per_month  \\\n",
       "count    48895.000000       48895.000000       48895.000000   \n",
       "mean         7.029962          23.274466           1.095022   \n",
       "std         20.510550          44.550582           1.594493   \n",
       "min          1.000000           0.000000           0.010000   \n",
       "25%          1.000000           1.000000           0.040000   \n",
       "50%          3.000000           5.000000           0.370000   \n",
       "75%          5.000000          24.000000           1.580000   \n",
       "max       1250.000000         629.000000          58.500000   \n",
       "\n",
       "       calculated_host_listings_count  availability_365  number_subway  \\\n",
       "count                    48895.000000      48895.000000   48895.000000   \n",
       "mean                         7.143982        112.781327      15.462788   \n",
       "std                         32.952519        131.622289       9.964604   \n",
       "min                          1.000000          0.000000       0.000000   \n",
       "25%                          1.000000          0.000000       9.000000   \n",
       "50%                          1.000000         45.000000      13.000000   \n",
       "75%                          2.000000        227.000000      19.000000   \n",
       "max                        327.000000        365.000000      43.000000   \n",
       "\n",
       "       nearest_subway  long_nearest_subway  latitude_nearest_subway  \\\n",
       "count    48895.000000         48895.000000             48895.000000   \n",
       "mean       219.550153           -73.954689                40.730351   \n",
       "std        145.099985             0.037732                 0.052465   \n",
       "min          1.119726           -74.030876                40.576034   \n",
       "25%        122.055142           -73.981929                40.693172   \n",
       "50%        191.452253           -73.956848                40.728251   \n",
       "75%        276.889799           -73.939793                40.759600   \n",
       "max        999.397111           -73.755405                40.903125   \n",
       "\n",
       "       number_aoi_df  nearest_aoi_df  long_nearest_aoi_df  \\\n",
       "count   48895.000000    48895.000000         48895.000000   \n",
       "mean        0.973944      519.211191           -73.952967   \n",
       "std         0.987971      239.249198             0.034789   \n",
       "min         0.000000       10.389715           -74.248565   \n",
       "25%         0.000000      313.884476           -73.966125   \n",
       "50%         1.000000      431.061169           -73.951981   \n",
       "75%         1.000000      727.637277           -73.951981   \n",
       "max         4.000000      999.997873           -73.732287   \n",
       "\n",
       "       latitude_nearest_aoi_df    n_of_crime  \n",
       "count             48895.000000  48895.000000  \n",
       "mean                 40.731127  39895.081685  \n",
       "std                   0.041822   9408.284347  \n",
       "min                  40.499566   5134.000000  \n",
       "25%                  40.720900  40136.000000  \n",
       "50%                  40.720900  40136.000000  \n",
       "75%                  40.742181  46511.000000  \n",
       "max                  40.896210  46511.000000  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48895.000000\n",
       "mean       152.720687\n",
       "std        240.154170\n",
       "min          0.000000\n",
       "25%         69.000000\n",
       "50%        106.000000\n",
       "75%        175.000000\n",
       "max      10000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we wanted to observe the prices to understand the range of the prices of Airbnb houses. Here we can see that the mean of Airbnb houses is 152.72 dolars and the maximum price is 10.000 dolars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are going the analyze the effect of room types to the price, we wanted to visualize room types and prices before applying an appropriate test after studying the price.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d83041400>"
      ]
     },
     "execution_count": 125,
     "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": [
    "graph1 = pd.crosstab(index=[df2['price']], columns= df2['room_type'])\n",
    "graph1.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at this graph, the Entire home/apt room type is prioritized. We can also see that there are not many Shared room options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>room_type</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7703</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8000</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8500</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>674 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "room_type  Entire home/apt  Private room  Shared room\n",
       "price                                                \n",
       "0                        2             7            2\n",
       "10                       7             8            2\n",
       "11                       2             0            1\n",
       "12                       2             1            1\n",
       "13                       0             0            1\n",
       "...                    ...           ...          ...\n",
       "7703                     1             0            0\n",
       "8000                     1             0            0\n",
       "8500                     1             0            0\n",
       "9999                     2             1            0\n",
       "10000                    2             1            0\n",
       "\n",
       "[674 rows x 3 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.crosstab(index=[df2['price']], columns=df2['room_type'])\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made this table because we wanted to observe the prices and room types better in order to comment and conduct our hypothesis. We expected that the pricing of entire home/apt would be higher since it provides more security, hygiene (as one does not have to share bathrooms, kitchen or living spaces with other people) and comfort. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After that we wanted to test our hypothesis and we decided that ANOVA is an appropriate test to apply here since we are working on more than two room types, so there are more than 2 groups. We assumed that this is a normal distribution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating samples based on room types.\n",
    "# We had 3 different values for room types.\n",
    "\n",
    "sample_1, sample_2, sample_3 = [df2[df2['room_type'] == i] for i in df2.room_type.unique()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_stats, p_values = f_oneway(sample_1['price'].values, sample_2['price'].values, sample_3['price'].values)\n",
    "p_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying ANOVA test, as p-value we obtained is smaller than the threshold significance level 0.05, we can conclude that room types does not affect the prices. Here, we reject the null hypothesis which means that the alternative hypothesis is true and room types and prices have no correlation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subway Stations Hypothesis\n",
    "\n",
    "Another feature we thought that it had a huge impact on the pricing was subway stations. New York is one of the most visited cities in the world with many famous places to visit. So transportation would play a huge role on the pricing of the Airbnbs. We thought that if the houses had subway stations close to them, their prices would be higher. \n",
    "\n",
    "In the first step of our project we decided a walking distance which is 1 km and we found how many subway stations each house has in walking distance and added these result to our data.\n",
    "\n",
    "We assumed that it was normal distribution.\n",
    "\n",
    "We decided on our null hypothesis.\n",
    "\n",
    "**Null Hypothesis (H₀):** Number of subway stations in walking distance affects the price.\n",
    "\n",
    "**Alternative Hypothesis (Hᴬ):** Number of subway stations in walking distance does not affect the price.\n",
    "\n",
    "**Significance level:** As most of hypothesis tests assume significance level as 0.05, we are setting it as 0.05 for our test too.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before starting on analyzing the relationship between number of subway stations in walking distance and price we wanted to visualize it so that we can have a better understanding of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d857998e0>"
      ]
     },
     "execution_count": 128,
     "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": [
    "# We added number of subway stations in walking distance (1 km) under 'number_subway' in the first step of our project.\n",
    "\n",
    "graph2 = pd.crosstab(index=[df2['price']], columns= df2['number_subway'])\n",
    "graph2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the graph was difficult to analyze so we decided to catagorize number of stations in order to understand better.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48895.000000\n",
       "mean        15.462788\n",
       "std          9.964604\n",
       "min          0.000000\n",
       "25%          9.000000\n",
       "50%         13.000000\n",
       "75%         19.000000\n",
       "max         43.000000\n",
       "Name: number_subway, dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detailed information on the number of subway stations in walking distance of each Airbnb house.\n",
