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Seattle Airbnb Open Data.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Seattle Airbnb Open Data.ipynb",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/jinglescode/08d21b680bd11008c73083f9645d6b1d/seattle-airbnb-open-data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V6Rnha-lfvNB",
"colab_type": "text"
},
"source": [
"# Introduction\n",
"As a prospective Airbnb host in Seattle, I have questions like:\n",
"* When to rent to maximise revenue, and for maintenance during off-peak?\n",
"* Common group size of Seattle travelers, is it 2 or family or 4 or larger?\n",
"* Bedroom configurations to maximise booking rates?\n",
"* Main factors to achieve good rating?\n",
"* Does higher ratings hosts has higher revenue?\n",
"* Common amenities to include?"
]
},
{
"cell_type": "code",
"metadata": {
"id": "b85lo5i1juLc",
"colab_type": "code",
"outputId": "19ed0265-d05c-4342-bc02-6e5468eafd37",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"#@title Load Packages\n",
"import pandas as pd\n",
"pd.options.mode.chained_assignment = None # default='warn'\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"print(\"Packages loaded\")"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Packages loaded\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QT3CxlNM_C9j",
"colab_type": "text"
},
"source": [
"# Fetch and Prepare Data\n",
"Fetch listing and reviews data from csv files, and perform data cleaning\n",
"* review_score_xx that are empty are assumed that they do not have reviews, so set as zero\n",
"* bathrooms, bedrooms, and beds that are empty are assumed that they do not have, so set as zero\n",
"* price has dollar sign, remove it and cast as float\n",
"* covert date to pandas datatime type\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BwNKcG7elfHn",
"colab_type": "code",
"cellView": "both",
"outputId": "e3aad258-ae2a-42fa-a638-9b5e74712017",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"#@title Load data from listings.csv\n",
"# get listings data\n",
"pd_listings = pd.read_csv(\"https://raw.githubusercontent.com/jinglescode/notebooks/master/kaggle/seattle_airbnb_open_data/listings.csv\")\n",
"\n",
"# select columns from pd_listings\n",
"pd_listings = pd_listings[['id','name','neighbourhood_group_cleansed','latitude','longitude','property_type','room_type','accommodates','bathrooms','bedrooms','beds','amenities','price','guests_included','minimum_nights','number_of_reviews','review_scores_rating','review_scores_accuracy','review_scores_cleanliness','review_scores_checkin','review_scores_communication','review_scores_location','review_scores_value']]\n",
"\n",
"# basic data cleaning\n",
"pd_listings['price'] = pd_listings['price'].str.replace(\"[$, ]\", \"\").astype(\"float\")\n",
"\n",
"pd_listings.at[pd_listings['bathrooms'].isnull(), 'bathrooms'] = 0\n",
"pd_listings.at[pd_listings['bedrooms'].isnull(), 'bedrooms'] = 0 # yea there are 6 that has no bedrooms, but they do have 1 bathrooms\n",
"pd_listings.at[pd_listings['beds'].isnull(), 'beds'] = 0 # there's one listing for 1 guest, without any beds\n",
"\n",
"pd_listings.at[pd_listings['review_scores_rating'].isnull(), 'review_scores_rating'] = 0\n",
"pd_listings.at[pd_listings['review_scores_accuracy'].isnull(), 'review_scores_accuracy'] = 0\n",
"pd_listings.at[pd_listings['review_scores_cleanliness'].isnull(), 'review_scores_cleanliness'] = 0\n",
"pd_listings.at[pd_listings['review_scores_checkin'].isnull(), 'review_scores_checkin'] = 0\n",
"pd_listings.at[pd_listings['review_scores_communication'].isnull(), 'review_scores_communication'] = 0\n",
"pd_listings.at[pd_listings['review_scores_location'].isnull(), 'review_scores_location'] = 0\n",
"pd_listings.at[pd_listings['review_scores_value'].isnull(), 'review_scores_value'] = 0\n",
"\n",
"pd_listings.rename(columns={'id':'listing_id'}, inplace=True)\n",
"\n",
"# pd_listings.describe()\n",
"\n",
"print('listings.csv loaded into pd_listings')"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"listings.csv loaded into pd_listings\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6kayoy_tQb-3",
"colab_type": "code",
"cellView": "both",
"outputId": "c3affeec-592d-4851-c0e5-9597c2af1ada",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"#@title Load data from reviews.csv\n",
"pd_reviews = pd.read_csv(\"https://raw.githubusercontent.com/jinglescode/notebooks/master/kaggle/seattle_airbnb_open_data/reviews.csv\")\n",
"\n",
"pd_reviews = pd_reviews[['id','listing_id','date']]\n",
"\n",
"# basic conversions\n",
"pd_reviews['date'] = pd.to_datetime(pd_reviews['date'])\n",
"\n",
"# pd_reviews.head()\n",
"print('reviews.csv loaded into pd_reviews')"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"reviews.csv loaded into pd_reviews\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8wDWQ1CxJ2xC",
"colab_type": "text"
},
"source": [
"**Check if reviews.csv tally with listings.csv**\n",
"\n",
"In listings.csv, it has 'number_of_reviews' column, so just to check if number of reviews in reviews.csv is equal to SUM(number_of_reviews). Result shows empty result (0 rows), means that the number of reviews in reviews.csv matches the sum of number_of_reviews in listings.csv. \n",
