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STATISTICS AND PLOTS

Data

Here we will work with public Data from airbnb, released under License CC0: Public Domain, which comes from Kaggle

Context

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in NYC, NY for 2019.

Content

This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.

Inspiration

• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

Acknowledgements

Part of this notebook is based on the work of Dgomonov, published in Kaggle under the Apache 2.0 open source license, adapted by Juan Carlos Basto Pineda. I encourage you to check out the original notebook for further details.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

airbnb=pd.read_csv("data/AB_NYC_2019.csv")

• Check the content, the size, and some summary statistics of the dataset

# Your Answer Here

#checking type of every column in the dataset
airbnb.dtypes

#dropping columns we are not interested in (or for ethical reasons)
airbnb.drop(['id','host_name','last_review'], axis=1, inplace=True)
#examing the changes
airbnb.head(3)

Let's check about NaN, and take care of them

airbnb.isnull().sum()

airbnb.shape

airbnb.fillna({'reviews_per_month':0}, inplace=True)

Let's check the values of some categorical variables

#let's proceed with examing some interesting categorical unique values
airbnb.neighbourhood_group.unique()

airbnb.neighbourhood_group.value_counts()

airbnb.room_type.unique()

• How many different values there are in the column neighbourhood?

# Your Answer Here

Which are the top 10 hosts, with the largest number of entries?

# Your Answer Here

• What are the following commands doing?

top_host_df = pd.DataFrame(top_host)
top_host_df.reset_index(inplace=True)
top_host_df.rename(columns={'index':'Host_ID', 'host_id':'P_Count'}, inplace=True)
top_host_df

Handy plots with seaborn and DataFrames

sns.barplot(x="Host_ID", y="P_Count", data=top_host_df,palette='Blues_d')
plt.xticks(rotation=45)

sns.boxplot(x = "neighbourhood_group", y='price', data = airbnb)
plt.xticks(rotation=45)

Looking for correlations between the variables in the DataFrame

airbnb.corr()