Beperkingen voor bandmaten

In deze les bouw je verder op het werk dat je al hebt gedaan met de DataFrame ride_sharing. Je werkt met de kolom tire_sizes, die de bandmaat van elke fiets bevat.

Fietsbandmaten kunnen 26″, 27″ of 29″ zijn en zijn hier correct opgeslagen als een categorische waarde. Om onderhoudskosten te verlagen, heeft de aanbieder van deelfietsen besloten de maximale bandmaat in te stellen op 27″.

In deze oefening zorg je dat de kolom tire_sizes binnen het juiste bereik valt door deze eerst naar een integer te converteren, vervolgens de nieuwe bovengrens van 27″ voor bandmaten te zetten en te testen.

Deze oefening maakt deel uit van de cursus

Data opschonen in Python

Cursus bekijken

Oefeninstructies

Converteer de kolom tire_sizes van category naar 'int'.
Gebruik .loc[] om alle waarden van tire_sizes boven 27 op 27 te zetten.
Converteer tire_sizes daarna terug van int naar 'category'.
Print de beschrijving van 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'].____())

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Data opschonen 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: Beperkingen op gegevenstypen Exercise 2: Veelvoorkomende gegevenstypen Exercise 3: Numerieke data of ... ?Exercise 4: Strings optellen en getallen aan elkaar plakken Exercise 5: Bereikbeperkingen voor data Exercise 6: Beperkingen voor bandmaten

Huidige oefening

Exercise 7: Terug naar de toekomst Exercise 8: Uniciteitsbeperkingen Exercise 9: Hoe groot is je subset?Exercise 10: Duplicaten vinden Exercise 11: Duplicaten behandelen

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!