    "df2['number_subway'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can observe that the maximum number of subway stations in walking distance is 43 and the mean number is 15.46."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function catagorizes number of stations close to Airbnb houses\n",
    "def change_no_stations(number_subway):\n",
    "     if number_subway < 5:\n",
    "        return 'a. 0-5'\n",
    "     elif number_subway < 10:\n",
    "        return 'b. 6-10'\n",
    "     elif number_subway < 15:\n",
    "        return 'c. 11-15'\n",
    "     elif number_subway < 20:\n",
    "        return 'd. 16-20'\n",
    "     elif number_subway < 30:\n",
    "        return 'e. 21-30'\n",
    "     elif number_subway < 40:\n",
    "        return 'f. 31-40'\n",
    "     elif number_subway < 45:\n",
    "        return 'g. 41-45'\n",
    "     elif number_subway == 0:\n",
    "        return 'h. no subway'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Applying the change\n",
    "df2['number_subway'] = df2['number_subway'].apply(change_no_stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d85ade070>"
      ]
     },
     "execution_count": 131,
     "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": [
    "graph3 = pd.crosstab(index=[df2['price']], columns= df2['number_subway'])\n",
    "graph3.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we can see it a little bit more clear, we can see that our thoughts were not correct since we thought that the more a house has subway stations closer, the higher the price would be. However we can see from this graph that it does not affect much. We still wanted to test our hypothesis with statiscal tests and since we grouped number of subway stations in walking distance, we decided to do ANOVA test. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>number_subway</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <th>b. 6-10</th>\n",
       "      <th>c. 11-15</th>\n",
       "      <th>d. 16-20</th>\n",
       "      <th>e. 21-30</th>\n",
       "      <th>f. 31-40</th>\n",
       "      <th>g. 41-45</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7703</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8000</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8500</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>674 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "number_subway  a. 0-5  b. 6-10  c. 11-15  d. 16-20  e. 21-30  f. 31-40  \\\n",
       "price                                                                    \n",
       "0                   0        1         5         3         2         0   \n",
       "10                  2        2         7         1         2         3   \n",
       "11                  0        1         2         0         0         0   \n",
       "12                  0        1         1         1         0         1   \n",
       "13                  1        0         0         0         0         0   \n",
       "...               ...      ...       ...       ...       ...       ...   \n",
       "7703                0        0         1         0         0         0   \n",
       "8000                0        1         0         0         0         0   \n",
       "8500                0        0         0         0         0         1   \n",
       "9999                0        1         0         0         2         0   \n",
       "10000               0        1         1         0         1         0   \n",
       "\n",
       "number_subway  g. 41-45  \n",
       "price                    \n",
       "0                     0  \n",
       "10                    0  \n",
       "11                    0  \n",
       "12                    0  \n",
       "13                    0  \n",
       "...                 ...  \n",
       "7703                  0  \n",
       "8000                  0  \n",
       "8500                  0  \n",
       "9999                  0  \n",
       "10000                 0  \n",
       "\n",
       "[674 rows x 7 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a table to see our groups and data better.\n",
    "table2 = pd.crosstab(index=[df2['price']], columns=df2['number_subway'])\n",
    "table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating samples based on number of subway stations in walking distance.\n",
    "# We had 7 different groups for subway stations.\n",
    "\n",
    "sample_1, sample_2, sample_3, sample_4, sample_5, sample_6, sample_7 = [df2[df2['number_subway'] == i] for i in df2.number_subway.unique()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  int64\n",
       "name                               object\n",
       "host_id                             int64\n",
       "host_name                          object\n",
       "neighbourhood_group                object\n",
       "neighbourhood                      object\n",
       "latitude                          float64\n",
       "longitude                         float64\n",
       "room_type                          object\n",
       "price                               int64\n",
       "minimum_nights                      int64\n",
       "number_of_reviews                   int64\n",
       "last_review                        object\n",
       "reviews_per_month                 float64\n",
       "calculated_host_listings_count      int64\n",
       "availability_365                    int64\n",
       "number_subway                      object\n",
       "nearest_subway                    float64\n",
       "long_nearest_subway               float64\n",
       "latitude_nearest_subway           float64\n",
       "number_aoi_df                     float64\n",
       "nearest_aoi_df                    float64\n",
       "long_nearest_aoi_df               float64\n",
       "latitude_nearest_aoi_df           float64\n",
       "n_of_crime                          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_stats, p_values = f_oneway(sample_1['price'].values, sample_2['price'].values, sample_3['price'].values, sample_4['price'].values, sample_5['price'].values, sample_6['price'].values, sample_7['price'].values)\n",
    "p_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying ANOVA test, we can see that the p-value we obtained is a lot smaller than the threshold significance level 0.05, we can conclude that the number of subway stations in walking distance does not affect the prices. Here, we reject the null hypothesis which means that the alternative hypothesis is true and the number of subway stations in walking distance and prices have no correlation.\n",
    "\n",
    "By applying a statistical test we also comfirmed our second thoughts after seeing the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features That Affect Airbnb Prices in Different Neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crime Hypothesis\n",
    "\n",
    "Since a lot of tourists visit New York people want to make sure that they are staying in safe neighbourhoods. So we thought that the number of crimes in the neighbourhood of the Airbnb house is in would affect the price. We assumed that the more crime a neighbourhood has the lower the price would be. Also, the prices in the safer neighbourhoods would be higher.\n",
    "\n",
    "Our manipulated data which we obtained in the first step of the project has the number of crimes in the neighbourhoods of the houses. Again, we assume that this is a normal distribution.\n",
    "\n",
    "**Null Hypothesis (H₀):** Number of crimes in a neighborhood affects the prices of Airbnb houses.\n",
    "\n",
    "**Alternative Hypothesis (Hᴬ):** Number of crimes in a neighborhood affects the prices of Airbnb houses.\n",
    "\n",
    "**Significance level:** As most of hypothesis tests assume significance level as 0.05, we are setting it as 0.05 for our test too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48895.000000\n",
       "mean       152.720687\n",
       "std        240.154170\n",
       "min          0.000000\n",
       "25%         69.000000\n",
       "50%        106.000000\n",
       "75%        175.000000\n",
       "max      10000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detailed information on price\n",
    "\n",
    "df2['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function catagorizes the prices\n",
    "def price_change(price):\n",
    "    if price < 50:\n",
    "        return 'a. 0-50'\n",
    "    elif price < 100:\n",
    "        return 'b. 51-100'\n",
    "    elif price < 150:\n",
    "        return 'c. 101-150'\n",
    "    elif price < 200:\n",
    "        return 'd. 151-200'\n",
    "    elif price < 300:\n",
    "        return 'e. 201-300'\n",
    "    elif price < 400:\n",
    "        return 'f. 301-400'\n",
    "    elif price < 500:\n",
    "        return 'g. 401-500'\n",
    "    else:\n",
    "        return 'h. 500-10.000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying the changes\n",
    "df2['price'] = df2['price'].apply(price_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>neighbourhood_group</th>\n",
       "      <th>Bronx</th>\n",
       "      <th>Brooklyn</th>\n",
       "      <th>Manhattan</th>\n",
       "      <th>Queens</th>\n",
       "      <th>Staten Island</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <th>n_of_crime</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 rowspan=\"5\" valign=\"top\">a. 0-50</th>\n",
       "      <th>5134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1157</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46511</th>\n",
       "      <td>0</td>\n",
       "      <td>2749</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">b. 51-100</th>\n",
       "      <th>5134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2708</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>509</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46511</th>\n",
