Gegevens raadplegen en filteren

Je werkt aan een Python-applicatie die informatie toont over honden die beschikbaar zijn voor adoptie bij het lokale asiel. Sommige variabelen van belang, zoals "breed", "size" en "coat", zijn opgeslagen als categorische variabelen. Zodat de applicatie goed werkt, moet je gegevens kunnen raadplegen en filteren met behulp van deze kolommen.

De variabele ID is ingesteld als de index van de pandas DataFrame dogs.

Deze oefening maakt deel uit van de cursus

Werken met categorische data in Python

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# Print the category of the coat for ID 23807
print(dogs.loc[____, ____])

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Deze oefening maakt deel uit van de cursus

Werken met categorische data in Python

SkillTag.level.intermediateSkillTag.label

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Almost every dataset contains categorical information—and often it’s an unexplored goldmine of information. In this chapter, you’ll learn how pandas handles categorical columns using the data type category. You’ll also discover how to group data by categories to unearth great summary statistics.

Exercise 1: Course introduction Exercise 2: Categorical vs. numerical Exercise 3: Exploring a target variable Exercise 4: Ordinal categorical variables Exercise 5: Categorical data in pandas Exercise 6: Setting dtypes and saving memory Exercise 7: Creating a categorical pandas Series Exercise 8: Setting dtype when reading data Exercise 9: Grouping data by category in pandas Exercise 10: Create lots of groups Exercise 11: Setting up a .groupby() statement Exercise 12: Using pandas functions effectively

Now it’s time to learn how to set, add, and remove categories from a Series. You’ll also explore how to update, rename, collapse, and reorder categories, before applying your new skills to clean and access other data within your DataFrame.

Exercise 1: Categorische variabelen instellen Exercise 2: Categorieën instellen Exercise 3: Categorieën toevoegen Exercise 4: Categorieën verwijderen Exercise 5: Categorieën bijwerken Exercise 6: Kennischeck: categorieën samenvoegen Exercise 7: Categorieën hernoemen Exercise 8: Categorieën samenvoegen Exercise 9: Categorieën opnieuw ordenen Exercise 10: Categorieën in een Series opnieuw ordenen Exercise 11: Gebruik .groupby() na het herordenen Exercise 12: Gegevens opschonen en benaderen Exercise 13: Variabelen opschonen Exercise 14: Gegevens raadplegen en filteren

Huidige oefening

In this chapter, you’ll use the seaborn Python library to create informative visualizations using categorical data—including categorical plots (cat-plot), box plots, bar plots, point plots, and count plots. You’ll then learn how to visualize categorical columns and split data across categorical columns to visualize summary statistics of numerical columns.

Exercise 1: Introduction to categorical plots using Seaborn Exercise 2: Boxplot understanding Exercise 3: Creating a box plot Exercise 4: Seaborn bar plots Exercise 5: Creating a bar plot Exercise 6: Ordering categories Exercise 7: Bar plot using hue Exercise 8: Point and count plots Exercise 9: Creating a point plot Exercise 10: Creating a count plot Exercise 11: Review catplot() types Exercise 12: Additional catplot() options Exercise 13: One visualization per group Exercise 14: Updating categorical plots

Lastly, you’ll learn how to overcome the common pitfalls of using categorical data. You’ll also grow your data encoding skills as you are introduced to label encoding and one-hot encoding—perfect for helping you prepare your data for use in machine learning algorithms.

Exercise 1: Categorical pitfalls Exercise 2: Memory usage knowledge check Exercise 3: Overcoming pitfalls: string issues Exercise 4: Overcoming pitfalls: using NumPy arrays Exercise 5: Label encoding Exercise 6: Create a label encoding and map Exercise 7: Using saved mappings Exercise 8: Creating a Boolean encoding Exercise 9: One-hot encoding Exercise 10: One-hot knowledge check Exercise 11: One-hot encoding specific columns Exercise 12: Wrap-up video