air_listings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7db14958-dfed-4740-a120-b1a8fbd096d5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Preparation of Airbnb Data from Maven Analystics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "868533a3-3051-4fa4-9f8d-a270452dc690",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b513124e-839d-4d1b-bae3-ed45038926a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbcc3fd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4512bec5-6c49-467c-9367-a4dbde659dde",
   "metadata": {
    "tags": []
   },
   "source": [
    "### set missing value conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d73bfefb-33fa-4986-8082-ef577e4d29ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "na_vals = ['NA', 'missing']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6c573f-015b-46b1-a173-d37745cadee8",
   "metadata": {},
   "source": [
    "### Import File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12e08f6f-04f4-4a42-b9a6-ce05d453bf83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set dtype for two cols because of error, set encoding to Latin becuase of latin characters\n",
    "# PC\n",
    "# air_df = pd.read_csv('C:/Users/conif/Documents/Airbnb_Data/Airbnb Data/Listings.csv', na_values = na_vals,dtype={\"host_response_time\": \"string\", \"district\": \"string\"}, encoding='latin1')\n",
    "# Linux\n",
    "air_df = pd.read_csv('/home/raf/Documents/Airbnb Data/Listings.csv', na_values = na_vals,dtype={\"host_response_time\": \"string\", \"district\": \"string\"}, encoding='latin1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09921555-0e03-424f-89f2-a67f7609a4d0",
   "metadata": {},
   "source": [
    "### List columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d21b0619-b776-4f88-9082-98fc247e5880",
   "metadata": {},
   "outputs": [],
   "source": [
    "# line of code from internet that lists columns with index numbers\n",
    "col_mapping = [f\"{c[0]}:{c[1]}\" for c in enumerate(air_df.columns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7218dc20-4f88-457b-9cb0-53de350c1e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0:listing_id',\n",
       " '1:name',\n",
       " '2:host_id',\n",
       " '3:host_since',\n",
       " '4:host_location',\n",
       " '5:host_response_time',\n",
       " '6:host_response_rate',\n",
       " '7:host_acceptance_rate',\n",
       " '8:host_is_superhost',\n",
       " '9:host_total_listings_count',\n",
       " '10:host_has_profile_pic',\n",
       " '11:host_identity_verified',\n",
       " '12:neighbourhood',\n",
       " '13:district',\n",
       " '14:city',\n",
       " '15:latitude',\n",
       " '16:longitude',\n",
       " '17:property_type',\n",
       " '18:room_type',\n",
       " '19:accommodates',\n",
       " '20:bedrooms',\n",
       " '21:amenities',\n",
       " '22:price',\n",
       " '23:minimum_nights',\n",
       " '24:maximum_nights',\n",
       " '25:review_scores_rating',\n",
       " '26:review_scores_accuracy',\n",
       " '27:review_scores_cleanliness',\n",
       " '28:review_scores_checkin',\n",
       " '29:review_scores_communication',\n",
       " '30:review_scores_location',\n",
       " '31:review_scores_value',\n",
       " '32:instant_bookable']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f64a0f-7db3-4225-b7e7-7a7172c16617",
   "metadata": {},
   "source": [
    "## Inspect Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff953ffe-0779-47e3-91bc-5d708381d8a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(279712, 33)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use shape method to get the total number of rows and columns\n",
    "air_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e722ea0-a81d-4659-adda-be2918783f42",
   "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>listing_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.797120e+05</td>\n",
       "      <td>2.797120e+05</td>\n",
       "      <td>150930.000000</td>\n",
       "      <td>166625.000000</td>\n",
       "      <td>279547.000000</td>\n",
       "      <td>279712.000000</td>\n",
       "      <td>279712.000000</td>\n",
       "      <td>279712.000000</td>\n",
       "      <td>250277.000000</td>\n",
       "      <td>279712.000000</td>\n",
       "      <td>279712.000000</td>\n",
       "      <td>2.797120e+05</td>\n",
       "      <td>188307.000000</td>\n",
       "      <td>187999.000000</td>\n",
       "      <td>188047.000000</td>\n",
       "      <td>187941.000000</td>\n",
       "      <td>188025.000000</td>\n",
       "      <td>187937.000000</td>\n",
       "      <td>187927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.638196e+07</td>\n",