       "      <td>0</td>\n",
       "      <td>8155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">c. 101-150</th>\n",
       "      <th>5134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>958</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4724</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46511</th>\n",
       "      <td>0</td>\n",
       "      <td>4118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">d. 151-200</th>\n",
       "      <th>5134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46511</th>\n",
       "      <td>0</td>\n",
       "      <td>2549</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">e. 201-300</th>\n",
       "      <th>5134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3921</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46511</th>\n",
       "      <td>0</td>\n",
       "      <td>1620</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">f. 301-400</th>\n",
       "      <th>5134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1383</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46511</th>\n",
       "      <td>0</td>\n",
       "      <td>462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">g. 401-500</th>\n",
       "      <th>5134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>547</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46511</th>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">h. 500-10.000</th>\n",
       "      <th>5134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>905</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44117</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46511</th>\n",
       "      <td>0</td>\n",
       "      <td>279</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "neighbourhood_group       Bronx  Brooklyn  Manhattan  Queens  Staten Island\n",
       "price         n_of_crime                                                   \n",
       "a. 0-50       5134            0         0          0       0             85\n",
       "              16975           0         0          0    1157              0\n",
       "              40136           0         0        722       0              0\n",
       "              44117         314         0          0       0              0\n",
       "              46511           0      2749          0       0              0\n",
       "b. 51-100     5134            0         0          0       0            164\n",
       "              16975           0         0          0    2708              0\n",
       "              40136           0         0       5314       0              0\n",
       "              44117         509         0          0       0              0\n",
       "              46511           0      8155          0       0              0\n",
       "c. 101-150    5134            0         0          0       0             66\n",
       "              16975           0         0          0     958              0\n",
       "              40136           0         0       4724       0              0\n",
       "              44117         167         0          0       0              0\n",
       "              46511           0      4118          0       0              0\n",
       "d. 151-200    5134            0         0          0       0             27\n",
       "              16975           0         0          0     432              0\n",
       "              40136           0         0       4145       0              0\n",
       "              44117          47         0          0       0              0\n",
       "              46511           0      2549          0       0              0\n",
       "e. 201-300    5134            0         0          0       0             20\n",
       "              16975           0         0          0     280              0\n",
       "              40136           0         0       3921       0              0\n",
       "              44117          26         0          0       0              0\n",
       "              46511           0      1620          0       0              0\n",
       "f. 301-400    5134            0         0          0       0              3\n",
       "              16975           0         0          0      68              0\n",
       "              40136           0         0       1383       0              0\n",
       "              44117          14         0          0       0              0\n",
       "              46511           0       462          0       0              0\n",
       "g. 401-500    5134            0         0          0       0              2\n",
       "              16975           0         0          0      27              0\n",
       "              40136           0         0        547       0              0\n",
       "              44117           5         0          0       0              0\n",
       "              46511           0       172          0       0              0\n",
       "h. 500-10.000 5134            0         0          0       0              6\n",
       "              16975           0         0          0      36              0\n",
       "              40136           0         0        905       0              0\n",
       "              44117           9         0          0       0              0\n",
       "              46511           0       279          0       0              0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = pd.crosstab(index=[df2['price'], df2['n_of_crime']], columns= df2['neighbourhood_group'])\n",
    "freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can observe our data more clear as we can see prices, neighbourhoods and number of crimes in that neighbourhood.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have catagorized the prices, we decided to apply Chi2 test.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test t=195580.000000 p-value=0.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(195580.0,\n",
       " 0.0,\n",
       " 156,\n",
       " array([[1.89661520e+00, 3.49491768e+01, 3.76558953e+01, 9.84988240e+00,\n",
       "         6.48430310e-01],\n",
       "        [2.58162798e+01, 4.75719971e+02, 5.12563186e+02, 1.34074282e+02,\n",
       "         8.82628081e+00],\n",
       "        [1.61100726e+01, 2.96862419e+02, 3.19853605e+02, 8.36660599e+01,\n",
       "         5.50784334e+00],\n",
       "        [7.00631966e+00, 1.29106371e+02, 1.39105307e+02, 3.63866244e+01,\n",
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       "         5.18744248e-01],\n",
       "        [3.08590449e+01, 5.68643665e+02, 6.12683567e+02, 1.60263381e+02,\n",
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       "        [3.12383679e-01, 5.75633500e+00, 6.20214746e+00, 1.62233357e+00,\n",
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       "        [6.02454239e-01, 1.11015032e+01, 1.19612844e+01, 3.12878617e+00,\n",
       "         2.05971981e-01],\n",
       "        [1.22052766e+01, 2.24908232e+02, 2.42326761e+02, 6.33868903e+01,\n",
       "         4.17283976e+00],\n",
       "        [1.11565600e-01, 2.05583393e+00, 2.21505266e+00, 5.79404847e-01,\n",
       "         3.81429594e-02],\n",
       "        [3.83785663e+00, 7.07206872e+01, 7.61978116e+01, 1.99315267e+01,\n",
       "         1.31211780e+00],\n",
       "        [1.33878720e-01, 2.46700072e+00, 2.65806320e+00, 6.95285817e-01,\n",
       "         4.57715513e-02],\n",
       "        [8.03272318e-01, 1.48020043e+01, 1.59483792e+01, 4.17171490e+00,\n",
       "         2.74629308e-01],\n",
       "        [2.01933736e+01, 3.72105941e+02, 4.00924532e+02, 1.04872277e+02,\n",
       "         6.90387565e+00],\n",
       "        [2.00818080e-01, 3.70050107e+00, 3.98709479e+00, 1.04292872e+00,\n",
       "         6.86573269e-02],\n",
       "        [6.22536047e+00, 1.14715533e+02, 1.23599939e+02, 3.23307905e+01,\n",
       "         2.12837713e+00]]))"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_stats = stats.chi2_contingency(freq)\n",
    "print ('Test t=%f p-value=%f' % (chi_stats[0], chi_stats[1]))\n",
    "chi_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186.14575078141743"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critical_value = crit = stats.chi2.ppf( q = 1 - alpha,\n",
    "                                     df= chi_stats[2])\n",
    "critical_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = np.linspace(0, 50, 10000) #set range for x-axis\n",
    "#plt.plot(x, stats.chi2.pdf(x, chi_stats[2])) #plot chi2 distribution with given degrees of freedom\n",
    "\n",
    "#plt.axvline(x=critical_value, ymin=0.05, ymax=0.05, c='r')\n",
    "#plt.axvline(x=chi_stats[0], ymin=0.05, ymax=0.1, c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195580.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_chi_val = chi_stats[0]\n",
    "obs_chi_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject null hypothesis!\n"
     ]
    }
   ],
   "source": [
    "if obs_chi_val <= critical_value:\n",
    "    print('Null hpothesis cannot be rejected!')\n",
    "else:\n",
    "    print('We reject null hypothesis!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying Chi2 test, we can see that the p-value we obtained is a lot smaller than the threshold significance level 0.05, we can conclude that the number of crimes in the neighbourhood of the houses does not affect the prices. Here, we reject the null hypothesis which means that the alternative hypothesis is true and the number of crimes in the neighbourhood of the houses and prices have no correlation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subway Stations Hypothesis for Neighbourhoods\n",
    "\n",
