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 listingprice: nightly listing price in USDnbhood_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 listingdescription: listing descriptionroom_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 listinghost_name: name of listing hostlast_review: date when the listing was last reviewed
In [23]:
# We've loaded your first package for you! You can add as many cells as you need.
import numpy as np
import pandas as pd
# Begin coding here ...
price = pd.read_csv("datasets/airbnb_price.csv")
room = pd.read_excel("datasets/airbnb_room_type.xlsx")
review = pd.read_csv("datasets/airbnb_last_review.tsv", sep='\t')
In [24]:
price.head()
Out[24]:
| listing_id | price | nbhood_full | |
|---|---|---|---|
| 0 | 2595 | 225 dollars | Manhattan, Midtown |
| 1 | 3831 | 89 dollars | Brooklyn, Clinton Hill |
| 2 | 5099 | 200 dollars | Manhattan, Murray Hill |
| 3 | 5178 | 79 dollars | Manhattan, Hell's Kitchen |
| 4 | 5238 | 150 dollars | Manhattan, Chinatown |
In [25]:
room.head()
Out[25]:
| listing_id | description | room_type | |
|---|---|---|---|
| 0 | 2595 | Skylit Midtown Castle | Entire home/apt |
| 1 | 3831 | Cozy Entire Floor of Brownstone | Entire home/apt |
| 2 | 5099 | Large Cozy 1 BR Apartment In Midtown East | Entire home/apt |
| 3 | 5178 | Large Furnished Room Near B'way | private room |
| 4 | 5238 | Cute & Cozy Lower East Side 1 bdrm | Entire home/apt |
In [26]:
review.head()
Out[26]:
| listing_id | host_name | last_review | |
|---|---|---|---|
| 0 | 2595 | Jennifer | May 21 2019 |
| 1 | 3831 | LisaRoxanne | July 05 2019 |
| 2 | 5099 | Chris | June 22 2019 |
| 3 | 5178 | Shunichi | June 24 2019 |
| 4 | 5238 | Ben | June 09 2019 |
- What are the dates of the earliest and most recent reviews? Store these values as two separate variables with your preferred names.
In [27]:
review["last_review"] = pd.to_datetime(review["last_review"], infer_datetime_format=True, errors='coerce')
review["last_review"].head()
Out[27]:
0 2019-05-21 1 2019-07-05 2 2019-06-22 3 2019-06-24 4 2019-06-09 Name: last_review, dtype: datetime64[ns]
In [28]:
first_review_date = review["last_review"].min()
last_review_date = review["last_review"].max()
display(first_review_date, last_review_date)
Timestamp('2019-01-01 00:00:00')
Timestamp('2019-07-09 00:00:00')
- How many of the listings are private rooms? Save this into any variable.
In [29]:
room["room_type"] = room["room_type"].str.lower()
room["room_type"].value_counts()
Out[29]:
entire home/apt 13266 private room 11356 shared room 587 Name: room_type, dtype: int64
In [30]:
private_rooms = room["room_type"].value_counts()["private room"]
display(private_rooms)
11356
- What is the average listing price? Round to the nearest penny and save into a variable.
In [31]:
price["price"].head()
Out[31]:
0 225 dollars 1 89 dollars 2 200 dollars 3 79 dollars 4 150 dollars Name: price, dtype: object
In [32]:
price["price"] = price["price"].str.replace(" dollars", "")
price["price"].head()
Out[32]:
0 225 1 89 2 200 3 79 4 150 Name: price, dtype: object
In [33]:
price["price"] = price["price"].astype("float64")
average_price = round(price["price"].mean(), 2)
display(average_price)
141.78
- Combine the new variables into one DataFrame called review_dates with four columns in the following order: first_reviewed, last_reviewed, nb_private_rooms, and avg_price. The DataFrame should only contain one row of values.
In [34]:
results = {
"first_reviewed": [first_review_date],
"last_reviewed": [last_review_date],
"nb_private_rooms": [private_rooms],
"avg_price": [average_price]
}
review_dates = pd.DataFrame(results)
display(review_dates)
| first_reviewed | last_reviewed | nb_private_rooms | avg_price | |
|---|---|---|---|---|
| 0 | 2019-01-01 | 2019-07-09 | 11356 | 141.78 |