FirstQuestion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Based on the room type-one has to find the prices in different neighbourhood_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\svlsrao\\\\Documents\\\\hack\\\\Hackatho'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34226 entries, 0 to 34225\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              34226 non-null  int64  \n",
      " 1   name                            34214 non-null  object \n",
      " 2   host_id                         34226 non-null  int64  \n",
      " 3   host_name                       34213 non-null  object \n",
      " 4   neighbourhood_group             34226 non-null  object \n",
      " 5   neighbourhood                   34226 non-null  object \n",
      " 6   latitude                        34226 non-null  float64\n",
      " 7   longitude                       34226 non-null  float64\n",
      " 8   room_type                       34226 non-null  object \n",
      " 9   price                           34226 non-null  int64  \n",
      " 10  minimum_nights                  34226 non-null  int64  \n",
      " 11  number_of_reviews               34226 non-null  int64  \n",
      " 12  last_review                     27244 non-null  object \n",
      " 13  reviews_per_month               27244 non-null  float64\n",
      " 14  calculated_host_listings_count  34226 non-null  int64  \n",
      " 15  availability_365                34226 non-null  int64  \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "path='C:\\\\Users\\\\svlsrao\\\\Documents\\\\First-Hackathon/file/Train.csv'\n",
    "data=pd.read_csv(path)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the unique type of rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Private room', 'Entire home/apt', 'Shared room'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['room_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dataframes containing only Private room, Entire home/apt and Shared room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr=data.loc[data['room_type']=='Private room']\n",
    "home=data.loc[data['room_type']=='Entire home/apt']\n",
    "Sr=data.loc[data['room_type']=='Shared room']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34226, 16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15594, 16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17829, 16)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(803, 16)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# checking unique neighbourhood_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Manhattan', 'Brooklyn', 'Queens', 'Staten Island', 'Bronx'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr['neighbourhood_group'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# description of price based on the grouping by neighbourhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>40.0</td>\n",
       "      <td>63.550000</td>\n",
       "      <td>123.463677</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>272.0</td>\n",
       "      <td>52.257353</td>\n",
       "      <td>60.448813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>340.0</td>\n",
       "      <td>88.429412</td>\n",
       "      <td>91.683136</td>\n",
       "      <td>10.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>89.25</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>146.0</td>\n",
       "      <td>73.767123</td>\n",
       "      <td>194.867383</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>50.75</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>5.0</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>57.743398</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count       mean         std   min   25%   50%    75%  \\\n",
       "neighbourhood_group                                                          \n",
       "Bronx                 40.0  63.550000  123.463677  20.0  27.5  30.0  52.00   \n",
       "Brooklyn             272.0  52.257353   60.448813   0.0  30.0  36.0  47.00   \n",
       "Manhattan            340.0  88.429412   91.683136  10.0  50.0  69.0  89.25   \n",
       "Queens               146.0  73.767123  194.867383  11.0  30.0  37.5  50.75   \n",
       "Staten Island          5.0  63.400000   57.743398  13.0  29.0  30.0  95.00   \n",
       "\n",
       "                        max  \n",
       "neighbourhood_group          \n",
       "Bronx                 800.0  \n",
       "Brooklyn              725.0  \n",
       "Manhattan            1000.0  \n",
       "Queens               1800.0  \n",
       "Staten Island         150.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.groupby(pr['neighbourhood_group']).price.describe()\n",
    "home.groupby(home['neighbourhood_group']).price.describe()\n",
    "Sr.groupby(Sr['neighbourhood_group']).price.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data variation\n",
    "    - Lot of outliers are present in the prices \n",
    "    - Mean is greater than median \n",
    "    - we can remove the outliers from the data and implement the regression to predict the prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5728806</td>\n",
       "      <td>Large private room in Nolita</td>\n",
       "      <td>4271676</td>\n",
       "      <td>Nat</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Nolita</td>\n",
       "      <td>40.72217</td>\n",
       "      <td>-73.99481</td>\n",
       "      <td>Private room</td>\n",
       "      <td>120</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4823959</td>\n",
       "      <td>Private room for 2 in cool BK apt</td>\n",
       "      <td>24633966</td>\n",
       "      <td>Phil</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64469</td>\n",
       "      <td>-73.97086</td>\n",