       "      <td>1.081658e+08</td>\n",
       "      <td>0.865939</td>\n",
       "      <td>0.827168</td>\n",
       "      <td>24.581612</td>\n",
       "      <td>18.761862</td>\n",
       "      <td>12.595075</td>\n",
       "      <td>3.288736</td>\n",
       "      <td>1.515509</td>\n",
       "      <td>608.792737</td>\n",
       "      <td>8.050967</td>\n",
       "      <td>2.755860e+04</td>\n",
       "      <td>93.405195</td>\n",
       "      <td>9.565476</td>\n",
       "      <td>9.312869</td>\n",
       "      <td>9.701534</td>\n",
       "      <td>9.698593</td>\n",
       "      <td>9.633994</td>\n",
       "      <td>9.335364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.442576e+07</td>\n",
       "      <td>1.108570e+08</td>\n",
       "      <td>0.283744</td>\n",
       "      <td>0.289202</td>\n",
       "      <td>284.041143</td>\n",
       "      <td>32.560343</td>\n",
       "      <td>73.081309</td>\n",
       "      <td>2.133379</td>\n",
       "      <td>1.153080</td>\n",
       "      <td>3441.826611</td>\n",
       "      <td>31.518946</td>\n",
       "      <td>7.282875e+06</td>\n",
       "      <td>10.070437</td>\n",
       "      <td>0.990878</td>\n",
       "      <td>1.146072</td>\n",
       "      <td>0.867434</td>\n",
       "      <td>0.886884</td>\n",
       "      <td>0.833234</td>\n",
       "      <td>1.042625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.577000e+03</td>\n",
       "      <td>1.822000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-34.264400</td>\n",
       "      <td>-99.339630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.384462e+07</td>\n",
       "      <td>1.720656e+07</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-22.964390</td>\n",
       "      <td>-43.198040</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.767098e+07</td>\n",
       "      <td>5.826911e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.710785</td>\n",
       "      <td>2.382780</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.125000e+03</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.978485e+07</td>\n",
       "      <td>1.832853e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.908610</td>\n",
       "      <td>28.986730</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>474.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.125000e+03</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.834353e+07</td>\n",
       "      <td>3.901874e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7235.000000</td>\n",
       "      <td>48.904910</td>\n",
       "      <td>151.339810</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>625216.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>2.147484e+09</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id       host_id  host_response_rate  host_acceptance_rate  \\\n",
       "count  2.797120e+05  2.797120e+05       150930.000000         166625.000000   \n",
       "mean   2.638196e+07  1.081658e+08            0.865939              0.827168   \n",
       "std    1.442576e+07  1.108570e+08            0.283744              0.289202   \n",
       "min    2.577000e+03  1.822000e+03            0.000000              0.000000   \n",
       "25%    1.384462e+07  1.720656e+07            0.900000              0.780000   \n",
       "50%    2.767098e+07  5.826911e+07            1.000000              0.980000   \n",
       "75%    3.978485e+07  1.832853e+08            1.000000              1.000000   \n",
       "max    4.834353e+07  3.901874e+08            1.000000              1.000000   \n",
       "\n",
       "       host_total_listings_count       latitude      longitude   accommodates  \\\n",
       "count              279547.000000  279712.000000  279712.000000  279712.000000   \n",
       "mean                   24.581612      18.761862      12.595075       3.288736   \n",
       "std                   284.041143      32.560343      73.081309       2.133379   \n",
       "min                     0.000000     -34.264400     -99.339630       0.000000   \n",
       "25%                     1.000000     -22.964390     -43.198040       2.000000   \n",
       "50%                     1.000000      40.710785       2.382780       2.000000   \n",
       "75%                     4.000000      41.908610      28.986730       4.000000   \n",
       "max                  7235.000000      48.904910     151.339810      16.000000   \n",
       "\n",
       "            bedrooms          price  minimum_nights  maximum_nights  \\\n",
       "count  250277.000000  279712.000000   279712.000000    2.797120e+05   \n",
       "mean        1.515509     608.792737        8.050967    2.755860e+04   \n",