    "After analyzig subway stations for each house we wanted to observe the effect of subway stations in walking distance neihgbourhood by neighbourhood to see if there are any differences since we thought that people would prefer to stay in neighbourhoods that have a lot of subway stations in walking distance to the places that they are staying in since transportation is a huge issue in New York and it would affect the price. We thought that if a neighbourhood has more subway stations in walking distance the prices of the houses in that neighbourhood would be higher.\n",
    "\n",
    "In the first step of our project we decided a walking distance which is 1 km and we found how many subway stations each house has in walking distance and added these result to our data.\n",
    "\n",
    "We assumed that it was normal distribution.\n",
    "\n",
    "We decided on our null hypothesis.\n",
    "\n",
    "**Null Hypothesis (H₀):** Number of subway stations in walking distance in a neighborhood affects the prices of Airbnb houses.\n",
    "\n",
    "**Alternative Hypothesis (Hᴬ):** Number subway stations in walking distance affects the prices of Airbnb houses.\n",
    "\n",
    "**Significance level:** As most of hypothesis tests assume significance level as 0.05, we are setting it as 0.05 for our test too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  int64\n",
       "name                               object\n",
       "host_id                             int64\n",
       "host_name                          object\n",
       "neighbourhood_group                object\n",
       "neighbourhood                      object\n",
       "latitude                          float64\n",
       "longitude                         float64\n",
       "room_type                          object\n",
       "price                              object\n",
       "minimum_nights                      int64\n",
       "number_of_reviews                   int64\n",
       "last_review                        object\n",
       "reviews_per_month                 float64\n",
       "calculated_host_listings_count      int64\n",
       "availability_365                    int64\n",
       "number_subway                      object\n",
       "nearest_subway                    float64\n",
       "long_nearest_subway               float64\n",
       "latitude_nearest_subway           float64\n",
       "number_aoi_df                     float64\n",
       "nearest_aoi_df                    float64\n",
       "long_nearest_aoi_df               float64\n",
       "latitude_nearest_aoi_df           float64\n",
       "n_of_crime                          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d85b172b0>"
      ]
     },
     "execution_count": 145,
     "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": [
    "graph5 = pd.crosstab(index=[df2['neighbourhood_group']], columns= df2['number_subway'])\n",
    "graph5.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wanted to visualize the number of subway stations in walking distance in neighbourhoods to observe a correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>neighbourhood_group</th>\n",
       "      <th>Bronx</th>\n",
       "      <th>Brooklyn</th>\n",
       "      <th>Manhattan</th>\n",
       "      <th>Queens</th>\n",
       "      <th>Staten Island</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <th>number_subway</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 rowspan=\"7\" valign=\"top\">a. 0-50</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <td>57</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>550</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 6-10</th>\n",
       "      <td>147</td>\n",
       "      <td>913</td>\n",
       "      <td>134</td>\n",
       "      <td>501</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 11-15</th>\n",
       "      <td>107</td>\n",
       "      <td>993</td>\n",
       "      <td>292</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 16-20</th>\n",
       "      <td>3</td>\n",
       "      <td>657</td>\n",
       "      <td>189</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 21-30</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 31-40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 41-45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">b. 51-100</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <td>141</td>\n",
       "      <td>484</td>\n",
       "      <td>3</td>\n",
       "      <td>1183</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 6-10</th>\n",
       "      <td>214</td>\n",
       "      <td>2443</td>\n",
       "      <td>761</td>\n",
       "      <td>1316</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 11-15</th>\n",
       "      <td>151</td>\n",
       "      <td>2864</td>\n",
       "      <td>1780</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 16-20</th>\n",
       "      <td>3</td>\n",
       "      <td>2160</td>\n",
       "      <td>1070</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 21-30</th>\n",
       "      <td>0</td>\n",
       "      <td>204</td>\n",
       "      <td>621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 31-40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 41-45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">c. 101-150</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <td>38</td>\n",
       "      <td>219</td>\n",
       "      <td>4</td>\n",
       "      <td>338</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 6-10</th>\n",
       "      <td>78</td>\n",
       "      <td>968</td>\n",
       "      <td>407</td>\n",
       "      <td>541</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 11-15</th>\n",
       "      <td>51</td>\n",
       "      <td>1540</td>\n",
       "      <td>1306</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 16-20</th>\n",
       "      <td>0</td>\n",
       "      <td>1154</td>\n",
       "      <td>696</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 21-30</th>\n",
       "      <td>0</td>\n",
       "      <td>237</td>\n",
       "      <td>808</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 31-40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1439</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 41-45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">d. 151-200</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <td>12</td>\n",
       "      <td>126</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 6-10</th>\n",
       "      <td>19</td>\n",
       "      <td>576</td>\n",
       "      <td>222</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 11-15</th>\n",
       "      <td>15</td>\n",
       "      <td>968</td>\n",
       "      <td>930</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 16-20</th>\n",
       "      <td>1</td>\n",
       "      <td>729</td>\n",
       "      <td>536</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 21-30</th>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>903</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 31-40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1487</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 41-45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">e. 201-300</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <td>5</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 6-10</th>\n",
       "      <td>12</td>\n",
       "      <td>373</td>\n",
       "      <td>166</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 11-15</th>\n",
       "      <td>8</td>\n",
       "      <td>577</td>\n",
       "      <td>683</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 16-20</th>\n",
       "      <td>1</td>\n",
       "      <td>452</td>\n",
       "      <td>442</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 21-30</th>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>899</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 31-40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1582</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 41-45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">f. 301-400</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 6-10</th>\n",
       "      <td>6</td>\n",
       "      <td>114</td>\n",
       "      <td>80</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 11-15</th>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>213</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 16-20</th>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 21-30</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>260</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 31-40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>611</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 41-45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">g. 401-500</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 6-10</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 11-15</th>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 16-20</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 21-30</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 31-40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 41-45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">h. 500-10.000</th>\n",
       "      <th>a. 0-5</th>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 6-10</th>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>71</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 11-15</th>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>136</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 16-20</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 21-30</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 31-40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>378</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 41-45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "neighbourhood_group          Bronx  Brooklyn  Manhattan  Queens  Staten Island\n",