"\n",
"**Create bookings DataFrame**\n",
"\n",
"With this, I shall create a dataframe pd_bookings using each review as one booking, and estimate revenue by (price * minimum_nights)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0BPSPsyN8v8n",
"colab_type": "code",
"outputId": "2aa8dd4d-2408-4620-e66d-31f81697a9a2",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 86
}
},
"source": [
"#@title \n",
"pd_listing_count_reviws = pd_reviews[['listing_id','id']].groupby(['listing_id']).count()\n",
"pd_listing_count_reviws.columns = ['# of reviews']\n",
"# pd_listing_count_reviws['listing_id'] = pd_listing_count_reviws.index\n",
"\n",
"pd_listings_plus_reviews = pd.merge(pd_listings, pd_listing_count_reviws, on='listing_id')\n",
"\n",
"pd_listings_plus_reviews.at[pd_listings_plus_reviews['# of reviews'].isnull(), '# of reviews'] = 0\n",
"\n",
"pd_listings_plus_reviews[ pd_listings_plus_reviews['# of reviews'] != pd_listings_plus_reviews['number_of_reviews']]"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"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>neighbourhood_group_cleansed</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>property_type</th>\n",
" <th>room_type</th>\n",
" <th>accommodates</th>\n",
" <th>bathrooms</th>\n",
" <th>bedrooms</th>\n",
" <th>beds</th>\n",
" <th>amenities</th>\n",
" <th>price</th>\n",
" <th>guests_included</th>\n",
" <th>minimum_nights</th>\n",
" <th>number_of_reviews</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># of reviews</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [listing_id, name, neighbourhood_group_cleansed, latitude, longitude, property_type, room_type, accommodates, bathrooms, bedrooms, beds, amenities, price, guests_included, minimum_nights, number_of_reviews, review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value, # of reviews]\n",
"Index: []"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nRz7ivxnMlAz",
"colab_type": "code",
"cellView": "form",
"colab": {}
},
"source": [
"#@title Calculate estimated revenue for each listing\n",
"\n",
"# get estimated bookings base on reviews\n",
"pd_bookings = pd.merge(pd_reviews, pd_listings, on='listing_id')\n",
"pd_bookings['estimated_revenue'] = pd_bookings['price'] * pd_bookings['minimum_nights']\n",
"\n",
"# get revenue by listings\n",
"pd_listings_revenue = pd_bookings[['listing_id','estimated_revenue']].groupby(['listing_id']).sum()\n",
"# pd_listings_revenue['listing_id'] = pd_listings_revenue.index\n",
"\n",
"pd_listings = pd.merge(pd_listings, pd_listings_revenue, on='listing_id', how='left')\n",
"pd_listings.at[pd_listings['estimated_revenue'].isnull(), 'estimated_revenue'] = 0"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "bwyT9Q88KCTm",
"colab_type": "text"
},
"source": [
"# Time to answer some questions"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xvALlL-zY80R",
"colab_type": "code",
"outputId": "6be70bce-a0fc-4ff6-d2b6-75df0fc2bd2d",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"#@title Showing 5 highest revenue listings\n",
"pd_listings[['listing_id','number_of_reviews','minimum_nights','accommodates','bedrooms','beds','estimated_revenue']].sort_values('estimated_revenue', ascending=False).head()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"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>number_of_reviews</th>\n",
" <th>minimum_nights</th>\n",
" <th>accommodates</th>\n",
" <th>bedrooms</th>\n",
" <th>beds</th>\n",
" <th>estimated_revenue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2617</th>\n",
" <td>3594885</td>\n",
" <td>8</td>\n",
" <td>1000</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1200000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2107</th>\n",
" <td>5056580</td>\n",
" <td>100</td>\n",
" <td>31</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>306900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1500</th>\n",
" <td>4009508</td>\n",
" <td>38</td>\n",
" <td>20</td>\n",
" <td>5</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>266000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1537</th>\n",
" <td>1954452</td>\n",
" <td>71</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>218680.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1519</th>\n",
" <td>3971934</td>\n",
" <td>48</td>\n",
" <td>20</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>171840.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" listing_id number_of_reviews ... beds estimated_revenue\n",
"2617 3594885 8 ... 1.0 1200000.0\n",
"2107 5056580 100 ... 1.0 306900.0\n",
"1500 4009508 38 ... 2.0 266000.0\n",
"1537 1954452 71 ... 1.0 218680.0\n",
"1519 3971934 48 ... 1.0 171840.0\n",
"\n",
"[5 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BSzM5ZcvSwGI",
"colab_type": "text"
},
"source": [
"Wow! Looks like our top earners are hosts have minimum nights of 1000. But it might be data anomaly because 1000 nights are kind of extreme, so let's look at the proportion of listings with different minimum_nights."
