Categorical data represents variables with a limited and fixed number of possible values (like t-shirt sizes: S, M, L). Converting strings to categorical cuts memory usage dramatically and speeds up groupby operations.
Pandas Categoricals
Reduce memory usage and enforce categorical orders on repeated strings.
Key Takeaways: Pandas Categoricals
Mastering Pandas Categoricals helps you write clean and reliable Pandas code. Follow standard conventions, test edge cases, and keep logic simple. Try running the code example below and complete the exercises to build confidence.
Pandas Categoricals Code Example
Pandasimport pandas as pd
sizes = pd.Series(["M", "L", "S", "M", "L"])
cat_sizes = pd.Categorical(sizes, categories=["S", "M", "L"], ordered=True)
print("Smallest size:", cat_sizes.min())Output
Smallest size: S
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