       "      <td>Private room</td>\n",
       "      <td>50</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20817201</td>\n",
       "      <td>Private Room in Trendy Williamsburg Loft</td>\n",
       "      <td>643120</td>\n",
       "      <td>Sam</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>40.71591</td>\n",
       "      <td>-73.95513</td>\n",
       "      <td>Private room</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>2.53</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5040218</td>\n",
       "      <td>LUX Blg-Prime Area! Lg PRIVATE RM &amp; BATH w/VIEWS!</td>\n",
       "      <td>26019828</td>\n",
       "      <td>Sonia</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>40.76159</td>\n",
       "      <td>-73.99824</td>\n",
       "      <td>Private room</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17224372</td>\n",
       "      <td>Friendly Room 20-202</td>\n",
       "      <td>115993835</td>\n",
       "      <td>Shimin</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Sunset Park</td>\n",
       "      <td>40.63992</td>\n",
       "      <td>-74.00760</td>\n",
       "      <td>Private room</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.08</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                               name    host_id  \\\n",
       "0    5728806                       Large private room in Nolita    4271676   \n",
       "10   4823959                  Private room for 2 in cool BK apt   24633966   \n",
       "11  20817201           Private Room in Trendy Williamsburg Loft     643120   \n",
       "14   5040218  LUX Blg-Prime Area! Lg PRIVATE RM & BATH w/VIEWS!   26019828   \n",
       "20  17224372                               Friendly Room 20-202  115993835   \n",
       "\n",
       "   host_name neighbourhood_group   neighbourhood  latitude  longitude  \\\n",
       "0        Nat           Manhattan          Nolita  40.72217  -73.99481   \n",
       "10      Phil            Brooklyn      Kensington  40.64469  -73.97086   \n",
       "11       Sam            Brooklyn    Williamsburg  40.71591  -73.95513   \n",
       "14     Sonia           Manhattan  Hell's Kitchen  40.76159  -73.99824   \n",
       "20    Shimin            Brooklyn     Sunset Park  40.63992  -74.00760   \n",
       "\n",
       "       room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0   Private room    120               7                  3  2015-09-01   \n",
       "10  Private room     50              14                  0         NaN   \n",
       "11  Private room     50               4                 56  2019-06-20   \n",
       "14  Private room     69               2                 22  2019-03-20   \n",
       "20  Private room     28               1                 31  2019-03-10   \n",
       "\n",
       "    reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0                0.06                               3                 0  \n",
       "10                NaN                               1                 0  \n",
       "11               2.53                               4                 8  \n",
       "14               0.64                               2                 7  \n",
       "20               1.08                               5                 1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now plot the average price of the individual room type in different neighbourhood_groups\n",
    "Since the difference of median and mean prices for each category was large so the average prices are influenced mainly due to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price ($/night)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(pr['neighbourhood_group'],pr['price'])\n",
    "plt.title('Average price of a private room per night')\n",
    "plt.xlabel('Different Areas in Newyork City')\n",
    "plt.ylabel('Price ($/night)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box plot for each room_type and different neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "#Here we have restricted the y-axis between 0 and 300 \n",
    "plt.ylim(-30,300)\n",
    "sns.boxplot(x='neighbourhood_group',y='price',data=pr)\n",
    "plt\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>459.0</td>\n",
       "      <td>67.337691</td>\n",
       "      <td>120.472529</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>7050.0</td>\n",
       "      <td>76.117872</td>\n",
       "      <td>111.853173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>5602.0</td>\n",
       "      <td>117.323456</td>\n",
       "      <td>190.916733</td>\n",
       "      <td>10.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>2352.0</td>\n",
       "      <td>68.716412</td>\n",
       "      <td>59.525037</td>\n",
       "      <td>10.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>131.0</td>\n",
       "      <td>61.198473</td>\n",
       "      <td>38.050052</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count        mean         std   min   25%   50%    75%  \\\n",
       "neighbourhood_group                                                            \n",
       "Bronx                 459.0   67.337691  120.472529  21.0  40.0  53.0   73.0   \n",
       "Brooklyn             7050.0   76.117872  111.853173   0.0  50.0  64.0   80.0   \n",
       "Manhattan            5602.0  117.323456  190.916733  10.0  67.0  90.0  120.0   \n",
       "Queens               2352.0   68.716412   59.525037  10.0  48.0  60.0   75.0   \n",
       "Staten Island         131.0   61.198473   38.050052  20.0  40.0  50.0   71.5   \n",
       "\n",
       "                        max  \n",
       "neighbourhood_group          \n",
       "Bronx                2500.0  \n",
       "Brooklyn             5000.0  \n",
       "Manhattan            9999.0  \n",