       "price         number_subway                                                   \n",
       "a. 0-50       a. 0-5            57       151          0     550             85\n",
       "              b. 6-10          147       913        134     501              0\n",
       "              c. 11-15         107       993        292     101              0\n",
       "              d. 16-20           3       657        189       5              0\n",
       "              e. 21-30           0        35         71       0              0\n",
       "              f. 31-40           0         0         35       0              0\n",
       "              g. 41-45           0         0          1       0              0\n",
       "b. 51-100     a. 0-5           141       484          3    1183            164\n",
       "              b. 6-10          214      2443        761    1316              0\n",
       "              c. 11-15         151      2864       1780     205              0\n",
       "              d. 16-20           3      2160       1070       4              0\n",
       "              e. 21-30           0       204        621       0              0\n",
       "              f. 31-40           0         0       1032       0              0\n",
       "              g. 41-45           0         0         47       0              0\n",
       "c. 101-150    a. 0-5            38       219          4     338             66\n",
       "              b. 6-10           78       968        407     541              0\n",
       "              c. 11-15          51      1540       1306      75              0\n",
       "              d. 16-20           0      1154        696       4              0\n",
       "              e. 21-30           0       237        808       0              0\n",
       "              f. 31-40           0         0       1439       0              0\n",
       "              g. 41-45           0         0         64       0              0\n",
       "d. 151-200    a. 0-5            12       126          1     163             27\n",
       "              b. 6-10           19       576        222     221              0\n",
       "              c. 11-15          15       968        930      47              0\n",
       "              d. 16-20           1       729        536       1              0\n",
       "              e. 21-30           0       150        903       0              0\n",
       "              f. 31-40           0         0       1487       0              0\n",
       "              g. 41-45           0         0         66       0              0\n",
       "e. 201-300    a. 0-5             5       104          1     127             20\n",
       "              b. 6-10           12       373        166     132              0\n",
       "              c. 11-15           8       577        683      20              0\n",
       "              d. 16-20           1       452        442       1              0\n",
       "              e. 21-30           0       114        899       0              0\n",
       "              f. 31-40           0         0       1582       0              0\n",
       "              g. 41-45           0         0        148       0              0\n",
       "f. 301-400    a. 0-5             8        34          0      31              3\n",
       "              b. 6-10            6       114         80      31              0\n",
       "              c. 11-15           0       163        213       6              0\n",
       "              d. 16-20           0       122        178       0              0\n",
       "              e. 21-30           0        29        260       0              0\n",
       "              f. 31-40           0         0        611       0              0\n",
       "              g. 41-45           0         0         41       0              0\n",
       "g. 401-500    a. 0-5             4        13          0      21              2\n",
       "              b. 6-10            1        45         18       5              0\n",
       "              c. 11-15           0        60         87       1              0\n",
       "              d. 16-20           0        49         62       0              0\n",
       "              e. 21-30           0         5        136       0              0\n",
       "              f. 31-40           0         0        216       0              0\n",
       "              g. 41-45           0         0         28       0              0\n",
       "h. 500-10.000 a. 0-5             5        27          0      19              6\n",
       "              b. 6-10            2        55         71      15              0\n",
       "              c. 11-15           2       100        136       2              0\n",
       "              d. 16-20           0        70        110       0              0\n",
       "              e. 21-30           0        27        183       0              0\n",
       "              f. 31-40           0         0        378       0              0\n",
       "              g. 41-45           0         0         27       0              0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq3 = pd.crosstab(index=[df2['price'], df2['number_subway']], columns= df2['neighbourhood_group'])\n",
    "freq3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we wanted to see the number of subway stations in walking distance in each neighbourhood as well as the prices. With this table we could not comment on the correlation clearly. so we decided to do Chi 2 test since we catagorized the prices and number of subway stations in walking distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test t=35485.656340 p-value=0.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(35485.65633969062,\n",
       " 0.0,\n",
       " 220,\n",
       " array([[1.88099601e+01, 3.46613601e+02, 3.73457879e+02, 9.76876572e+01,\n",
       "         6.43090296e+00],\n",
       "        [3.78207383e+01, 6.96927702e+02, 7.50902853e+02, 1.96418243e+02,\n",
       "         1.29304632e+01],\n",
       "        [3.33134881e+01, 6.13872011e+02, 6.61414725e+02, 1.73010287e+02,\n",
       "         1.13894877e+01],\n",
       "        [1.90554044e+01, 3.51136435e+02, 3.78330995e+02, 9.89623479e+01,\n",
       "         6.51481747e+00],\n",
       "        [2.36519071e+00, 4.35836793e+01, 4.69591165e+01, 1.22833828e+01,\n",
       "         8.08630739e-01],\n",
       "        [7.80959198e-01, 1.43908375e+01, 1.55053686e+01, 4.05583393e+00,\n",
       "         2.67000716e-01],\n",
       "        [2.23131200e-02, 4.11166786e-01, 4.43010533e-01, 1.15880969e-01,\n",
       "         7.62859188e-03],\n",
       "        [4.40684119e+01, 8.12054402e+02, 8.74945802e+02, 2.28864915e+02,\n",
       "         1.50664690e+01],\n",
       "        [1.05630310e+02, 1.94646356e+03, 2.09721186e+03, 5.48580509e+02,\n",
       "         3.61137540e+01],\n",
       "        [1.11565600e+02, 2.05583393e+03, 2.21505266e+03, 5.79404847e+02,\n",
       "         3.81429594e+01],\n",
       "        [7.22275693e+01, 1.33094689e+03, 1.43402509e+03, 3.75106698e+02,\n",
       "         2.46937519e+01],\n",
       "        [1.84083240e+01, 3.39212598e+02, 3.65483690e+02, 9.56017998e+01,\n",
       "         6.29358830e+00],\n",
       "        [2.30271398e+01, 4.24324123e+02, 4.57186870e+02, 1.19589160e+02,\n",
       "         7.87270682e+00],\n",
       "        [1.04871664e+00, 1.93248389e+01, 2.08214950e+01, 5.44640556e+00,\n",
       "         3.58543818e-01],\n",
       "        [1.48382248e+01, 2.73425913e+02, 2.94602004e+02, 7.70608447e+01,\n",
       "         5.07301360e+00],\n",
       "        [4.44923612e+01, 8.19866571e+02, 8.83363002e+02, 2.31066653e+02,\n",
       "         1.52114122e+01],\n",
       "        [6.63145925e+01, 1.22198769e+03, 1.31662730e+03, 3.44398241e+02,\n",
       "         2.26721751e+01],\n",
       "        [4.13685244e+01, 7.62303221e+02, 8.21341528e+02, 2.14843317e+02,\n",
       "         1.41434093e+01],\n",
       "        [2.33172103e+01, 4.29669291e+02, 4.62946007e+02, 1.21095613e+02,\n",
       "         7.97187852e+00],\n",
       "        [3.21085796e+01, 5.91669005e+02, 6.37492157e+02, 1.66752715e+02,\n",
       "         1.09775437e+01],\n",
       "        [1.42803968e+00, 2.63146743e+01, 2.83526741e+01, 7.41638204e+00,\n",
       "         4.88229880e-01],\n",
       "        [7.34101646e+00, 1.35273873e+02, 1.45750465e+02, 3.81248389e+01,\n",
       "         2.50980673e+00],\n",
       "        [2.31610185e+01, 4.26791124e+02, 4.59844933e+02, 1.20284446e+02,\n",
       "         7.91847837e+00],\n",
       "        [4.37337151e+01, 8.05886901e+02, 8.68300644e+02, 2.27126700e+02,\n",
       "         1.49520401e+01],\n",
       "        [2.82707230e+01, 5.20948318e+02, 5.61294345e+02, 1.46821188e+02,\n",
       "         9.66542591e+00],\n",
       "        [2.34957153e+01, 4.32958626e+02, 4.66490091e+02, 1.22022661e+02,\n",
       "         8.03290725e+00],\n",
       "        [3.31796094e+01, 6.11405011e+02, 6.58756662e+02, 1.72315002e+02,\n",
       "         1.13437161e+01],\n",
       "        [1.47266592e+00, 2.71370079e+01, 2.92386952e+01, 7.64814398e+00,\n",
       "         5.03487064e-01],\n",
       "        [5.73447183e+00, 1.05669864e+02, 1.13853707e+02, 2.97814091e+01,\n",
       "         1.96054811e+00],\n",
       "        [1.52398609e+01, 2.80826915e+02, 3.02576194e+02, 7.91467021e+01,\n",
       "         5.21032825e+00],\n",
       "        [2.87392985e+01, 5.29582820e+02, 5.70597566e+02, 1.49254689e+02,\n",
       "         9.82562634e+00],\n",
       "        [1.99925555e+01, 3.68405440e+02, 3.96937437e+02, 1.03829349e+02,\n",
       "         6.83521832e+00],\n",
