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.

Deze oefening maakt deel uit van de cursus

Cleaning Data in Python

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Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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'].____())

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Deze oefening maakt deel uit van de cursus

Cleaning Data in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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

Huidige oefening

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

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Exercise 1: Membership constraints Exercise 2: Members only Exercise 3: Finding consistency Exercise 4: Categorical variables Exercise 5: Categories of errors Exercise 6: Inconsistent categories Exercise 7: Remapping categories Exercise 8: Cleaning text data Exercise 9: Removing titles and taking names Exercise 10: Keeping it descriptive

In this chapter, you'll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You'll also gain invaluable skills that will help you verify that values have been added correctly, and that missing values don't negatively impact your analyses.

Exercise 1: Uniformity Exercise 2: Ambiguous dates Exercise 3: Uniform currencies Exercise 4: Uniform dates Exercise 5: Cross field validation Exercise 6: Cross field or no cross field?Exercise 7: How's our data integrity?Exercise 8: Completeness Exercise 9: Is this missing at random?Exercise 10: Missing investors Exercise 11: Follow the money

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you'll then use your new skills to join two restaurant review datasets into one clean master dataset.

Exercise 1: Comparing strings Exercise 2: Minimum edit distance Exercise 3: The cutoff point Exercise 4: Remapping categories II Exercise 5: Generating pairs Exercise 6: To link or not to link?Exercise 7: Pairs of restaurants Exercise 8: Similar restaurants Exercise 9: Linking DataFrames Exercise 10: Getting the right index Exercise 11: Linking them together!Exercise 12: Congratulations!