       "std         1.153080    3441.826611       31.518946    7.282875e+06   \n",
       "min         1.000000       0.000000        1.000000    1.000000e+00   \n",
       "25%         1.000000      75.000000        1.000000    4.500000e+01   \n",
       "50%         1.000000     150.000000        2.000000    1.125000e+03   \n",
       "75%         2.000000     474.000000        5.000000    1.125000e+03   \n",
       "max        50.000000  625216.000000     9999.000000    2.147484e+09   \n",
       "\n",
       "       review_scores_rating  review_scores_accuracy  \\\n",
       "count         188307.000000           187999.000000   \n",
       "mean              93.405195                9.565476   \n",
       "std               10.070437                0.990878   \n",
       "min               20.000000                2.000000   \n",
       "25%               91.000000                9.000000   \n",
       "50%               96.000000               10.000000   \n",
       "75%              100.000000               10.000000   \n",
       "max              100.000000               10.000000   \n",
       "\n",
       "       review_scores_cleanliness  review_scores_checkin  \\\n",
       "count              188047.000000          187941.000000   \n",
       "mean                    9.312869               9.701534   \n",
       "std                     1.146072               0.867434   \n",
       "min                     2.000000               2.000000   \n",
       "25%                     9.000000              10.000000   \n",
       "50%                    10.000000              10.000000   \n",
       "75%                    10.000000              10.000000   \n",
       "max                    10.000000              10.000000   \n",
       "\n",
       "       review_scores_communication  review_scores_location  \\\n",
       "count                188025.000000           187937.000000   \n",
       "mean                      9.698593                9.633994   \n",
       "std                       0.886884                0.833234   \n",
       "min                       2.000000                2.000000   \n",
       "25%                      10.000000                9.000000   \n",
       "50%                      10.000000               10.000000   \n",
       "75%                      10.000000               10.000000   \n",
       "max                      10.000000               10.000000   \n",
       "\n",
       "       review_scores_value  \n",
       "count        187927.000000  \n",
       "mean              9.335364  \n",
       "std               1.042625  \n",
       "min               2.000000  \n",
       "25%               9.000000  \n",
       "50%              10.000000  \n",
       "75%              10.000000  \n",
       "max              10.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some descriptive stats\n",
    "air_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3521d14-346f-46c7-b1b8-9cb281e1d862",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0e426b64-ec49-4879-9f95-10728eb201b0",
   "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>listing_id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>...</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>instant_bookable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>281420</td>\n",
       "      <td>Beautiful Flat in le Village Montmartre, Paris</td>\n",
       "      <td>1466919</td>\n",
       "      <td>2011-12-03</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3705183</td>\n",
       "      <td>39 mÃÂ² Paris (Sacre CÃ",
       "âur)</td>\n",
       "      <td>10328771</td>\n",
       "      <td>2013-11-29</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4082273</td>\n",
       "      <td>Lovely apartment with Terrace, 60m2</td>\n",
       "      <td>19252768</td>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4797344</td>\n",
       "      <td>Cosy studio (close to Eiffel tower)</td>\n",
       "      <td>10668311</td>\n",
       "      <td>2013-12-17</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4823489</td>\n",
       "      <td>Close to Eiffel Tower - Beautiful flat : 2 rooms</td>\n",
       "      <td>24837558</td>\n",
       "      <td>2014-12-14</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279707</th>\n",
       "      <td>38338635</td>\n",
       "      <td>Appartement T2 neuf prÃÂ¨s du tram T3a Porte ...</td>\n",
       "      <td>31161181</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279708</th>\n",
       "      <td>38538692</td>\n",
       "      <td>Cozy Studio in Montmartre</td>\n",
       "      <td>10294858</td>\n",