]
},
{
"cell_type": "code",
"metadata": {
"id": "TI798sgQSwYN",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 762
},
"outputId": "474ace48-c2db-41a7-e2ed-8af8c513a0ed"
},
"source": [
"pd_listings[['listing_id','minimum_nights']].groupby(['minimum_nights']).count().sort_values('minimum_nights')"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
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" 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",
" </tr>\n",
" <tr>\n",
" <th>minimum_nights</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" listing_id\n",
"minimum_nights \n",
"1 1610\n",
"2 1423\n",
"3 480\n",
"4 122\n",
"5 67\n",
"6 16\n",
"7 50\n",
"8 1\n",
"10 5\n",
"11 1\n",
"12 1\n",
"13 2\n",
"14 16\n",
"15 1\n",
"20 11\n",
"21 1\n",
"26 1\n",
"28 1\n",
"29 1\n",
"30 6\n",
"31 1\n",
"1000 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tK-6IUkCLEFO",
"colab_type": "text"
},
"source": [
"Most hosts have minimum_nights of up to a month, the host with 1000 nights, gotta filter it away."
]
},
{
"cell_type": "code",
"metadata": {
"id": "gkg8Q5j_LYii",
"colab_type": "code",
"outputId": "7517a978-06f8-4c76-b9d8-9bd698a3aa09",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"#@title Showing 5 highest revenue listings (minimum_nights <= 7)\n",
"pd_listings.loc[pd_listings['minimum_nights']<=7, ['listing_id','number_of_reviews','minimum_nights','accommodates','bedrooms','beds','estimated_revenue']].sort_values('estimated_revenue', ascending=False).head()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\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>number_of_reviews</th>\n",
" <th>minimum_nights</th>\n",
" <th>accommodates</th>\n",
" <th>bedrooms</th>\n",
" <th>beds</th>\n",
" <th>estimated_revenue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1070</th>\n",
" <td>3385421</td>\n",
" <td>31</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>103602.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3308979</td>\n",
" <td>20</td>\n",
" <td>4</td>\n",
" <td>11</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>78000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3667</th>\n",
" <td>53803</td>\n",
" <td>41</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>71750.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1592</th>\n",
" <td>9460</td>\n",
" <td>240</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>71280.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3165</th>\n",
" <td>3040278</td>\n",
" <td>156</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>67704.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" listing_id number_of_reviews ... beds estimated_revenue\n",
"1070 3385421 31 ... 2.0 103602.0\n",
"2 3308979 20 ... 7.0 78000.0\n",
"3667 53803 41 ... 3.0 71750.0\n",
"1592 9460 240 ... 1.0 71280.0\n",
"3165 3040278 156 ... 2.0 67704.0\n",
"\n",
"[5 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1IhfK2hBV0Zp",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"cellView": "form",
"outputId": "6a602e63-2b79-46d0-bd11-05c035243283"
},
"source": [
"#@title Showing 5 highest revenue listings (minimum_nights <= 4)\n",
"pd_listings.loc[pd_listings['minimum_nights']<=4, ['listing_id','number_of_reviews','minimum_nights','accommodates','bedrooms','beds','estimated_revenue']].sort_values('estimated_revenue', ascending=False).head()"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"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>number_of_reviews</th>\n",
" <th>minimum_nights</th>\n",
" <th>accommodates</th>\n",
" <th>bedrooms</th>\n",
" <th>beds</th>\n",
" <th>estimated_revenue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3308979</td>\n",
" <td>20</td>\n",
" <td>4</td>\n",
" <td>11</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>78000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1592</th>\n",
" <td>9460</td>\n",
" <td>240</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>71280.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3165</th>\n",
" <td>3040278</td>\n",
" <td>156</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>67704.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3216</th>\n",
" <td>481220</td>\n",
" <td>164</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>63960.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2922</th>\n",
" <td>699596</td>\n",
" <td>136</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>61200.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" listing_id number_of_reviews ... beds estimated_revenue\n",
"2 3308979 20 ... 7.0 78000.0\n",
"1592 9460 240 ... 1.0 71280.0\n",
"3165 3040278 156 ... 2.0 67704.0\n",
"3216 481220 164 ... 3.0 63960.0\n",
"2922 699596 136 ... 1.0 61200.0\n",
"\n",
"[5 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PLjh8P2BNCqJ",
"colab_type": "text"
},
"source": [
"Short term hosts has lower revenue compared to our long term hosts. What is the correlation between minimum nights and estimated revenue?"
