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Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv, .tsv, and .xlsx.

Recall that CSV, TSV, and Excel files are three common formats for storing data. Three files containing data on 2019 Airbnb listings are available to you:

data/airbnb_price.csv This is a CSV file containing data on Airbnb listing prices and locations.

  • listing_id: unique identifier of listing
  • price: nightly listing price in USD
  • nbhood_full: name of borough and neighborhood where listing is located

data/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

  • listing_id: unique identifier of listing
  • description: listing description
  • room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments

data/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

  • listing_id: unique identifier of listing
  • host_name: name of listing host
  • last_review: date when the listing was last reviewed
# Import necessary packages
import pandas as pd
import numpy as np

# Begin coding here ...
# Use as many cells as you like
#load data from the data folder
#load the csv data using read_csv function from pandas
from pandas import read_csv
a_prices= read_csv('data/airbnb_price.csv')
print(a_prices)


#load the xls file 
a_room_type= 'data/airbnb_room_type.xlsx'
rtype= pd.ExcelFile(a_room_type)
print(rtype.sheet_names)
room_t= pd.read_excel(rtype)
room_t.head()

#load the tsv file using the read_csv 
from pandas import read_csv
review= read_csv('data/airbnb_last_review.tsv', sep= '\t')
print(review)
Hidden output 
#Merging the dataframes using their unique identifiers listing_id
price_type= a_prices.merge(room_t,how= 'right', on= 'listing_id' )
df_merged= price_type.merge(review,how='left', on='listing_id')
print(df_merged.head())
column_names= df_merged.columns
print(column_names)
Hidden output 
df_merged
#determine the earliest and the most recent review dates
#convert relevant review column to date format using pd.to_datetime
df_dated= pd.to_datetime(df_merged['last_review'], infer_datetime_format=True, errors= "coerce")
print(df_dated)


#find the earliest and the most recent reviews
earliest_review=df_dated.iloc[df_dated. argmin()]
most_recent_review=df_dated.max()
print(earliest_review)
print(most_recent_review)


#sort the data by data_review dates
review_sorted_dates= df_dated.sort_values()
print(review_sorted_dates)
Hidden output 
#sum of private rooms
#print the room_type column
print(df_merged['room_type'])

#find unique values of the room_type attributes
df_merged['room_type'].unique()

#convert from object to string data type
df_merged['room_type']= df_merged['room_type'].astype('string')
df_merged['room_type'].info()
#unique values
df_merged['room_type'].unique()
#standardizing capitalization in string data to all lower case
df_merged['room_type']= df_merged['room_type'].str.lower()
print(df_merged['room_type'].unique())
# counting the number of private rooms
import pandas as pd

# Assuming df_merged is already defined somewhere in the notebook
airbnb_df = pd.DataFrame(df_merged)
private_rooms = airbnb_df.loc[airbnb_df['room_type'] == 'private room']
nb_p_rooms = private_rooms.shape[0]

print(nb_p_rooms)

# 11356 private rooms
# To find the average price of listings
# Get the price column
airbnb_df['price']

# Ensure the 'price' column is of string type
airbnb_df['price'] = airbnb_df['price'].astype(str)

# Replace 'dollars' with an empty space
airbnb_df['price'] = airbnb_df['price'].str.replace('dollars', '')

# Remove any extra spaces and convert to float
airbnb_df['price'] = airbnb_df['price'].str.strip().astype(float)

print(airbnb_df['price'])
#find mean
mean_price=(airbnb_df['price'].mean().round(2))
print(mean_price)
#combine new variables
#loading solutions into a dataframe
import pandas as pd
review_dates = pd.DataFrame({'first_reviewed':[earliest_review], 
                             'last_reviewed':[most_recent_review],
                             'nb_private_rooms':[nb_p_rooms], 
                                    'avg_price':[mean_price]})
display(review_dates)