       "      <td>2013-11-27</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279709</th>\n",
       "      <td>38683356</td>\n",
       "      <td>Nice and cosy mini-appartement in Paris</td>\n",
       "      <td>2238502</td>\n",
       "      <td>2012-04-27</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279710</th>\n",
       "      <td>39659000</td>\n",
       "      <td>Charming apartment near Rue Saint Maur / Oberk...</td>\n",
       "      <td>38633695</td>\n",
       "      <td>2015-07-16</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279711</th>\n",
       "      <td>40219504</td>\n",
       "      <td>Cosy apartment with view on Canal St Martin</td>\n",
       "      <td>6955618</td>\n",
       "      <td>2013-06-17</td>\n",
       "      <td>Paris, Ile-de-France, France</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279712 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        listing_id                                               name  \\\n",
       "0           281420     Beautiful Flat in le Village Montmartre, Paris   \n",
       "1          3705183                    39 mÃÂ² Paris (Sacre CÃ\n",
       "âur)   \n",
       "2          4082273                Lovely apartment with Terrace, 60m2   \n",
       "3          4797344                Cosy studio (close to Eiffel tower)   \n",
       "4          4823489   Close to Eiffel Tower - Beautiful flat : 2 rooms   \n",
       "...            ...                                                ...   \n",
       "279707    38338635  Appartement T2 neuf prÃÂ¨s du tram T3a Porte ...   \n",
       "279708    38538692                          Cozy Studio in Montmartre   \n",
       "279709    38683356            Nice and cosy mini-appartement in Paris   \n",
       "279710    39659000  Charming apartment near Rue Saint Maur / Oberk...   \n",
       "279711    40219504        Cosy apartment with view on Canal St Martin   \n",
       "\n",
       "         host_id  host_since                 host_location host_response_time  \\\n",
       "0        1466919  2011-12-03  Paris, Ile-de-France, France               <NA>   \n",
       "1       10328771  2013-11-29  Paris, Ile-de-France, France               <NA>   \n",
       "2       19252768  2014-07-31  Paris, Ile-de-France, France               <NA>   \n",
       "3       10668311  2013-12-17  Paris, Ile-de-France, France               <NA>   \n",
       "4       24837558  2014-12-14  Paris, Ile-de-France, France               <NA>   \n",
       "...          ...         ...                           ...                ...   \n",
       "279707  31161181  2015-04-13  Paris, Ile-de-France, France               <NA>   \n",
       "279708  10294858  2013-11-27  Paris, Ile-de-France, France               <NA>   \n",
       "279709   2238502  2012-04-27  Paris, Ile-de-France, France               <NA>   \n",
       "279710  38633695  2015-07-16  Paris, Ile-de-France, France               <NA>   \n",
       "279711   6955618  2013-06-17  Paris, Ile-de-France, France               <NA>   \n",
       "\n",
       "        host_response_rate  host_acceptance_rate host_is_superhost  \\\n",
       "0                      NaN                   NaN                 f   \n",
       "1                      NaN                   NaN                 f   \n",
       "2                      NaN                   NaN                 f   \n",
       "3                      NaN                   NaN                 f   \n",
       "4                      NaN                   NaN                 f   \n",
       "...                    ...                   ...               ...   \n",
       "279707                 NaN                   NaN                 f   \n",
       "279708                 NaN                   NaN                 f   \n",
       "279709                 NaN                   NaN                 f   \n",
       "279710                 NaN                   NaN                 f   \n",
       "279711                 NaN                   NaN                 f   \n",
       "\n",
       "        host_total_listings_count  ... minimum_nights maximum_nights  \\\n",
       "0                             1.0  ...              2           1125   \n",
       "1                             1.0  ...              2           1125   \n",
       "2                             1.0  ...              2           1125   \n",
       "3                             1.0  ...              2           1125   \n",
       "4                             1.0  ...              2           1125   \n",
       "...                           ...  ...            ...            ...   \n",
       "279707                        1.0  ...              1              7   \n",
