Look_at_the_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id='part1'>A Look at the Data</h2>\n",
    "\n",
    "In order to get a better understanding of the data we will be looking at throughout this project, let's take a look at some of the characteristics of the dataset.\n",
    "\n",
    "First, let's read in the data and necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pycodestyle_magic extension is already loaded. To reload it, use:\n",
      "  %reload_ext pycodestyle_magic\n"
     ]
    }
   ],
   "source": [
    "# Import necessary packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "% config InlineBachend.figure_format = 'retina'\n",
    "\n",
    "# Enable pep8 code style test\n",
    "%load_ext pycodestyle_magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listings dataset\n",
    "This dataset including full descriptions and average review score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\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>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>https://www.airbnb.com/rooms/241032</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Stylish Queen Anne Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>953595</td>\n",
       "      <td>https://www.airbnb.com/rooms/953595</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Bright &amp; Airy Queen Anne Apartment</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>Beautiful, hypoallergenic apartment in an extr...</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>none</td>\n",
       "      <td>Queen Anne is a wonderful, truly functional vi...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3308979</td>\n",
       "      <td>https://www.airbnb.com/rooms/3308979</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>New Modern House-Amazing water view</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>Our house is modern, light and fresh with a wa...</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>none</td>\n",
       "      <td>Upper Queen Anne is a charming neighborhood fu...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7421966</td>\n",
       "      <td>https://www.airbnb.com/rooms/7421966</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Queen Anne Chateau</td>\n",
       "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>278830</td>\n",
       "      <td>https://www.airbnb.com/rooms/278830</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Charming craftsman 3 bdm house</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>none</td>\n",
       "      <td>We are in the beautiful neighborhood of Queen ...</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url       scrape_id last_scraped  \\\n",
       "0   241032   https://www.airbnb.com/rooms/241032  20160104002432   2016-01-04   \n",
       "1   953595   https://www.airbnb.com/rooms/953595  20160104002432   2016-01-04   \n",
       "2  3308979  https://www.airbnb.com/rooms/3308979  20160104002432   2016-01-04   \n",
       "3  7421966  https://www.airbnb.com/rooms/7421966  20160104002432   2016-01-04   \n",
       "4   278830   https://www.airbnb.com/rooms/278830  20160104002432   2016-01-04   \n",
       "\n",
       "                                  name  \\\n",
       "0         Stylish Queen Anne Apartment   \n",
       "1   Bright & Airy Queen Anne Apartment   \n",
       "2  New Modern House-Amazing water view   \n",
       "3                   Queen Anne Chateau   \n",
       "4       Charming craftsman 3 bdm house   \n",
       "\n",
       "                                             summary  \\\n",
       "0                                                NaN   \n",
       "1  Chemically sensitive? We've removed the irrita...   \n",
       "2  New modern house built in 2013.  Spectacular s...   \n",
       "3  A charming apartment that sits atop Queen Anne...   \n",
       "4  Cozy family craftman house in beautiful neighb...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Make your self at home in this charming one-be...   \n",
       "1  Beautiful, hypoallergenic apartment in an extr...   \n",
       "2  Our house is modern, light and fresh with a wa...   \n",
       "3                                                NaN   \n",
       "4  Cozy family craftman house in beautiful neighb...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Make your self at home in this charming one-be...                none   \n",
       "1  Chemically sensitive? We've removed the irrita...                none   \n",
       "2  New modern house built in 2013.  Spectacular s...                none   \n",
       "3  A charming apartment that sits atop Queen Anne...                none   \n",
       "4  Cozy family craftman house in beautiful neighb...                none   \n",
       "\n",
       "                               neighborhood_overview        ...         \\\n",
       "0                                                NaN        ...          \n",
       "1  Queen Anne is a wonderful, truly functional vi...        ...          \n",
       "2  Upper Queen Anne is a charming neighborhood fu...        ...          \n",
       "3                                                NaN        ...          \n",
       "4  We are in the beautiful neighborhood of Queen ...        ...          \n",
       "\n",
       "  review_scores_value requires_license license jurisdiction_names  \\\n",
       "0                10.0                f     NaN         WASHINGTON   \n",
       "1                10.0                f     NaN         WASHINGTON   \n",
       "2                10.0                f     NaN         WASHINGTON   \n",
       "3                 NaN                f     NaN         WASHINGTON   \n",
       "4                 9.0                f     NaN         WASHINGTON   \n",
       "\n",
       "  instant_bookable cancellation_policy  require_guest_profile_picture  \\\n",
       "0                f            moderate                              f   \n",
       "1                f              strict                              t   \n",
       "2                f              strict                              f   \n",
       "3                f            flexible                              f   \n",
       "4                f              strict                              f   \n",
       "\n",
       "  require_guest_phone_verification calculated_host_listings_count  \\\n",
       "0                                f                              2   \n",
       "1                                t                              6   \n",
       "2                                f                              2   \n",
       "3                                f                              1   \n",