]
},
{
"cell_type": "code",
"metadata": {
"id": "3-leZ0k3WO-k",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
},
"cellView": "form",
"outputId": "19c58940-955a-4099-ff78-220ddd9357c5"
},
"source": [
"#@title Correlation between minimum nights and estimated revenue (not filtering min night 1000)\n",
"pd_listings[['minimum_nights','estimated_revenue']].corr()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"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>minimum_nights</th>\n",
" <th>estimated_revenue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>minimum_nights</th>\n",
" <td>1.000000</td>\n",
" <td>0.872084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>estimated_revenue</th>\n",
" <td>0.872084</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" minimum_nights estimated_revenue\n",
"minimum_nights 1.000000 0.872084\n",
"estimated_revenue 0.872084 1.000000"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "yRYYjq5WNX38",
"colab_type": "code",
"outputId": "6fd2bf29-4205-4dae-dd1e-a6fc2c7ae8c9",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
}
},
"source": [
"#@title Correlation between minimum nights and estimated revenue (filter min night 1000)\n",
"pd_listings.loc[pd_listings['minimum_nights']<=7, ['minimum_nights','estimated_revenue']].corr()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"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>minimum_nights</th>\n",
" <th>estimated_revenue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>minimum_nights</th>\n",
" <td>1.000000</td>\n",
" <td>0.199189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>estimated_revenue</th>\n",
" <td>0.199189</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" minimum_nights estimated_revenue\n",
"minimum_nights 1.000000 0.199189\n",
"estimated_revenue 0.199189 1.000000"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oJMHyV6LOTN5",
"colab_type": "text"
},
"source": [
"Host with 1000 minimum nights has influenced the correlation for revenue. But after removing that host, minimum nights and estimated revenue are not highly correlated."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "324oYXKzRg9C",
"colab_type": "text"
},
"source": [
"It would be useful to know the most popular time of the year to rent in Seattle (so dont have to rent all year round and do maintenance during offpeak wont hurt revenue)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "C-V2FvyPOt55",
"colab_type": "code",
"cellView": "form",
"outputId": "598fe0ae-e885-4472-f273-aa772c705967",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 336
}
},
"source": [
"#@title Best months for rental?\n",
"\n",
"plt.figure(figsize=(15, 5))\n",
"\n",
"# # bookings by month\n",
"plotdata = pd_reviews[['date']].groupby(pd_reviews[\"date\"].dt.month).count()\n",
"plotdata.rename(columns={'date':'# of bookings'}, inplace=True)\n",
"\n",
"ax = plt.subplot(1, 3, 1)\n",
"ax.set_title(\"# bookings by month\")\n",
"plt.bar(plotdata.index, plotdata['# of bookings'])\n",
"\n",
"# revenue by month\n",
"plotdata2 = pd_bookings[['date','estimated_revenue']].groupby(pd_bookings[\"date\"].dt.month).sum()\n",
"plotdata2.rename(columns={'estimated_revenue':'revenue'}, inplace=True)\n",
"\n",
"ax = plt.subplot(1, 3, 2)\n",
"ax.set_title(\"revenue by month\")\n",
"plt.bar(plotdata2.index, plotdata2['revenue'])\n",
"\n",
"# avg booking price by month\n",
"plotdata3 = pd.concat([plotdata, plotdata2], axis=1)\n",
"plotdata3['avg booking price'] = plotdata3['revenue'] / plotdata3['# of bookings']\n",
"plotdata3.head()\n",
"\n",
"ax = plt.subplot(1, 3, 3)\n",
"ax.set_title(\"avg booking price by month\")\n",
"plt.bar(plotdata3.index, plotdata3['avg booking price'])\n",
"\n",
"_ = plt.plot()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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J0rwzk2dPYct6/tQ7fZIkSZI0xEz6JEmSJGmI2b1TGiAOkS1JkqTZZtInSZLU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\nKQHJuK6mQsIAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1080x360 with 3 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KwVrOl4wKZcp",
"colab_type": "text"
},
"source": [
"Jul, Aug and Sep are the best period to maximise revenue.\n",
"Before May are the best time for maintenance work\n",
"Oct to Dec is a good time to take a break and enjoy the holidays.\n",
"\n",
"Dive into the price, what are the range, and where are the expensive listings? To normalise price to compare between listings, we usually look at per person per night. That's what we usually do when we travel."
