Ordinale categorische variabelen

Kolommen over iemands inkomen hoeven niet numeriek te zijn. Als het inkomensbedrag is opgesplitst in afzonderlijke categorieën, is de nieuwe kolom categorisch. Omdat deze categorieën een natuurlijke volgorde hebben (van kleinste naar grootste inkomen), wordt de variabele als ordinair beschouwd.

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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: Introductie van de cursus Exercise 2: Categorisch vs. numeriek Exercise 3: Een doelvariabele verkennen Exercise 4: Ordinale categorische variabelen

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Exercise 5: Categorische gegevens in pandas Exercise 6: dtypes instellen en geheugen besparen Exercise 7: Een categorische pandas-Series maken Exercise 8: dtype instellen bij het inlezen van data Exercise 9: Gegevens groeperen op categorie in pandas Exercise 10: Maak heel veel groepen Exercise 11: Een .groupby()-instructie opzetten Exercise 12: pandas-functies effectief gebruiken

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: 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