       "279708                        1.0  ...              7             15   \n",
       "279709                        1.0  ...              6             30   \n",
       "279710                        1.0  ...              3             18   \n",
       "279711                        1.0  ...              2              4   \n",
       "\n",
       "       review_scores_rating review_scores_accuracy review_scores_cleanliness  \\\n",
       "0                     100.0                   10.0                      10.0   \n",
       "1                     100.0                   10.0                      10.0   \n",
       "2                     100.0                   10.0                      10.0   \n",
       "3                     100.0                   10.0                      10.0   \n",
       "4                     100.0                   10.0                      10.0   \n",
       "...                     ...                    ...                       ...   \n",
       "279707                100.0                   10.0                      10.0   \n",
       "279708                100.0                   10.0                      10.0   \n",
       "279709                100.0                   10.0                      10.0   \n",
       "279710                100.0                   10.0                      10.0   \n",
       "279711                100.0                   10.0                      10.0   \n",
       "\n",
       "        review_scores_checkin  review_scores_communication  \\\n",
       "0                        10.0                         10.0   \n",
       "1                        10.0                         10.0   \n",
       "2                        10.0                         10.0   \n",
       "3                        10.0                         10.0   \n",
       "4                        10.0                         10.0   \n",
       "...                       ...                          ...   \n",
       "279707                   10.0                         10.0   \n",
       "279708                   10.0                         10.0   \n",
       "279709                   10.0                         10.0   \n",
       "279710                   10.0                         10.0   \n",
       "279711                   10.0                         10.0   \n",
       "\n",
       "       review_scores_location review_scores_value  instant_bookable  \n",
       "0                        10.0                10.0                 f  \n",
       "1                        10.0                10.0                 f  \n",
       "2                        10.0                10.0                 f  \n",
       "3                        10.0                10.0                 f  \n",
       "4                        10.0                10.0                 f  \n",
       "...                       ...                 ...               ...  \n",
       "279707                   10.0                10.0                 f  \n",
       "279708                   10.0                10.0                 f  \n",
       "279709                   10.0                10.0                 f  \n",
       "279710                   10.0                10.0                 f  \n",
       "279711                   10.0                10.0                 f  \n",
       "\n",
       "[279712 rows x 33 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470ff324-5465-4ff9-962b-bbe55e19d0fa",
   "metadata": {},
   "source": [
    "## Calculate missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83fed3a3-4f4d-4b58-93ac-5e6468c07352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "listing_id                     279712\n",
      "name                           279539\n",
      "host_id                        279712\n",
      "host_since                     279547\n",
      "host_location                  278872\n",
      "host_response_time             150930\n",
      "host_response_rate             150930\n",
      "host_acceptance_rate           166625\n",
      "host_is_superhost              279547\n",
      "host_total_listings_count      279547\n",
      "host_has_profile_pic           279547\n",
      "host_identity_verified         279547\n",
      "neighbourhood                  279712\n",
      "district                        37012\n",
      "city                           279712\n",
      "latitude                       279712\n",
      "longitude                      279712\n",
      "property_type                  279712\n",
      "room_type                      279712\n",
      "accommodates                   279712\n",
      "bedrooms                       250277\n",
