Treating duplicates

In the last exercise, you were able to verify that the new update feeding into ride_sharing contains a bug generating both complete and incomplete duplicated rows for some values of the ride_id column, with occasional discrepant values for the user_birth_year and duration columns.

In this exercise, you will be treating those duplicated rows by first dropping complete duplicates, and then merging the incomplete duplicate rows into one while keeping the average duration, and the minimum user_birth_year for each set of incomplete duplicate rows.

This exercise is part of the course

Cleaning Data in Python

Exercise instructions

Drop complete duplicates in ride_sharing and store the results in ride_dup.
Create the statistics dictionary which holds minimum aggregation for user_birth_year and mean aggregation for duration.
Drop incomplete duplicates by grouping by ride_id and applying the aggregation in statistics.
Find duplicates again and run the assert statement to verify de-duplication.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Drop complete duplicates from ride_sharing
ride_dup = ____.____()

# Create statistics dictionary for aggregation function
statistics = {'user_birth_year': ____, 'duration': ____}

# Group by ride_id and compute new statistics
ride_unique = ride_dup.____('____').____(____).reset_index()

# Find duplicated values again
duplicates = ride_unique.____(subset = 'ride_id', keep = False)
duplicated_rides = ride_unique[duplicates == True]

# Assert duplicates are processed
assert duplicated_rides.shape[0] == 0

Edit and Run Code

This exercise is part of the course

Cleaning Data in Python

IntermediateSkill Level

4.8+

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In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Exercise 1: Data type constraints Exercise 2: Common data types Exercise 3: Numeric data or ... ?Exercise 4: Summing strings and concatenating numbers Exercise 5: Data range constraints Exercise 6: Tire size constraints Exercise 7: Back to the future Exercise 8: Uniqueness constraints Exercise 9: How big is your subset?Exercise 10: Finding duplicates Exercise 11: Treating duplicates

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