In this lab, you'll get some hands-on practice creating and using lambda functions.

In this lab you will:

• Create lambda functions to use as arguments of other functions
• Use the .map() or .apply() method to apply a function to a pandas series or DataFrame

import pandas as pd
df = pd.read_csv('Yelp_Reviews.csv', index_col=0)
df.head(2)

Use a lambda function to create a new column called 'stars_squared' by squaring the stars column.

# Your code here

Select the month from the date string using a lambda function.

# Your code here

Do this with a single line of code.

# Your code here

# Your code here

Create a new column 'Review_Length' by applying this lambda function to the 'Review_num_words' column.

# Rewrite the following function as a lambda function
def rewrite_as_lambda(value):
    if len(value) < 50:
        return 'Short'
    elif len(value) < 80:
        return 'Medium'
    else:
        return 'Long'
# Hint: nest your if, else conditionals

df['Review_length'] = None

Print the first five rows of the 'date' column.

# Your code here

Overwrite the 'date' column by reordering the month and day from YYYY-MM-DD to DD-MM-YYYY. Try to do this using a lambda function.

# Your code here

Hopefully, you're getting the hang of lambda functions now! It's important not to overuse them - it will often make more sense to define a function so that it's reusable elsewhere. But whenever you need to quickly apply some simple processing to a collection of data you have a new technique that will help you to do just that. It'll also be useful if you're reading someone else's code that happens to use lambdas.