Practice DataFrame Mutations using Airbnb Data

Create a new column called price_per_night that calculates the price per night for each Airbnb listing.

Modify the original DataFrame df.

Modify the original DataFrame df.

Add a new row to the DataFrame df at the end with the following details:

new_row_data = {'id': 12345, 'name': 'Cozy Studio Apartment', 'host_id': 67890, 'room_type': 'Entire home/apt',
                'price': 150, 'minimum_nights': 3, 'number_of_reviews': 10}

Sort the DataFrame df by the price column in descending order and assign the result to sorted_df.

Create a new column price_eur in the DataFrame df that contains the prices in euros. The conversion rate is 1 US dollar = 0.85 euros.

Create a new column total_nights_price in the DataFrame df that contains the total price of nights for each listing. The total price of nights is calculated by multiplying the minimum_nights column with the price_per_night column.

For splitting a string into a list of words, you can use the split() function. The split() function returns a list of words. To access the first word in the list, you can use indexing. For example, text.split().str[0] returns the first word from the name column.

Create a new column year in the DataFrame df that contains the year information from the last_review column. For example, if the last_review column contains the date 2019-05-21, the year column should contain the value 2019. You can use the dt accessor to access the datetime properties of a column. For example, df['last_review'].dt.year returns the year information from the last_review column.