Tire size constraints

In this lesson, you're going to build on top of the work you've been doing with the ride_sharing DataFrame. You'll be working with the tire_sizes column which contains data on each bike's tire size.

Bicycle tire sizes could be either 26″, 27″ or 29″ and are here correctly stored as a categorical value. In an effort to cut maintenance costs, the ride sharing provider decided to set the maximum tire size to be 27″.

In this exercise, you will make sure the tire_sizes column has the correct range by first converting it to an integer, then setting and testing the new upper limit of 27″ for tire sizes.

This exercise is part of the course

Cleaning Data in Python

Exercise instructions

Convert the tire_sizes column from category to 'int'.
Use .loc[] to set all values of tire_sizes above 27 to 27.
Reconvert back tire_sizes to 'category' from int.
Print the description of the tire_sizes.

Hands-on interactive exercise

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

# Convert tire_sizes to integer
ride_sharing['tire_sizes'] = ____['____'].____('____')

# Set all values above 27 to 27
ride_sharing.____[____ > ____, ____] = ____

# Reconvert tire_sizes back to categorical
ride_sharing['tire_sizes'] = ____

# Print tire size description
print(ride_sharing['tire_sizes'].____())

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

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