       "        [2.26031905e+01, 4.16511954e+02, 4.48769670e+02, 1.17387422e+02,\n",
       "         7.72776358e+00],\n",
       "        [3.52993558e+01, 6.50465855e+02, 7.00842663e+02, 1.83323694e+02,\n",
       "         1.20684324e+01],\n",
       "        [3.30234175e+00, 6.08526843e+01, 6.55655589e+01, 1.71503835e+01,\n",
       "         1.12903160e+00],\n",
       "        [1.69579712e+00, 3.12486757e+01, 3.36688005e+01, 8.80695368e+00,\n",
       "         5.79772983e-01],\n",
       "        [5.15433071e+00, 9.49795276e+01, 1.02335433e+02, 2.67685039e+01,\n",
       "         1.76220472e+00],\n",
       "        [8.52361182e+00, 1.57065712e+02, 1.69230024e+02, 4.42665303e+01,\n",
       "         2.91412210e+00],\n",
       "        [6.69393599e+00, 1.23350036e+02, 1.32903160e+02, 3.47642908e+01,\n",
       "         2.28857756e+00],\n",
       "        [6.44849167e+00, 1.18827201e+02, 1.28030044e+02, 3.34896002e+01,\n",
       "         2.20466305e+00],\n",
       "        [1.36333163e+01, 2.51222906e+02, 2.70679436e+02, 7.08032723e+01,\n",
       "         4.66106964e+00],\n",
       "        [9.14837918e-01, 1.68578382e+01, 1.81634318e+01, 4.75111975e+00,\n",
       "         3.12772267e-01],\n",
       "        [8.92524798e-01, 1.64466714e+01, 1.77204213e+01, 4.63523878e+00,\n",
       "         3.05143675e-01],\n",
       "        [1.53960528e+00, 2.83705082e+01, 3.05677268e+01, 7.99578689e+00,\n",
       "         5.26372840e-01],\n",
       "        [3.30234175e+00, 6.08526843e+01, 6.55655589e+01, 1.71503835e+01,\n",
       "         1.12903160e+00],\n",
       "        [2.47675631e+00, 4.56395132e+01, 4.91741691e+01, 1.28627876e+01,\n",
       "         8.46773699e-01],\n",
       "        [3.14614991e+00, 5.79745168e+01, 6.24644851e+01, 1.63392167e+01,\n",
       "         1.07563146e+00],\n",
       "        [4.81963391e+00, 8.88120258e+01, 9.56902751e+01, 2.50302894e+01,\n",
       "         1.64777585e+00],\n",
       "        [6.24767359e-01, 1.15126700e+01, 1.24042949e+01, 3.24466714e+00,\n",
       "         2.13600573e-01],\n",
       "        [1.27184784e+00, 2.34365068e+01, 2.52516004e+01, 6.60521526e+00,\n",
       "         4.34829737e-01],\n",
       "        [3.19077615e+00, 5.87968504e+01, 6.33505062e+01, 1.65709786e+01,\n",
       "         1.09088864e+00],\n",
       "        [5.35514879e+00, 9.86800286e+01, 1.06322528e+02, 2.78114327e+01,\n",
       "         1.83086205e+00],\n",
       "        [4.01636159e+00, 7.40100215e+01, 7.97418959e+01, 2.08585745e+01,\n",
       "         1.37314654e+00],\n",
       "        [4.68575519e+00, 8.63450251e+01, 9.30322119e+01, 2.43350036e+01,\n",
       "         1.60200429e+00],\n",
       "        [8.43435934e+00, 1.55421045e+02, 1.67457981e+02, 4.38030064e+01,\n",
       "         2.88360773e+00],\n",
       "        [6.02454239e-01, 1.11015032e+01, 1.19612844e+01, 3.12878617e+00,\n",
       "         2.05971981e-01]]))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_stats3 = stats.chi2_contingency(freq3)\n",
    "print ('Test t=%f p-value=%f' % (chi_stats3[0], chi_stats3[1]))\n",
    "chi_stats3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255.60181599470303"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critical_value3 = crit3 = stats.chi2.ppf( q = 1 - alpha,\n",
    "                                     df= chi_stats3[2])\n",
    "critical_value3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35485.65633969062"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_chi_val3 = chi_stats3[0]\n",
    "obs_chi_val3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject null hypothesis!\n"
     ]
    }
   ],
   "source": [
    "if obs_chi_val3 <= critical_value3:\n",
    "    print('Null hpothesis cannot be rejected!')\n",
    "else:\n",
    "    print('We reject null hypothesis!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though we could observe the correlation with our eyes, we were able to observe by applying Chi 2 test that the p-value we obtained is a lot smaller than the threshold significance level 0.05, we can conclude that the number of subway stations in walking distance in neighbourhoods does not affect the prices. Here, we reject the null hypothesis which means that the alternative hypothesis is true and the number of subway stations in walking distance in neighbourhoods and prices have no correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effects of Proximity of POIs to Airbnb Houses\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of AOIs to the Houses Hypothesis\n",
    "\n",
    "Since New York is full of tourists we thought that the distance between AOIs (Area of Interests) and Airbnb houses would important. That is why we wanted to observe the effect of AOIs in walking distance to the prices of the houses. We expected that if a house has a lot of AOIs in walking distance its price would be higher. Also, the less AOIs a house has, the lower its price would be.\n",
    "\n",
    "In the first step of the project we worked on the number of AOIs in walking distance (1 km) from Airbnb houses. Also, we assumed that this was a normal distribution.\n",
    "\n",
    "**Null Hypothesis (H₀):** Number of AOIs in walking distance affects the prices of Airbnb houses.\n",
    "\n",
    "**Alternative Hypothesis (Hᴬ):** Number of AOIs in walking distance does not eaffect the prices of Airbnb houses.\n",
    "\n",
    "**Significance level:** As most of hypothesis tests assume significance level as 0.05, we are setting it as 0.05 for our test too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48895.000000\n",
       "mean         0.973944\n",
       "std          0.987971\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          1.000000\n",
       "75%          1.000000\n",
       "max          4.000000\n",
       "Name: number_aoi_df, dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detailed information on AOIs\n",
    "\n",
    "df2['number_aoi_df'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the maximum number of AOIs in walking distance from an Airbnb house is 4.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d863ba340>"
      ]
     },
     "execution_count": 151,
     "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": [
    "# In the first step of our project we found the number of AOIs in walking distance and added them to our manipulated data under 'number_aoi_df'\n",
    "\n",
    "graph6 = pd.crosstab(index=[df2['price']], columns= df2['number_aoi_df'])\n",
    "graph6.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expected that if a house has more AOIs in walking distance, the prices of the houses would be higher. However, from this graph we can note that number of AOIs do not really affect the price since we can see that there are not many AOIs in walking distance on the expensive side of the graph. Here we can observe that the number of AOIs in walking distance has no correlation with the prices. But we still wanted to test it to comfirm our outcome from this graph.\n",
    "\n",
    "Since we had catagorized the price before we decided to use Chi 2 test again.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>number_aoi_df</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</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>a. 0-50</th>\n",
       "      <td>2112</td>\n",
       "      <td>2075</td>\n",
       "      <td>718</td>\n",
       "      <td>110</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b. 51-100</th>\n",
       "      <td>6549</td>\n",
       "      <td>7014</td>\n",
       "      <td>2603</td>\n",
       "      <td>477</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c. 101-150</th>\n",
       "      <td>3712</td>\n",
       "      <td>3672</td>\n",
       "      <td>1846</td>\n",
       "      <td>507</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d. 151-200</th>\n",
       "      <td>2543</td>\n",
       "      <td>2530</td>\n",
       "      <td>1429</td>\n",
       "      <td>404</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e. 201-300</th>\n",
       "      <td>2025</td>\n",
       "      <td>1895</td>\n",
       "      <td>1276</td>\n",
       "      <td>365</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f. 301-400</th>\n",
       "      <td>667</td>\n",
       "      <td>566</td>\n",
       "      <td>438</td>\n",
       "      <td>106</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g. 401-500</th>\n",
       "      <td>246</td>\n",
       "      <td>237</td>\n",
       "      <td>183</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h. 500-10.000</th>\n",
       "      <td>462</td>\n",
       "      <td>371</td>\n",
       "      <td>280</td>\n",
       "      <td>56</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "number_aoi_df   0.0   1.0   2.0  3.0  4.0\n",
       "price                                    \n",
       "a. 0-50        2112  2075   718  110   12\n",
       "b. 51-100      6549  7014  2603  477  207\n",
       "c. 101-150     3712  3672  1846  507  296\n",
       "d. 151-200     2543  2530  1429  404  294\n",
       "e. 201-300     2025  1895  1276  365  306\n",
       "f. 301-400      667   566   438  106  153\n",
       "g. 401-500      246   237   183   44   43\n",
       "h. 500-10.000   462   371   280   56   66"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq1 = pd.crosstab(index=[df2['price']], columns= df2['number_aoi_df'])\n",
    "freq1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test t=1398.495688 p-value=0.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1398.4956878700468,\n",
       " 3.2702619185448935e-277,\n",
       " 28,\n",
       " array([[1883.10731159, 1887.63104612,  901.97097863,  212.71833521,\n",
       "          141.57232846],\n",
       "        [6311.98691073, 6327.15001534, 3023.31629001,  713.01053277,\n",