]
},
{
"cell_type": "code",
"metadata": {
"id": "GO-W4iuF7YMK",
"colab_type": "code",
"cellView": "form",
"outputId": "78d0a252-d2d8-4e24-b6b4-15dba9e37fb5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 422
}
},
"source": [
"#@title Price distribution of listings, per person, per night\n",
"pd_listings['price_per_person_per_night'] = pd_listings['price'] / pd_listings['accommodates'] / pd_listings['minimum_nights']\n",
"n, bins, patches = plt.hist(pd_listings['price_per_person_per_night'], 100, facecolor='blue', alpha=0.5)\n",
"print(pd_listings['price_per_person_per_night'].describe())"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"count 3818.000000\n",
"mean 27.137816\n",
"std 19.962500\n",
"min 0.037500\n",
"25% 14.375000\n",
"50% 22.500000\n",
"75% 34.500000\n",
"max 275.000000\n",
"Name: price_per_person_per_night, dtype: float64\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "B95w9Qed7bMf",
"colab_type": "code",
"cellView": "form",
"outputId": "e9f5708a-93ca-4850-8af2-bd3f73acd10b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
}
},
"source": [
"#@title Pricier than average Airbnbs\n",
"\n",
"filter_price_gt = 75\n",
"\n",
"plt.figure(figsize=(14, 10))\n",
"\n",
"ax = plt.subplot(1, 2, 1)\n",
"ax.set_title(\"Price per person per night\")\n",
"\n",
"ax.set_autoscaley_on(False)\n",
"ax.set_ylim([47.4, 47.8])\n",
"ax.set_autoscalex_on(False)\n",
"ax.set_xlim([-122.5, -122.2])\n",
"plt.scatter(pd_listings[\"longitude\"],\n",
" pd_listings[\"latitude\"],\n",
" cmap=\"Oranges\",\n",
" c=pd_listings[\"price_per_person_per_night\"] / pd_listings[\"price_per_person_per_night\"].max())\n",
"\n",
"\n",
"ax = plt.subplot(1, 2, 2)\n",
"ax.set_title(\"Pricier than average Airbnbs\")\n",
"\n",
"ax.set_autoscaley_on(False)\n",
"ax.set_ylim([47.4, 47.8])\n",
"ax.set_autoscalex_on(False)\n",
"ax.set_xlim([-122.5, -122.2])\n",
"plt.scatter(pd_listings[pd_listings['price_per_person_per_night'] > filter_price_gt][\"longitude\"],\n",
" pd_listings[pd_listings['price_per_person_per_night'] > filter_price_gt][\"latitude\"],\n",
" cmap=\"coolwarm\",\n",
" c=pd_listings[pd_listings['price_per_person_per_night'] > filter_price_gt][\"price\"] / pd_listings[pd_listings['price_per_person_per_night'] > filter_price_gt][\"price\"].max())\n",
"\n",
"_ = plt.plot()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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IiIjIDimgEhERERER2SEFVCIiIiIiIjukgEpERERERGSHFFCJiIiIiIjskAIqERERERGR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\nERERERGN6f8HbJFSboNqkfEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1008x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AMI3SYKtBldj",
"colab_type": "text"
},
"source": [
"Pricy listings are quite scattered, though we see a cluster of them at the middle.\n",
"\n",
"How about which neighbourhood has the highest revenue?\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "y3U1pXz9CJQD",
"colab_type": "code",
"outputId": "65da9937-5a17-4b22-9902-59e1799bc224",
"cellView": "both",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 767
}
},
"source": [
"#@title Revenue by neighbourhood\n",
"\n",
"pd_neighbourhood_revenue = pd_listings[['neighbourhood_group_cleansed','estimated_revenue']].groupby(['neighbourhood_group_cleansed']).mean().sort_values('estimated_revenue', ascending=False)\n",
"print(pd_neighbourhood_revenue)\n",
"\n",
"pd_listings_plot = pd_listings[['neighbourhood_group_cleansed','longitude','latitude','estimated_revenue']]\n",
"pd_listings_plot.loc[:,'color'] = 0\n",
"\n",
"color_value = 1\n",
"for neighbourhood in pd_neighbourhood_revenue[0:3].index:\n",
" pd_listings_plot.at[pd_listings_plot['neighbourhood_group_cleansed'] == neighbourhood, 'color'] = color_value\n",
" color_value -= 0.2\n",
"\n",
"# plot\n",
"plt.figure(figsize=(4, 7))\n",
"ax = plt.subplot(1, 1, 1)\n",
"ax.set_title(\"Top 3 revenue neighbourhoods\")\n",
"\n",
"ax.set_autoscaley_on(False)\n",
"ax.set_ylim([47.4, 47.8])\n",
"ax.set_autoscalex_on(False)\n",
"ax.set_xlim([-122.5, -122.2])\n",
"plt.scatter(pd_listings_plot['longitude'],\n",
" pd_listings_plot['latitude'],\n",
" cmap=\"coolwarm\",\n",
" c=pd_listings_plot['color']\n",
" )\n",
"\n",
"_ = plt.plot()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
" estimated_revenue\n",
"neighbourhood_group_cleansed \n",
"Downtown 7247.666038\n",
"Capitol Hill 7064.079365\n",
"Beacon Hill 6606.983051\n",
"Ballard 6078.726087\n",
"Queen Anne 6038.752542\n",
"Central Area 4636.371274\n",
"Other neighborhoods 4409.843829\n",
"Cascade 4075.134831\n",
"Seward Park 4063.500000\n",
"Rainier Valley 3827.345912\n",
"Delridge 3641.189873\n",
"Magnolia 3587.819672\n",
"West Seattle 3370.783251\n",