       "Queens               2000.0  \n",
       "Staten Island         300.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean, median and different quartiles and outliers  for private room in different neighbourhood \n",
    "pr.groupby(pr['neighbourhood_group']).price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "#Here we have restricted the y-axis between 0 and 500 \n",
    "plt.ylim(0,800)\n",
    "sns.boxplot(x='neighbourhood_group',y='price',data=home)\n",
    "plt\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>268.0</td>\n",
       "      <td>127.455224</td>\n",
       "      <td>101.120399</td>\n",
       "      <td>28.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>6711.0</td>\n",
       "      <td>181.116525</td>\n",
       "      <td>251.806081</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>9251.0</td>\n",
       "      <td>249.621554</td>\n",
       "      <td>338.568558</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>1487.0</td>\n",
       "      <td>148.470746</td>\n",
       "      <td>135.556613</td>\n",
       "      <td>10.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>112.0</td>\n",
       "      <td>187.964286</td>\n",
       "      <td>478.771399</td>\n",
       "      <td>48.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>101.5</td>\n",
       "      <td>175.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count        mean         std   min    25%    50%  \\\n",
       "neighbourhood_group                                                       \n",
       "Bronx                 268.0  127.455224  101.120399  28.0   79.0  100.0   \n",
       "Brooklyn             6711.0  181.116525  251.806081   0.0  105.0  146.0   \n",
       "Manhattan            9251.0  249.621554  338.568558   0.0  143.0  190.0   \n",
       "Queens               1487.0  148.470746  135.556613  10.0   94.0  120.0   \n",
       "Staten Island         112.0  187.964286  478.771399  48.0   75.0  101.5   \n",
       "\n",
       "                       75%      max  \n",
       "neighbourhood_group                  \n",
       "Bronx                139.0   1000.0  \n",
       "Brooklyn             199.0  10000.0  \n",
       "Manhattan            255.0  10000.0  \n",
       "Queens               170.0   2600.0  \n",
       "Staten Island        175.0   5000.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean, median and different quartiles and outliers  for private room in different neighbourhood \n",
    "home.groupby(home['neighbourhood_group']).price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "#Here we have restricted the y-axis between 0 and 300 \n",
    "plt.ylim(0,300)\n",
    "sns.boxplot(x='neighbourhood_group',y='price',data=Sr)\n",
    "plt\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>40.0</td>\n",
       "      <td>63.550000</td>\n",
       "      <td>123.463677</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>272.0</td>\n",
       "      <td>52.257353</td>\n",
       "      <td>60.448813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>340.0</td>\n",
       "      <td>88.429412</td>\n",
       "      <td>91.683136</td>\n",
       "      <td>10.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>89.25</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>146.0</td>\n",
       "      <td>73.767123</td>\n",
       "      <td>194.867383</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>50.75</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>5.0</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>57.743398</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count       mean         std   min   25%   50%    75%  \\\n",
       "neighbourhood_group                                                          \n",
       "Bronx                 40.0  63.550000  123.463677  20.0  27.5  30.0  52.00   \n",
       "Brooklyn             272.0  52.257353   60.448813   0.0  30.0  36.0  47.00   \n",
       "Manhattan            340.0  88.429412   91.683136  10.0  50.0  69.0  89.25   \n",
       "Queens               146.0  73.767123  194.867383  11.0  30.0  37.5  50.75   \n",
       "Staten Island          5.0  63.400000   57.743398  13.0  29.0  30.0  95.00   \n",
       "\n",
       "                        max  \n",
       "neighbourhood_group          \n",
       "Bronx                 800.0  \n",
       "Brooklyn              725.0  \n",
       "Manhattan            1000.0  \n",
       "Queens               1800.0  \n",
       "Staten Island         150.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean, median and different quartiles and outliers  for private room in different neighbourhood \n",
    "Sr.groupby(Sr['neighbourhood_group']).price.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we have seen zero price properties in the brooklyn neighbourhood only so those properties can also be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29906</th>\n",
       "      <td>11234747</td>\n",
       "      <td>Mins away to Manhattan Suite Residence</td>\n",
       "      <td>24146326</td>\n",
       "      <td>Julien</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>40.76626</td>\n",
       "      <td>-73.93054</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>1800</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                    name   host_id host_name  \\\n",
       "29906  11234747  Mins away to Manhattan Suite Residence  24146326    Julien   \n",
       "\n",
       "      neighbourhood_group neighbourhood  latitude  longitude    room_type  \\\n",
       "29906              Queens       Astoria  40.76626  -73.93054  Shared room   \n",
       "\n",
       "       price  minimum_nights  number_of_reviews last_review  \\\n",
       "29906   1800               3                  5  2017-04-09   \n",