      "amenities                      279712\n",
      "price                          279712\n",
      "minimum_nights                 279712\n",
      "maximum_nights                 279712\n",
      "review_scores_rating           188307\n",
      "review_scores_accuracy         187999\n",
      "review_scores_cleanliness      188047\n",
      "review_scores_checkin          187941\n",
      "review_scores_communication    188025\n",
      "review_scores_location         187937\n",
      "review_scores_value            187927\n",
      "instant_bookable               279712\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count non-missing values\n",
    "print(air_df.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c37574e-e68c-4d06-af4c-05c76a5fc42e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4043e5e-39c1-44f3-91d3-c0dd51101d25",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# subtract non-missing from total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb248065-0f1b-4d7a-ab4b-aa9d3d2be54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_rows= air_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "08f0fb67-6c27-46dc-a4e0-f24686faf77b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "279712\n"
     ]
    }
   ],
   "source": [
    "print(num_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f8dbf8b-aa98-468f-8672-759a2ef4a675",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_missing = num_rows - air_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976f7824-f292-448c-889e-2e9ac381c29c",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aaf37fcb-07da-4240-8b2e-8d25fbe026a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "listing_id                          0\n",
      "name                              173\n",
      "host_id                             0\n",
      "host_since                        165\n",
      "host_location                     840\n",
      "host_response_time             128782\n",
      "host_response_rate             128782\n",
      "host_acceptance_rate           113087\n",
      "host_is_superhost                 165\n",
      "host_total_listings_count         165\n",
      "host_has_profile_pic              165\n",
      "host_identity_verified            165\n",
      "neighbourhood                       0\n",
      "district                       242700\n",
      "city                                0\n",
      "latitude                            0\n",
      "longitude                           0\n",
      "property_type                       0\n",
      "room_type                           0\n",
      "accommodates                        0\n",
      "bedrooms                        29435\n",
      "amenities                           0\n",
      "price                               0\n",
      "minimum_nights                      0\n",
      "maximum_nights                      0\n",
      "review_scores_rating            91405\n",
      "review_scores_accuracy          91713\n",
      "review_scores_cleanliness       91665\n",
      "review_scores_checkin           91771\n",
      "review_scores_communication     91687\n",
      "review_scores_location          91775\n",
      "review_scores_value             91785\n",
      "instant_bookable                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# MISSING VALUES\n",
    "print(num_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149763c0-a9b4-49db-abde-bf93e19c6491",
   "metadata": {},
   "source": [
    "## Deal with outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf74baf5-89cb-44e9-aab3-b0911aa46bfa",
   "metadata": {},
   "source": [
    "### Plots with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9491b6c2-f358-403a-b2c3-d41dbf75dd7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a boxplot function\n",
    "def plot_boxplot(df, ft):\n",
    "    df.boxplot(column=[ft])\n",
    "    plt.grid(False)\n",
    "    plt.show()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "59fa7f5a-aa0d-4a39-908c-f3b12a519a95",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_boxplot(air_df, \"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "207cd588-7cbc-430f-9692-c0c7e7e66f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'price'}>]], dtype=object)"
      ]
     },
     "execution_count": 22,
     "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": [
    "air_df.hist(\"price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e3c62e-99c8-4e24-a9d6-1d1858ee7ed8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Remove Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84ff49a6-523a-4622-9ff1-27ef56f4237f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function to remove outliers (from a copy of the dataframe)\n",