"Northgate 2962.362500\n",
"Lake City 2476.432836\n",
"Interbay 2105.727273\n",
"University District 1558.557377\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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KX6feZqXUYUqpIUqp85RS+uelkZxyfG9SU2wRLRExDJh6Xj8y+sYjciBlrM+n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\n/wB3Xf8Z5W+QtgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 288x504 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5zpsjqElKyl1",
"colab_type": "text"
},
"source": [
"Its clear that Downtown, Capitol Hill and Beacon Hill are the highest revenue neighbourhood. From Google Map, those looks like shopping and CBD district\n",
"\n",
"What are the common group size? is it 2? or family of 4? or larger? \n",
"And if guest usually comes in 2s, is it over saturated? (supply and demand)\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "6o5RFOjBJAaL",
"colab_type": "code",
"outputId": "5b0e8e13-3b26-40b7-c272-e1a1d3a6ed89",
"cellView": "both",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 545
}
},
"source": [
"#@title Guests’ group size, and supply & demand\n",
"\n",
"def get_supply_demand_by(by_column_name, pd_listings, pd_bookings):\n",
" # get supply: number of listing that provide X \n",
" pd_listing_supply_by_pax = pd_listings[[by_column_name,'listing_id']].groupby([by_column_name]).count().sort_values('listing_id', ascending=False)\n",
" pd_listing_supply_by_pax.rename(columns={'listing_id':'# of listings'}, inplace=True)\n",
"\n",
" # get demand base on bookings\n",
" pd_listing_demand_by_pax = pd_bookings[[by_column_name,'id']].groupby([by_column_name]).count().sort_values('id', ascending=False)\n",
" pd_listing_demand_by_pax.rename(columns={'id':'# of bookings'}, inplace=True)\n",
"\n",
" # merge supply and demand\n",
" pd_listing_supply_demand_pax = pd.concat([pd_listing_supply_by_pax, pd_listing_demand_by_pax], axis=1)\n",
" pd_listing_supply_demand_pax['ratio'] = pd_listing_supply_demand_pax['# of bookings'] / pd_listing_supply_demand_pax['# of listings']\n",
"\n",
" pd_listing_supply_demand_pax = pd_listing_supply_demand_pax.sort_values('ratio', ascending=False)\n",
"\n",
" return pd_listing_supply_demand_pax\n",
"\n",
"get_supply_demand_by('accommodates', pd_listings, pd_bookings)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"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># of listings</th>\n",
" <th># of bookings</th>\n",
" <th>ratio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>accommodates</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>3</td>\n",
" <td>83</td>\n",
" <td>27.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1627</td>\n",
" <td>42821</td>\n",
" <td>26.318992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>398</td>\n",
" <td>10170</td>\n",
" <td>25.552764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>25</td>\n",
" <td>520</td>\n",
" <td>20.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>785</td>\n",
" <td>16041</td>\n",
" <td>20.434395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>52</td>\n",
" <td>956</td>\n",
" <td>18.384615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>184</td>\n",
" <td>3221</td>\n",
" <td>17.505435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>332</td>\n",
" <td>5580</td>\n",
" <td>16.807229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>15</td>\n",
" <td>229</td>\n",
" <td>15.266667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>256</td>\n",
" <td>3542</td>\n",
" <td>13.835938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>119</td>\n",
" <td>1501</td>\n",
" <td>12.613445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>4</td>\n",
" <td>48</td>\n",
" <td>12.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" <td>9.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>13</td>\n",
" <td>98</td>\n",
" <td>7.538462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>3</td>\n",
" <td>20</td>\n",
" <td>6.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" # of listings # of bookings ratio\n",
"accommodates \n",
"14 3 83 27.666667\n",
"2 1627 42821 26.318992\n",
"3 398 10170 25.552764\n",
"10 25 520 20.800000\n",
"4 785 16041 20.434395\n",
"7 52 956 18.384615\n",
"5 184 3221 17.505435\n",
"6 332 5580 16.807229\n",
"12 15 229 15.266667\n",
"1 256 3542 13.835938\n",
"8 119 1501 12.613445\n",
"16 4 48 12.000000\n",
"15 2 19 9.500000\n",
"9 13 98 7.538462\n",
"11 3 20 6.666667"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sjAjmW9dFn4l",
"colab_type": "text"
},
"source": [
"A place for 14 ranked first, but the demand is considered low (83 bookings) as compared to places for 2 or 3 person.\n",
"\n",
"Renting a place for 2 or 3 person will give host a pretty good regular rentals.\n",