       "\n",
       "       reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "29906               0.13                               2                90  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_position=Sr[['price']].idxmax()\n",
    "\n",
    "#pr.columns\n",
    "\n",
    "Sr.head()\n",
    "#va=pr['name'][var_position]\n",
    "#pr['name'][var_position]\n",
    "#pr.head(20)\n",
    "df=Sr[Sr.price == Sr.price.max()]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similar plots for Entire home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price ($/night)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(home['neighbourhood_group'],home['price'])\n",
    "plt.title('Average price of a Entire home/Apartment per night')\n",
    "plt.xlabel('Different Areas in Newyork City')\n",
    "plt.ylabel('Price ($/night)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price ($/night)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(Sr['neighbourhood_group'],Sr['price'])\n",
    "plt.title('Average price of a Shared room per night')\n",
    "plt.xlabel('Different Areas in Newyork City')\n",
    "plt.ylabel('Price ($/night)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.400000e+02</td>\n",
       "      <td>3.400000e+02</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>257.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>340.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.149793e+07</td>\n",
       "      <td>1.001471e+08</td>\n",
       "      <td>40.769129</td>\n",
       "      <td>-73.972632</td>\n",
       "      <td>88.429412</td>\n",
       "      <td>7.570588</td>\n",
       "      <td>21.326471</td>\n",
       "      <td>1.779961</td>\n",
       "      <td>3.688235</td>\n",
       "      <td>141.591176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.094318e+07</td>\n",
       "      <td>9.568637e+07</td>\n",
       "      <td>0.037006</td>\n",
       "      <td>0.020992</td>\n",
       "      <td>91.683136</td>\n",
       "      <td>55.320768</td>\n",
       "      <td>37.212567</td>\n",
       "      <td>1.734454</td>\n",
       "      <td>5.200111</td>\n",
       "      <td>145.946410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.204800e+04</td>\n",
       "      <td>7.549000e+03</td>\n",
       "      <td>40.703720</td>\n",
       "      <td>-74.015640</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.199484e+07</td>\n",
       "      <td>1.860110e+07</td>\n",
       "      <td>40.743913</td>\n",
       "      <td>-73.988642</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.301327e+07</td>\n",
       "      <td>4.510284e+07</td>\n",
       "      <td>40.765430</td>\n",
       "      <td>-73.980935</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>1.310000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.121772e+07</td>\n",
       "      <td>1.920155e+08</td>\n",
       "      <td>40.793650</td>\n",
       "      <td>-73.953052</td>\n",
       "      <td>89.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>320.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.648561e+07</td>\n",
       "      <td>2.724777e+08</td>\n",
       "      <td>40.869310</td>\n",
       "      <td>-73.916820</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>9.120000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id       host_id    latitude   longitude        price  \\\n",
       "count  3.400000e+02  3.400000e+02  340.000000  340.000000   340.000000   \n",
       "mean   2.149793e+07  1.001471e+08   40.769129  -73.972632    88.429412   \n",
       "std    1.094318e+07  9.568637e+07    0.037006    0.020992    91.683136   \n",
       "min    1.204800e+04  7.549000e+03   40.703720  -74.015640    10.000000   \n",
       "25%    1.199484e+07  1.860110e+07   40.743913  -73.988642    50.000000   \n",
       "50%    2.301327e+07  4.510284e+07   40.765430  -73.980935    69.000000   \n",
       "75%    3.121772e+07  1.920155e+08   40.793650  -73.953052    89.250000   \n",
       "max    3.648561e+07  2.724777e+08   40.869310  -73.916820  1000.000000   \n",
       "\n",
       "       minimum_nights  number_of_reviews  reviews_per_month  \\\n",
       "count      340.000000         340.000000         257.000000   \n",
       "mean         7.570588          21.326471           1.779961   \n",
       "std         55.320768          37.212567           1.734454   \n",
       "min          1.000000           0.000000           0.020000   \n",
       "25%          1.000000           1.000000           0.380000   \n",
       "50%          1.000000           5.500000           1.310000   \n",
       "75%          2.000000          23.000000           2.700000   \n",
       "max        999.000000         229.000000           9.120000   \n",
       "\n",
       "       calculated_host_listings_count  availability_365  \n",
       "count                      340.000000        340.000000  \n",
       "mean                         3.688235        141.591176  \n",
       "std                          5.200111        145.946410  \n",
       "min                          1.000000          0.000000  \n",
       "25%                          1.000000          0.000000  \n",
       "50%                          2.000000         83.000000  \n",
       "75%                          4.000000        320.000000  \n",
       "max                         28.000000        365.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1=Sr.loc[Sr['neighbourhood_group']=='Manhattan']\n",
    "s1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1=data.groupby([data['room_type'],data['neighbourhood_group']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.barplot(p1['neighbourhood_group'],p1['price'])"
   ]
  }
 ],
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