       "4                                f                              1   \n",
       "\n",
       "  reviews_per_month  \n",
       "0              4.07  \n",
       "1              1.48  \n",
       "2              1.15  \n",
       "3               NaN  \n",
       "4              0.89  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Listings data\n",
    "Listings = pd.read_csv('listings.csv')\n",
    "Listings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3818, 92)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The data shape\n",
    "Listings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3818 entries, 0 to 3817\n",
      "Data columns (total 92 columns):\n",
      "id                                  3818 non-null int64\n",
      "listing_url                         3818 non-null object\n",
      "scrape_id                           3818 non-null int64\n",
      "last_scraped                        3818 non-null object\n",
      "name                                3818 non-null object\n",
      "summary                             3641 non-null object\n",
      "space                               3249 non-null object\n",
      "description                         3818 non-null object\n",
      "experiences_offered                 3818 non-null object\n",
      "neighborhood_overview               2786 non-null object\n",
      "notes                               2212 non-null object\n",
      "transit                             2884 non-null object\n",
      "thumbnail_url                       3498 non-null object\n",
      "medium_url                          3498 non-null object\n",
      "picture_url                         3818 non-null object\n",
      "xl_picture_url                      3498 non-null object\n",
      "host_id                             3818 non-null int64\n",
      "host_url                            3818 non-null object\n",
      "host_name                           3816 non-null object\n",
      "host_since                          3816 non-null object\n",
      "host_location                       3810 non-null object\n",
      "host_about                          2959 non-null object\n",
      "host_response_time                  3295 non-null object\n",
      "host_response_rate                  3295 non-null object\n",
      "host_acceptance_rate                3045 non-null object\n",
      "host_is_superhost                   3816 non-null object\n",
      "host_thumbnail_url                  3816 non-null object\n",
      "host_picture_url                    3816 non-null object\n",
      "host_neighbourhood                  3518 non-null object\n",
      "host_listings_count                 3816 non-null float64\n",
      "host_total_listings_count           3816 non-null float64\n",
      "host_verifications                  3818 non-null object\n",
      "host_has_profile_pic                3816 non-null object\n",
      "host_identity_verified              3816 non-null object\n",
      "street                              3818 non-null object\n",
      "neighbourhood                       3402 non-null object\n",
      "neighbourhood_cleansed              3818 non-null object\n",
      "neighbourhood_group_cleansed        3818 non-null object\n",
      "city                                3818 non-null object\n",
      "state                               3818 non-null object\n",
      "zipcode                             3811 non-null object\n",
      "market                              3818 non-null object\n",
      "smart_location                      3818 non-null object\n",
      "country_code                        3818 non-null object\n",
      "country                             3818 non-null object\n",
      "latitude                            3818 non-null float64\n",
      "longitude                           3818 non-null float64\n",
      "is_location_exact                   3818 non-null object\n",
      "property_type                       3817 non-null object\n",
      "room_type                           3818 non-null object\n",
      "accommodates                        3818 non-null int64\n",
      "bathrooms                           3802 non-null float64\n",
      "bedrooms                            3812 non-null float64\n",
      "beds                                3817 non-null float64\n",
      "bed_type                            3818 non-null object\n",
      "amenities                           3818 non-null object\n",
      "square_feet                         97 non-null float64\n",
      "price                               3818 non-null object\n",
      "weekly_price                        2009 non-null object\n",
      "monthly_price                       1517 non-null object\n",
      "security_deposit                    1866 non-null object\n",
      "cleaning_fee                        2788 non-null object\n",
      "guests_included                     3818 non-null int64\n",
      "extra_people                        3818 non-null object\n",
      "minimum_nights                      3818 non-null int64\n",
      "maximum_nights                      3818 non-null int64\n",
      "calendar_updated                    3818 non-null object\n",
      "has_availability                    3818 non-null object\n",
      "availability_30                     3818 non-null int64\n",
      "availability_60                     3818 non-null int64\n",
      "availability_90                     3818 non-null int64\n",
      "availability_365                    3818 non-null int64\n",
      "calendar_last_scraped               3818 non-null object\n",
      "number_of_reviews                   3818 non-null int64\n",
      "first_review                        3191 non-null object\n",
      "last_review                         3191 non-null object\n",
      "review_scores_rating                3171 non-null float64\n",
      "review_scores_accuracy              3160 non-null float64\n",
      "review_scores_cleanliness           3165 non-null float64\n",
      "review_scores_checkin               3160 non-null float64\n",
      "review_scores_communication         3167 non-null float64\n",
      "review_scores_location              3163 non-null float64\n",
      "review_scores_value                 3162 non-null float64\n",
      "requires_license                    3818 non-null object\n",
      "license                             0 non-null float64\n",
      "jurisdiction_names                  3818 non-null object\n",
      "instant_bookable                    3818 non-null object\n",
      "cancellation_policy                 3818 non-null object\n",
      "require_guest_profile_picture       3818 non-null object\n",