"\n",
"Do these guests prefer 1 bedroom or 2 separate bedrooms?"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sqMnloeaBOgC",
"colab_type": "code",
"outputId": "d67c7799-9ddb-4e42-ef24-7c13654b3ee5",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
}
},
"source": [
"#@title Bedrooms configurations for 2 person\n",
"get_supply_demand_by('bedrooms', pd_listings[pd_listings['accommodates']<=2], pd_bookings[pd_bookings['accommodates']<=2])"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"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># of listings</th>\n",
" <th># of bookings</th>\n",
" <th>ratio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bedrooms</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>1613</td>\n",
" <td>40897</td>\n",
" <td>25.354619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.0</th>\n",
" <td>261</td>\n",
" <td>5376</td>\n",
" <td>20.597701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>9</td>\n",
" <td>90</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" # of listings # of bookings ratio\n",
"bedrooms \n",
"1.0 1613 40897 25.354619\n",
"0.0 261 5376 20.597701\n",
"2.0 9 90 10.000000"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rWhOATXB_oyH",
"colab_type": "code",
"outputId": "bbeee0c8-9983-4b95-fb60-9df783c12977",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
}
},
"source": [
"#@title Bedrooms configurations for 3 person\n",
"get_supply_demand_by('bedrooms', pd_listings[pd_listings['accommodates']==3], pd_bookings[pd_bookings['accommodates']==3])"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"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># of listings</th>\n",
" <th># of bookings</th>\n",
" <th>ratio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bedrooms</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>311</td>\n",
" <td>8214</td>\n",
" <td>26.411576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.0</th>\n",
" <td>53</td>\n",
" <td>1396</td>\n",
" <td>26.339623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>34</td>\n",
" <td>560</td>\n",
" <td>16.470588</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" # of listings # of bookings ratio\n",
"bedrooms \n",
"1.0 311 8214 26.411576\n",
"0.0 53 1396 26.339623\n",
"2.0 34 560 16.470588"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LJzMgw-5IZbX",
"colab_type": "text"
},
"source": [
"Majority prefers 1 bed room, less than 1% prefers 2 bedrooms (90 out of 40897). But wait, 0 bedrooms, what are kind of apartments are these?"
]
},
{
"cell_type": "code",
"metadata": {
"id": "_bCXM69aCH-b",
"colab_type": "code",
"outputId": "76fd1b5c-89a7-4036-eeb1-b6feb7bc9c4a",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
}
},
"source": [
"#@title Places with 0 bedrooms\n",
"pd_listings[pd_listings['bedrooms']==0][['room_type','listing_id']].groupby(['room_type']).count().sort_values('listing_id', ascending=False)"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"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",
" </tr>\n",
" <tr>\n",
" <th>room_type</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Entire home/apt</th>\n",
" <td>378</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" listing_id\n",
"room_type \n",
"Entire home/apt 378"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8WPuQaDCI_bl",
"colab_type": "text"
},
"source": [
"All these 0 bedrooms apartments are renting their entire home, so we can assume these have 1 room. I hope they provide beds?"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7d60LtfyI-fZ",
"colab_type": "code",
"outputId": "468c2e9e-74dd-44e0-ea31-07631cde0935",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"#@title Number of beds in 0 bedrooms apartments\n",
"pd_bed_count = pd_listings[pd_listings['bedrooms']==0][['beds','listing_id']].groupby(['beds']).count().sort_values('listing_id', ascending=False)\n",
"pd_bed_count.rename(columns={'listing_id':'# of listings'}, inplace=True)\n",
"pd_bed_count"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"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># of listings</th>\n",
" </tr>\n",
" <tr>\n",
" <th>beds</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" # of listings\n",
"beds \n",
"1.0 292\n",
"2.0 76\n",
"3.0 8\n",
"4.0 2"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CcIdrvH9JZZ-",
"colab_type": "text"
},
"source": [
"At least 1 bed are provided in these 0 bedrooms apartments"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HXXs50_63nXc",
"colab_type": "code",
"outputId": "9e49fe93-48d0-4789-abcb-6b4f730e5a8f",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 452
}
},
"source": [