       "          474.53625115],\n",
       "        [3758.34805195, 3767.37662338, 1800.17402597,  424.54805195,\n",
       "          282.55324675],\n",
       "        [2697.11013396, 2703.58932406, 1291.86215359,  304.66918908,\n",
       "          202.7691993 ],\n",
       "        [2197.77016055, 2203.04980059, 1052.68822988,  248.26307393,\n",
       "          165.22873504],\n",
       "        [ 722.97535535,  724.71213826,  346.29082728,   81.66826874,\n",
       "           54.35341037],\n",
       "        [ 282.07276818,  282.75038347,  135.10725023,   31.86331936,\n",
       "           21.20627876],\n",
       "        [ 462.6293077 ,  463.74066878,  221.5902444 ,   52.25922896,\n",
       "           34.78055016]]))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_stats1 = stats.chi2_contingency(freq1)\n",
    "print ('Test t=%f p-value=%f' % (chi_stats1[0], chi_stats1[1]))\n",
    "chi_stats1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.33713815142739"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critical_value1 = crit1 = stats.chi2.ppf( q = 1 - alpha,\n",
    "                                     df= chi_stats1[2])\n",
    "critical_value1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1398.4956878700468"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_chi_val1 = chi_stats1[0]\n",
    "obs_chi_val1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject null hypothesis!\n"
     ]
    }
   ],
   "source": [
    "if obs_chi_val1 <= critical_value1:\n",
    "    print('Null hpothesis cannot be rejected!')\n",
    "else:\n",
    "    print('We reject null hypothesis!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying Chi 2 test, we can see that the p-value we obtained is a lot smaller than the threshold significance level 0.05, we can conclude that the number of AOIs in walking distance does not affect the prices. Here, we reject the null hypothesis which means that the alternative hypothesis is true and the number of AOIs in walking distance and prices have no correlation.\n",
    "\n",
    "Although we guessed this outcome from the graph, we comfirmed our guess by applying the test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AOIs in Walking Distance to the Houses in Neighborhoods\n",
    "\n",
    "We wanted to observe the effect of AOIs more and after seeing our test above, we wondered if AOIs effected anything in neighbourhoods so we wanted to test it for the neighbourhoods since some neighbourhoods might be more preferred by tourists.\n",
    "\n",
    "We expected that if a neighbourhood has more houses that has a lot of AOIs in walking distance the prices in that neighbourhood  would be higher. Also, if a neighbourhood has more houses that has less AOIs in walking distance the prices in that neighbourhood would be lower.\n",
    "\n",
    "In the first step of the project we worked on the number of AOIs in walking distance (1 km) from Airbnb houses. Also, we assumed that this was a normal distribution.\n",
    "\n",
    "**Null Hypothesis (H₀):** The number of AOIs in walking distance in a neighbourhood affects the prices of Airbnb houses.\n",
    "\n",
    "**Alternative Hypothesis (Hᴬ):** The number of AOIs in walking distance in a neighbourhood does not affect the prices of Airbnb houses.\n",
    "\n",
    "\n",
    "**Significance level:** As most of hypothesis tests assume significance level as 0.05, we are setting it as 0.05 for our test too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d86424430>"
      ]
     },
     "execution_count": 153,
     "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": [
    "graph7 = pd.crosstab(index=[df2['number_aoi_df']], columns= df2['neighbourhood_group'])\n",
    "graph7.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtained similar results as the relation graph we did for the seperate houses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>neighbourhood_group</th>\n",
       "      <th>Bronx</th>\n",
       "      <th>Brooklyn</th>\n",
       "      <th>Manhattan</th>\n",
       "      <th>Queens</th>\n",
       "      <th>Staten Island</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <th>number_aoi_df</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 rowspan=\"5\" valign=\"top\">a. 0-50</th>\n",
       "      <th>0.0</th>\n",
       "      <td>137</td>\n",
       "      <td>1457</td>\n",
       "      <td>208</td>\n",
       "      <td>253</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>129</td>\n",
       "      <td>1096</td>\n",
       "      <td>350</td>\n",
       "      <td>475</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>24</td>\n",
       "      <td>185</td>\n",
       "      <td>146</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">b. 51-100</th>\n",
       "      <th>0.0</th>\n",
       "      <td>228</td>\n",
       "      <td>4066</td>\n",
       "      <td>1482</td>\n",
       "      <td>679</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>198</td>\n",
       "      <td>3384</td>\n",
       "      <td>2276</td>\n",
       "      <td>1103</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>69</td>\n",
       "      <td>666</td>\n",
       "      <td>1106</td>\n",
       "      <td>747</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>8</td>\n",
       "      <td>37</td>\n",
       "      <td>251</td>\n",
       "      <td>179</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">c. 101-150</th>\n",
       "      <th>0.0</th>\n",
       "      <td>66</td>\n",
       "      <td>2026</td>\n",
       "      <td>1345</td>\n",
       "      <td>236</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>63</td>\n",
       "      <td>1666</td>\n",
       "      <td>1551</td>\n",
       "      <td>368</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>30</td>\n",
       "      <td>408</td>\n",
       "      <td>1155</td>\n",
       "      <td>251</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>383</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">d. 151-200</th>\n",
       "      <th>0.0</th>\n",
       "      <td>21</td>\n",
       "      <td>1182</td>\n",
       "      <td>1225</td>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>20</td>\n",
       "      <td>1118</td>\n",
       "      <td>1199</td>\n",
       "      <td>183</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>4</td>\n",
       "      <td>247</td>\n",
       "      <td>1061</td>\n",
       "      <td>115</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>368</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">e. 201-300</th>\n",
       "      <th>0.0</th>\n",
       "      <td>9</td>\n",
       "      <td>710</td>\n",
       "      <td>1203</td>\n",
       "      <td>88</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>13</td>\n",
       "      <td>754</td>\n",
       "      <td>1015</td>\n",
       "      <td>109</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>3</td>\n",
       "      <td>149</td>\n",
       "      <td>1062</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>305</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">f. 301-400</th>\n",
       "      <th>0.0</th>\n",
       "      <td>8</td>\n",
       "      <td>204</td>\n",
       "      <td>427</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>5</td>\n",
       "      <td>213</td>\n",
       "      <td>323</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>379</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">g. 401-500</th>\n",
       "      <th>0.0</th>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "      <td>162</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>142</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>160</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">h. 500-10.000</th>\n",
       "      <th>0.0</th>\n",
       "      <td>4</td>\n",
       "      <td>137</td>\n",
       "      <td>306</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>5</td>\n",
       "      <td>107</td>\n",
       "      <td>242</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>237</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "neighbourhood_group          Bronx  Brooklyn  Manhattan  Queens  Staten Island\n",
       "price         number_aoi_df                                                   \n",
       "a. 0-50       0.0              137      1457        208     253             57\n",
       "              1.0              129      1096        350     475             25\n",
       "              2.0               24       185        146     361              2\n",
       "              3.0               18        10         13      68              1\n",
       "              4.0                6         1          5       0              0\n",
       "b. 51-100     0.0              228      4066       1482     679             94\n",
       "              1.0              198      3384       2276    1103             53\n",
       "              2.0               69       666       1106     747             15\n",
       "              3.0                8        37        251     179              2\n",
       "              4.0                6         2        199       0              0\n",
       "c. 101-150    0.0               66      2026       1345     236             39\n",
       "              1.0               63      1666       1551     368             24\n",
       "              2.0               30       408       1155     251              2\n",
       "              3.0                5        15        383     103              1\n",
       "              4.0                3         3        290       0              0\n",
       "d. 151-200    0.0               21      1182       1225     100             15\n",
       "              1.0               20      1118       1199     183             10\n",