      "require_guest_phone_verification    3818 non-null object\n",
      "calculated_host_listings_count      3818 non-null int64\n",
      "reviews_per_month                   3191 non-null float64\n",
      "dtypes: float64(17), int64(13), object(62)\n",
      "memory usage: 29.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Check memory usage and non-null values in each column\n",
    "Listings.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calendar Dataset\n",
    "Dataset including listing id and the price and availability for that day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>t</td>\n",
       "      <td>$85.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>t</td>\n",
       "      <td>$85.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available   price\n",
       "0      241032  2016-01-04         t  $85.00\n",
       "1      241032  2016-01-05         t  $85.00\n",
       "2      241032  2016-01-06         f     NaN\n",
       "3      241032  2016-01-07         f     NaN\n",
       "4      241032  2016-01-08         f     NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Laod the calendar data\n",
    "calendar = pd.read_csv('calendar.csv')\n",
    "# Print Five first rows\n",
    "calendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1393565</th>\n",
       "      <td>10208623</td>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393566</th>\n",
       "      <td>10208623</td>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393567</th>\n",
       "      <td>10208623</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393568</th>\n",
       "      <td>10208623</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393569</th>\n",
       "      <td>10208623</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id        date available price\n",
       "1393565    10208623  2016-12-29         f   NaN\n",
       "1393566    10208623  2016-12-30         f   NaN\n",
       "1393567    10208623  2016-12-31         f   NaN\n",
       "1393568    10208623  2017-01-01         f   NaN\n",
       "1393569    10208623  2017-01-02         f   NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the end of the file\n",
    "calendar.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1393570, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The data shape\n",
    "calendar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1393570 entries, 0 to 1393569\n",
      "Data columns (total 4 columns):\n",
      "listing_id    1393570 non-null int64\n",
      "date          1393570 non-null object\n",
      "available     1393570 non-null object\n",
      "price         934542 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 258.0 MB\n"
     ]
    }
   ],
   "source": [
    "# Check memory usege and number of non null values in each columns\n",
    "calendar.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reviews Datasets\n",
    "This datasets including unique id for each reviewer and detailed comments."
   ]
  },
  {
   "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>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7202016</td>\n",
       "      <td>38917982</td>\n",
       "      <td>2015-07-19</td>\n",
       "      <td>28943674</td>\n",
       "      <td>Bianca</td>\n",
       "      <td>Cute and cozy place. Perfect location to every...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7202016</td>\n",
       "      <td>39087409</td>\n",
       "      <td>2015-07-20</td>\n",
       "      <td>32440555</td>\n",
       "      <td>Frank</td>\n",
       "      <td>Kelly has a great room in a very central locat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7202016</td>\n",
       "      <td>39820030</td>\n",
       "      <td>2015-07-26</td>\n",
       "      <td>37722850</td>\n",
       "      <td>Ian</td>\n",
       "      <td>Very spacious apartment, and in a great neighb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7202016</td>\n",
       "      <td>40813543</td>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>33671805</td>\n",
       "      <td>George</td>\n",
       "      <td>Close to Seattle Center and all it has to offe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7202016</td>\n",
       "      <td>41986501</td>\n",
       "      <td>2015-08-10</td>\n",
       "      <td>34959538</td>\n",
       "      <td>Ming</td>\n",
       "      <td>Kelly was a great host and very accommodating ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        id        date  reviewer_id reviewer_name  \\\n",
       "0     7202016  38917982  2015-07-19     28943674        Bianca   \n",
       "1     7202016  39087409  2015-07-20     32440555         Frank   \n",
       "2     7202016  39820030  2015-07-26     37722850           Ian   \n",
       "3     7202016  40813543  2015-08-02     33671805        George   \n",
       "4     7202016  41986501  2015-08-10     34959538          Ming   \n",
       "\n",
       "                                            comments  \n",
       "0  Cute and cozy place. Perfect location to every...  \n",
       "1  Kelly has a great room in a very central locat...  \n",
       "2  Very spacious apartment, and in a great neighb...  \n",
       "3  Close to Seattle Center and all it has to offe...  \n",
       "4  Kelly was a great host and very accommodating ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the reviews data\n",
    "reviews = pd.read_csv('reviews.csv')\n",
    "# Print five first rows\n",
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84849, 6)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The data shape\n",
    "reviews.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 84849 entries, 0 to 84848\n",
      "Data columns (total 6 columns):\n",
      "listing_id       84849 non-null int64\n",
      "id               84849 non-null int64\n",
      "date             84849 non-null object\n",
      "reviewer_id      84849 non-null int64\n",
      "reviewer_name    84849 non-null object\n",
      "comments         84831 non-null object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 49.2 MB\n"
     ]
    }
   ],
   "source": [
    "# Memory usege and non null values per column\n",
    "reviews.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Questions (Data understanding)\n",
    "\n",
    "After looking at our data variables we came  out with three important questions that we will try to answer throughout this project :\n",
    " - What are the neighborhoods most requested by AirBnb customers?\n",
    " - What are the busiest times of the year to visit Seattle? By how much do prices spike ?\n",
    " - How can you generate higher ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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