"#@title Popular bed configurations\n",
"pd_bed_count = pd_listings[['beds','listing_id']].groupby(['beds']).count().sort_values('listing_id', ascending=False)\n",
"pd_bed_count.rename(columns={'listing_id':'# of listings'}, inplace=True)\n",
"pd_bed_count"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"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># of listings</th>\n",
" </tr>\n",
" <tr>\n",
" <th>beds</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>2201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7.0</th>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8.0</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9.0</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10.0</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.0</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15.0</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" # of listings\n",
"beds \n",
"1.0 2201\n",
"2.0 912\n",
"3.0 433\n",
"4.0 152\n",
"5.0 73\n",
"6.0 21\n",
"7.0 14\n",
"8.0 4\n",
"9.0 4\n",
"10.0 2\n",
"0.0 1\n",
"15.0 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nOiHT1bJSVWE",
"colab_type": "code",
"outputId": "52e13497-69af-49ff-f4e5-01de9e626779",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328
}
},
"source": [
"#@title Bedroom configurations with highest demands?\n",
"get_supply_demand_by('bedrooms', pd_listings, pd_bookings)"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"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># of listings</th>\n",
" <th># of bookings</th>\n",
" <th>ratio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bedrooms</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>2417</td>\n",
" <td>61399</td>\n",
" <td>25.402979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.0</th>\n",
" <td>378</td>\n",
" <td>8403</td>\n",
" <td>22.230159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>640</td>\n",
" <td>10593</td>\n",
" <td>16.551562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>6</td>\n",
" <td>82</td>\n",
" <td>13.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>283</td>\n",
" <td>3593</td>\n",
" <td>12.696113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>24</td>\n",
" <td>205</td>\n",
" <td>8.541667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>69</td>\n",
" <td>569</td>\n",
" <td>8.246377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7.0</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" # of listings # of bookings ratio\n",
"bedrooms \n",
"1.0 2417 61399 25.402979\n",
"0.0 378 8403 22.230159\n",
"2.0 640 10593 16.551562\n",
"6.0 6 82 13.666667\n",
"3.0 283 3593 12.696113\n",
"5.0 24 205 8.541667\n",
"4.0 69 569 8.246377\n",
"7.0 1 5 5.000000"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LR0fkoRUV_WD",
"colab_type": "text"
},
"source": [
"1 to 2 bedrooms configuration has most demands, or renting the entire home (zero bedrooms)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "sm1EcElYC_Z0",
"colab_type": "code",
"outputId": "888ac593-f57a-490a-93d2-e247582e6a34",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 593
}
},
"source": [
"#@title Which factors (ratings) matters the most to visitors of Seattle? \n",
"\n",
"pd_listings_reviews = pd_listings[['review_scores_rating','review_scores_accuracy','review_scores_cleanliness','review_scores_checkin','review_scores_communication','review_scores_location','review_scores_value']]\n",
"\n",
"def plot_topn_correlation_matrix_with_target(df, target_col_name, k=10):\n",
" corrmat = df.corr()\n",
" top_correlated_columns = corrmat.nlargest(k, target_col_name)[target_col_name].index\n",
" cm = np.corrcoef(df[top_correlated_columns].values.T)\n",
" sns.set(font_scale=1.25)\n",
" hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': k}, yticklabels=top_correlated_columns.values, xticklabels=top_correlated_columns.values)\n",
" \n",
" return top_correlated_columns.values\n",
"\n",
"top_correlated_columns = plot_topn_correlation_matrix_with_target(pd_listings_reviews, 'review_scores_rating')\n",
"\n",
"print(\"Top most correlated columns:\")\n",
"for i in top_correlated_columns:\n",
" if(i!='review_scores_rating'):\n",
" print(i)\n"
],
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"text": [
"Top most correlated columns:\n",
"review_scores_communication\n",
"review_scores_cleanliness\n",
"review_scores_value\n",
"review_scores_accuracy\n",
"review_scores_checkin\n",
"review_scores_location\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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NmjX4+vrSoEEDunTpwvnz519rW1RUxJw5c/D19cXBwYHOnTtz5swZJZuMjAyG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\nQPDhkD95c+L1/yf+Uw5E1apV2bx588duxntj8eLFZ