Kennistoets geheugengebruik

Categorische pandas-Series kunnen veel geheugen besparen — soms wel tot 90% van het geheugengebruik van de oorspronkelijke kolom. Als elk van de genoemde kolommen wordt omgezet naar een categorische Series, welke kolom bespaart dan het meeste geheugen? De used_cars-gegevensset is voor je geladen.

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Werken met categorische data in Python

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Werken met categorische data in Python

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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: Setting category variables Exercise 2: Setting categories Exercise 3: Adding categories Exercise 4: Removing categories Exercise 5: Updating categories Exercise 6: Collapsing categories knowledge check Exercise 7: Renaming categories Exercise 8: Collapsing categories Exercise 9: Reordering categories Exercise 10: Reordering categories in a Series Exercise 11: Using .groupby() after reordering Exercise 12: Cleaning and accessing data Exercise 13: Cleaning variables Exercise 14: Accessing and filtering data

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: Valkuilen bij categorische data Exercise 2: Kennistoets geheugengebruik

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Exercise 3: Valkuilen overwinnen: stringproblemen Exercise 4: Valkuilen voorkomen: NumPy-arrays gebruiken Exercise 5: Label encoding Exercise 6: Maak een label-encoding en een map Exercise 7: Opgeslagen mappings gebruiken Exercise 8: Een Booleaanse codering maken Exercise 9: One-hot encoding Exercise 10: One-hot kennistoets Exercise 11: One-hot encoden van specifieke kolommen Exercise 12: Afsluitende video