       "              2.0                4       247       1061     115              2\n",
       "              3.0                0         2        368      34              0\n",
       "              4.0                2         0        292       0              0\n",
       "e. 201-300    0.0                9       710       1203      88             15\n",
       "              1.0               13       754       1015     109              4\n",
       "              2.0                3       149       1062      61              1\n",
       "              3.0                0         7        336      22              0\n",
       "              4.0                1         0        305       0              0\n",
       "f. 301-400    0.0                8       204        427      28              0\n",
       "              1.0                5       213        323      22              3\n",
       "              2.0                0        45        379      14              0\n",
       "              3.0                1         0        101       4              0\n",
       "              4.0                0         0        153       0              0\n",
       "g. 401-500    0.0                3        70        162      10              1\n",
       "              1.0                1        84        142       9              1\n",
       "              2.0                0        17        160       6              0\n",
       "              3.0                1         1         40       2              0\n",
       "              4.0                0         0         43       0              0\n",
       "h. 500-10.000 0.0                4       137        306      14              1\n",
       "              1.0                5       107        242      14              3\n",
       "              2.0                0        35        237       6              2\n",
       "              3.0                0         0         54       2              0\n",
       "              4.0                0         0         66       0              0"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq7 = pd.crosstab(index=[df2['price'], df2['number_aoi_df']], columns= df2['neighbourhood_group'])\n",
    "freq7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It was difficult to make a clear assumption by looking at this table since we have visualized more data than we did before so we could not make clear assumptions. However, because of the graph above we guessed that there would be no correlation again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test t=13367.440579 p-value=0.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(13367.440578967067,\n",
       " 0.0,\n",
       " 156,\n",
       " array([[4.71253093e+01, 8.68384252e+02, 9.35638245e+02, 2.44740607e+02,\n",
       "         1.61115861e+01],\n",
       "        [4.62997239e+01, 8.53171081e+02, 9.19246856e+02, 2.40453012e+02,\n",
       "         1.58293282e+01],\n",
       "        [1.60208201e+01, 2.95217752e+02, 3.18081563e+02, 8.32025360e+01,\n",
       "         5.47732897e+00],\n",
       "        [2.45444319e+00, 4.52283465e+01, 4.87311586e+01, 1.27469066e+01,\n",
       "         8.39145107e-01],\n",
       "        [2.67757439e-01, 4.93400143e+00, 5.31612639e+00, 1.39057163e+00,\n",
       "         9.15431026e-02],\n",
       "        [1.46128623e+02, 2.69273128e+03, 2.90127598e+03, 7.58904469e+02,\n",
       "         4.99596482e+01],\n",
       "        [1.56504223e+02, 2.88392384e+03, 3.10727588e+03, 8.12789120e+02,\n",
       "         5.35069435e+01],\n",
       "        [5.80810512e+01, 1.07026714e+03, 1.15315642e+03, 3.01638163e+02,\n",
       "         1.98572247e+01],\n",
       "        [1.06433582e+01, 1.96126557e+02, 2.11316024e+02, 5.52752224e+01,\n",
       "         3.63883833e+00],\n",
       "        [4.61881583e+00, 8.51115247e+01, 9.17031803e+01, 2.39873607e+01,\n",
       "         1.57911852e+00],\n",
       "        [8.28263013e+01, 1.52625111e+03, 1.64445510e+03, 4.30150159e+02,\n",
       "         2.83173331e+01],\n",
       "        [8.19337765e+01, 1.50980444e+03, 1.62673468e+03, 4.25514920e+02,\n",
       "         2.80121894e+01],\n",
       "        [4.11900194e+01, 7.59013887e+02, 8.17797444e+02, 2.13916270e+02,\n",
       "         1.40823806e+01],\n",
       "        [1.13127518e+01, 2.08461560e+02, 2.24606340e+02, 5.87516515e+01,\n",
       "         3.86769608e+00],\n",
       "        [6.60468351e+00, 1.21705369e+02, 1.31131118e+02, 3.43007669e+01,\n",
       "         2.25806320e+00],\n",
       "        [5.67422640e+01, 1.04559714e+03, 1.12657578e+03, 2.94685305e+02,\n",
       "         1.93995092e+01],\n",
       "        [5.64521935e+01, 1.04025197e+03, 1.12081665e+03, 2.93178853e+02,\n",
       "         1.93003375e+01],\n",
       "        [3.18854484e+01, 5.87557337e+02, 6.33062051e+02, 1.65593905e+02,\n",
       "         1.09012578e+01],\n",
       "        [9.01450046e+00, 1.66111382e+02, 1.78976255e+02, 4.68159116e+01,\n",
       "         3.08195112e+00],\n",
       "        [6.56005727e+00, 1.20883035e+02, 1.30245097e+02, 3.40690050e+01,\n",
       "         2.24280601e+00],\n",
       "        [4.51840679e+01, 8.32612742e+02, 8.97096329e+02, 2.34658963e+02,\n",
       "         1.54478986e+01],\n",
       "        [4.22833623e+01, 7.79161059e+02, 8.39504960e+02, 2.19594437e+02,\n",
       "         1.44561816e+01],\n",
       "        [2.84715411e+01, 5.24648819e+02, 5.65281440e+02, 1.47864117e+02,\n",
       "         9.73408324e+00],\n",
       "        [8.14428878e+00, 1.50075877e+02, 1.61698844e+02, 4.22965538e+01,\n",
       "         2.78443604e+00],\n",
       "        [6.82781470e+00, 1.25817037e+02, 1.35561223e+02, 3.54595766e+01,\n",
       "         2.33434912e+00],\n",
       "        [1.48828510e+01, 2.74248246e+02, 2.95488025e+02, 7.72926066e+01,\n",
       "         5.08827078e+00],\n",
       "        [1.26292259e+01, 2.32720401e+02, 2.50743962e+02, 6.55886287e+01,\n",
       "         4.31778300e+00],\n",
       "        [9.77314654e+00, 1.80091052e+02, 1.94038613e+02, 5.07558646e+01,\n",
       "         3.34132324e+00],\n",
       "        [2.36519071e+00, 4.35836793e+01, 4.69591165e+01, 1.22833828e+01,\n",
       "         8.08630739e-01],\n",
       "        [3.41390735e+00, 6.29085183e+01, 6.77806115e+01, 1.77297883e+01,\n",
       "         1.16717456e+00],\n",
       "        [5.48902751e+00, 1.01147029e+02, 1.08980591e+02, 2.85067185e+01,\n",
       "         1.87663360e+00],\n",
       "        [5.28820943e+00, 9.74465283e+01, 1.04993496e+02, 2.74637898e+01,\n",
       "         1.80797628e+00],\n",
       "        [4.08330095e+00, 7.52435218e+01, 8.10709275e+01, 2.12062174e+01,\n",
       "         1.39603231e+00],\n",
       "        [9.81777278e-01, 1.80913386e+01, 1.94924634e+01, 5.09876265e+00,\n",
       "         3.35658043e-01],\n",
       "        [9.59464158e-01, 1.76801718e+01, 1.90494529e+01, 4.98288169e+00,\n",
       "         3.28029451e-01],\n",
       "        [1.03086614e+01, 1.89959055e+02, 2.04670866e+02, 5.35370079e+01,\n",
       "         3.52440945e+00],\n",
       "        [8.27816750e+00, 1.52542878e+02, 1.64356908e+02, 4.29918397e+01,\n",
       "         2.83020759e+00],\n",
       "        [6.24767359e+00, 1.15126700e+02, 1.24042949e+02, 3.24466714e+01,\n",
       "         2.13600573e+00],\n",
       "        [1.24953472e+00, 2.30253400e+01, 2.48085898e+01, 6.48933429e+00,\n",
       "         4.27201145e-01],\n",
       "        [1.47266592e+00, 2.71370079e+01, 2.92386952e+01, 7.64814398e+00,\n",
       "         5.03487064e-01]]))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_stats7 = stats.chi2_contingency(freq7)\n",
    "print ('Test t=%f p-value=%f' % (chi_stats7[0], chi_stats7[1]))\n",
    "chi_stats7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186.14575078141743"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critical_value7 = crit7 = stats.chi2.ppf( q = 1 - alpha,\n",
    "                                     df= chi_stats7[2])\n",
    "critical_value7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13367.440578967067"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_chi_val7 = chi_stats7[0]\n",
    "obs_chi_val7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject null hypothesis!\n"
     ]
    }
   ],
   "source": [
    "if obs_chi_val7 <= critical_value7:\n",
    "    print('Null hpothesis cannot be rejected!')\n",
    "else:\n",
    "    print('We reject null hypothesis!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying Chi 2 test, we can see that the p-value we obtained is a lot smaller than the threshold significance level 0.05, we can conclude that the number of AOIs in walking distance in neighbourhoods does not affect the prices. Here, we reject the null hypothesis which means that the alternative hypothesis is true and the number of AOIs in walking distance in neighbourhoods and prices have no correlation.\n",
    "\n",
    "Although we guessed this outcome from the graph, we comfirmed our guess by applying the test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "While doing our project we expected that at least one of the features we had observed would have correlation and we could comment on the effects of pricing of the Airbnb houses in New York. However none of our assumptions were true so we could not find a correlation between the prices and number of room types/number of subway stations in walking distance/ crimes in the neighbourhoods/number of subway stations in walking distance in each neighbourhood/ number of AOIs in walking distance/ number of AOIs in walking distance in each neighbourhood. So we do not know what exactly affects the pricing distribution in Airbnb houses in New York."
   ]
  }
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