    "def outliers(df, ft):\n",
    "    Q1=df[ft].quantile(0.25)\n",
    "    Q3=df[ft].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    ls = df.index[ (df[ft] < lower_bound) | (df[ft] > upper_bound) ]\n",
    "    return ls\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "12d4a490-1c9b-45ec-a975-93954fc4560a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RAF to print bounds\n",
    "def outlier_print(df, ft):\n",
    "    Q1=df[ft].quantile(0.25)\n",
    "    Q3=df[ft].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    print(str(lower_bound) + \" \" + str(upper_bound))\n",
    "    # print(f\"lower{lower_bound} \")\n",
    "    return\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "03539598-552e-4af6-b648-2b3ac620061d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-523.5 1072.5\n"
     ]
    }
   ],
   "source": [
    "outlier_print(air_df, 'price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "918c7c68-18f1-48b9-8728-bdadc4092359",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c762c2e4-7be4-4916-be1c-15e358f83278",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RAF reate a function for upper outliers only, not used here\n",
    "def upper_outliers(df, ft):\n",
    "    Q1=df[ft].quantile(0.25)\n",
    "    Q3=df[ft].quantile(0.75)\n",
    "    IQR = Q3 -Q1\n",
    "    #lower_bound = Q1- 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    ls = df.index[ (df[ft] > upper_bound) ]\n",
    "    return ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b7805396-2f17-47f9-af1d-973129fd848e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list of index numbers for outliers\n",
    "# can put more features in list additional to price\n",
    "index_list=[]\n",
    "for feature in [\"price\"]:\n",
    "   index_list.extend(outliers(air_df, feature ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90d5d842-89c3-439a-8315-bbe339a85a08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31395"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no of outliers\n",
    "len(index_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d8e8cc06-9635-4b50-a79a-0eb199507d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new function called remove which returns a cleaned dataframe without outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5051a1e0-c174-49ed-9fc3-7b74da9c7005",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove(df, ls):\n",
    "    # sort list and make unique\n",
    "    ls=sorted(set(ls))\n",
    "    df=df.drop(ls)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f2f03ec5-3016-4d5d-be17-4c73761f5c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "air_df_cleaned = remove(air_df, index_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc5ebe98-5b78-496b-8f85-0ee646d92b47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248317, 33)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_df_cleaned.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e94cd287-c366-4fdf-87c3-e4429d2ae9e1",
   "metadata": {},
   "source": [
    "### Output cleaned dataframe to CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "402e9819-df3b-4185-b235-32f3904cc02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output cleaned dataframe to CSV file -----------------------------------------------------------------------------\n",
    "# PC\n",
    "# air_df_cleaned.to_csv(\"C:/Users/conif/Documents/Airbnb_Data/Airbnb Data/Listings_cleaned.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d6d928-030b-40a5-b669-a21c04d3679a",
   "metadata": {
    "tags": []
   },
   "source": [
    "###  Plots without outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0c4933a5-a93e-477f-a553-cda439047daf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOBUlEQVR4nO3df6zd9V3H8efLlvFrMEEupGvLbk0qUqZkeqnMLcYEMqoYyx82dslcY4g1s040Jlr8w0UTEozG+CNCbNjcNVvWNbiEBqYbqS4GNcDl10YpDTeD0SuV3mkEsiBQfPvH/WJObk9Lzzntubf9PB/Jzfmez/l8z/fTP+7zfvu959yTqkKS1IbvW+oFSJLGx+hLUkOMviQ1xOhLUkOMviQ1ZOVSL+DdXHbZZTU5ObnUy5CkM8pjjz323aqaWDy+7KM/OTnJzMzMUi9Dks4oSb7Tb9zLO5LUEKMvSQ0x+pLUEKMvSQ0x+pLUkGX/6h1puUlyzJh/uFBnCs/0pQH0C/6JxqXlxuhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkPeNfpJPpfkSJKne8YuTfJgkue620t6Hrs9yWySg0lu6hn/8STf6h77i/jHSiRp7E7mTP/zwKZFYzuBfVW1HtjX3SfJBmArcE23z11JVnT73A1sB9Z3X4ufU5J0mr1r9Kvqn4H/WjS8GZjutqeBW3rGd1fVG1X1PDALbEyyCri4qv6tFv4G7d/27CNJGpNhr+lfUVWHAbrby7vx1cChnnlz3djqbnvxuCRpjE71L3L7XaevE4z3f5Jke5KZJDPz8/OnbHGS1Lpho/9yd8mG7vZINz4HrO2ZtwZ4qRtf02e8r6raVVVTVTU1MTEx5BIlSYsNG/29wLZuextwX8/41iTnJlnHwi9sH+kuAb2W5PruVTuf7NlHkjQm7/oZuUm+BPw0cFmSOeAzwJ3AniS3Ai8CWwCqan+SPcAzwFFgR1W93T3Vp1h4JdD5wN93X5KkMcpy/0DnqampmpmZWeplSMCJPwt3uX8vqS1JHquqqcXjviNXkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpIUZfkhpi9CWpISNFP8lvJdmf5OkkX0pyXpJLkzyY5Lnu9pKe+bcnmU1yMMlNoy9fkjSIoaOfZDXwG8BUVX0QWAFsBXYC+6pqPbCvu0+SDd3j1wCbgLuSrBht+ZKkQYx6eWclcH6SlcAFwEvAZmC6e3wauKXb3gzsrqo3qup5YBbYOOLxJUkDGDr6VfXvwJ8ALwKHgVeq6uvAFVV1uJtzGLi822U1cKjnKea6sWMk2Z5kJsnM/Pz8sEuUJC0yyuWdS1g4e18HvB+4MMknTrRLn7HqN7GqdlXVVFVNTUxMDLtESdIio1zeuRF4vqrmq+ot4CvATwIvJ1kF0N0e6ebPAWt79l/DwuUgSdKYjBL9F4Hrk1yQJMANwAFgL7Ctm7MNuK/b3gtsTXJuknXAeuCREY4vSRrQymF3rKqHk9wLPA4cBZ4AdgHvBfYkuZWFHwxbuvn7k+wBnunm76iqt0dcvyRpAKnqe1l92ZiamqqZmZmlXoYEwMJ/avtb7t9LakuSx6pqavG478iVpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIasXOoFSMtFkrHsX1UjHUcahdGXOicT4xOF3ZjrTODlHUlqiNGXBnC8s3nP8nWmMPrSgKqKquIDv3v//29LZ4qRop/k+5Pcm+TZJAeSfDjJpUkeTPJcd3tJz/zbk8wmOZjkptGXL0kaxKhn+n8O/ENV/TBwLXAA2Ansq6r1wL7uPkk2AFuBa4BNwF1JVox4fEnSAIaOfpKLgZ8CPgtQVW9W1X8Dm4Hpbto0cEu3vRnYXVVvVNXzwCywcdjjS5IGN8qZ/g8C88DfJHkiyT1JLgSuqKrDAN3t5d381cChnv3nurFjJNmeZCbJzPz8/AhLlCT1GiX6K4EfA+6uqg8B36O7lHMc/V7g3Pc3YFW1q6qmqmpqYmJihCVKknqNEv05YK6qHu7u38vCD4GXk6wC6G6P9Mxf27P/GuClEY4vSRrQ0NGvqv8ADiW5qhu6AXgG2Ats68a2Afd123uBrUnOTbIOWA88MuzxJUmDG/XPMHwa+GKS9wDfBn6ZhR8ke5LcCrwIbAGoqv1J9rDwg+EosKOq3h7x+JKkAYwU/ap6Epjq89ANx5l/B3DHKMeUJA3Pd+RKUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1ZOToJ1mR5Ikk93f3L03yYJLnuttLeubenmQ2ycEkN416bEnSYE7Fmf5twIGe+zuBfVW1HtjX3SfJBmArcA2wCbgryYpTcHxJ0kkaKfpJ1gA3A/f0DG8GprvtaeCWnvHdVfVGVT0PzAIbRzm+JGkwo57p/xnwO8D/9oxdUVWHAbrby7vx1cChnnlz3dgxkmxPMpNkZn5+fsQlSpLeMXT0k/wccKSqHjvZXfqMVb+JVbWrqqaqampiYmLYJUqSFlk5wr4fAX4+yc8C5wEXJ/kC8HKSVVV1OMkq4Eg3fw5Y27P/GuClEY4vSRrQ0Gf6VXV7Va2pqkkWfkH7j1X1CWAvsK2btg24r9veC2xNcm6SdcB64JGhVy5JGtgoZ/rHcyewJ8mtwIvAFoCq2p9kD/AMcBTYUVVvn4bjS5KO45REv6q+AXyj2/5P4IbjzLsDuONUHFOSNDjfkStJDTH6ktQQoy9JDTH6ktQQoy9JDTH6ktQQoy9JDTH6ktQQoy9JDTH6ktQQoy9JDTH6ktQQoy9JDTkdf1pZWnLX/sHXeeX1t077cSZ3PnBan/9955/DU5/52Gk9htpi9HVWeuX1t3jhzpuXehkjO90/VNQeL+9IUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOMviQ1xOhLUkOGjn6StUn+KcmBJPuT3NaNX5rkwSTPdbeX9Oxze5LZJAeT3HQq/gGSpJM3ypn+UeC3q+pq4HpgR5INwE5gX1WtB/Z19+ke2wpcA2wC7kqyYpTFS5IGM3T0q+pwVT3ebb8GHABWA5uB6W7aNHBLt70Z2F1Vb1TV88AssHHY40uSBndKruknmQQ+BDwMXFFVh2HhBwNweTdtNXCoZ7e5bqzf821PMpNkZn5+/lQsUZLEKYh+kvcCfwf8ZlW9eqKpfcaq38Sq2lVVU1U1NTExMeoSJUmdkaKf5BwWgv/FqvpKN/xyklXd46uAI934HLC2Z/c1wEujHF+SNJhRXr0T4LPAgar6056H9gLbuu1twH0941uTnJtkHbAeeGTY40uSBjfKB6N/BPgl4FtJnuzGfg+4E9iT5FbgRWALQFXtT7IHeIaFV/7sqKq3Rzi+JGlAQ0e/qh6i/3V6gBuOs88dwB3DHlOSNBrfkStJDTH6ktQQoy9JDTH6ktQQoy9JDTH6ktQQoy9JDTH6ktSQUd6RKy1bF129kx+Z3rnUyxjZRVcD3LzUy9BZxOjrrPTagTt54c4zP5aTOx9Y6iXoLOPlHUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIYYfUlqiNGXpIb45iydtc6GNza97/xzlnoJOssYfZ2VxvFu3MmdD5wV7/pVW7y8I0kNMfqS1BCjL0kNMfqS1BCjL0kNMfqS1BCjL0kNMfqS1JCxRz/JpiQHk8wmOfM/xFSSziBjjX6SFcBfAT8DbAA+nmTDONcgSS0b95n+RmC2qr5dVW8Cu4HNY16DJDVr3H97ZzVwqOf+HPATiycl2Q5sB7jyyivHszI1L8ng+/zR4MepqsF3kk6RcZ/p9/uuOuY7oKp2VdVUVU1NTEyMYVnSQozH8SUtpXFHfw5Y23N/DfDSmNcgSc0ad/QfBdYnWZfkPcBWYO+Y1yBJzRrrNf2qOprk14GvASuAz1XV/nGuQZJaNvYPUamqrwJfHfdxJUm+I1eSmmL0JakhRl+SGmL0JakhWe5vFkkyD3xnqdch9XEZ8N2lXoR0HB+oqmPe3brsoy8tV0lmqmpqqdchDcLLO5LUEKMvSQ0x+tLwdi31AqRBeU1fkhrimb4kNcToS1JDjL40hCR/mOTGpV6HNCiv6UsDSrKiqt5e6nVIw/BMX+qRZDLJs0mmk3wzyb1JLkjyQpLfT/IQsCXJ55P8QrfPdUn+NclTSR5JclGSFUn+OMmj3fP86hL/0yTA6Ev9XAXsqqofBV4Ffq0b/5+q+mhV7X5nYvcJcF8Gbquqa4EbgdeBW4FXquo64DrgV5KsG+c/QurH6EvHOlRV/9JtfwH4aLf95T5zrwIOV9WjAFX1alUdBT4GfDLJk8DDwA8A60/rqqWTMPZPzpLOAIt/0fXO/e/1mZs+898Z/3RVfe1ULkwalWf60rGuTPLhbvvjwEMnmPss8P4k1wF01/NXsvA50J9Kck43/kNJLjydi5ZOhtGXjnUA2Jbkm8ClwN3Hm1hVbwK/CPxlkqeAB4HzgHuAZ4DHkzwN/DX+z1rLgC/ZlHokmQTur6oPLvVapNPBM31Jaohn+pLUEM/0JakhRl+SGmL0JakhRl+SGmL0Jakh/weBJ5yw84K/0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boxplot(air_df_cleaned, \"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4a4b6309-8af3-4c16-bc6d-506f65916246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'price'}>]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "air_df_cleaned.hist(\"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "820bafdb-6015-44b0-a4d2-1c2f08db03cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          53\n",
       "1         120\n",
       "2          89\n",
       "3          58\n",
       "4          60\n",
       "         ... \n",
       "279707    120\n",
       "279708     60\n",
       "279709     50\n",
       "279710    105\n",
       "279711     70\n",
       "Name: price, Length: 248317, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_df_cleaned['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0f50116-86d1-4625-8c9a-24801237f028",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72f34122-d73f-43ec-9287-9adcf2f5000d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}