import pandas as pd pd.__version__

# Download a sample file from http://insideairbnb.com/ ! wget http://data.insideairbnb.com/united-states/fl/broward-county/2022-06-17/visualisations/listings.csv -O listings.csv

# read the airbnb NYC listings csv file airbnb = pd.read_csv("listings.csv")

# display the pandas DataFrame display(airbnb)

# View first few entries airbnb.head

# View last few entries airbnb.tail()

# Results for a single column airbnb['name']

# results for multiple columns hosts = airbnb[['host_id', 'host_name']] hosts.head()

# Show the data types for each column airbnb.dtypes

# Change the type of a column to datetime airbnb['last_review'] = pd.to_datetime(airbnb['last_review']) airbnb.dtypes

# extract the year from a datetime series airbnb['year'] = airbnb['last_review'].dt.year airbnb['year'].head()

# Strip leading and trailing spaces from a string series airbnb['name'] = airbnb['name'].str.strip() airbnb['name'].head()

# Uppercase all strings in a series airbnb['name_upper'] = airbnb['name'].str.upper() airbnb['name_upper'].tail()

# lowercase all strings in a series airbnb['name_lower'] = airbnb['name'].str.lower() airbnb['name_lower'].tail()

# calculate using two columns airbnb['min_revenue'] = airbnb['minimum_nights'] * airbnb['price'] airbnb[['minimum_nights', 'price', 'min_revenue']].head()

# get the mean price airbnb['price'].mean()