Skip to Content
Learn
Working with Multiple DataFrames
Review

This lesson introduced some methods for combining multiple DataFrames:

  • Creating a DataFrame made by matching the common columns of two DataFrames is called a merge
  • We can specify which columns should be matches by using the keyword arguments left_on and right_on
  • We can combine DataFrames whose rows don’t all match using left, right, and outer merges and the how keyword argument
  • We can stack or concatenate DataFrames with the same columns using pd.concat

Instructions

1.

Cool T-Shirts Inc. just created a website for ordering their products. They want you to analyze two datasets for them:

  • visits contains information on all visits to their landing page
  • checkouts contains all users who began to checkout on their website

Use print to inspect each DataFrame.

2.

We want to know the amount of time from a user’s initial visit to the website to when they start to check out.

Use merge to combine visits and checkouts and save it to the variable v_to_c.

3.

In order to calculate the time between visiting and checking out, define a column of v_to_c called time by pasting the following code into script.py:

v_to_c['time'] = v_to_c.checkout_time - \ v_to_c.visit_time print(v_to_c)
4.

To get the average time to checkout, paste the following code into script.py:

print(v_to_c.time.mean())
Folder Icon

Sign up to start coding

Already have an account?