Pandas MultiIndex — Hierarchical Data Structures — Free Data Analytics Tutorial
Learn Pandas MultiIndex — Hierarchical Data Structures in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Pandas MultiIndex — Hierarchical Data Structures in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian stu…
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Pandas MultiIndex — Hierarchical Data Structures in Data Analytics
MultiIndex (multi-level index) is used when your data has more than one level of categories. Example: Sales data with (City, Product, Year). Instead of three separate columns for grouping, you can create a hierarchical index.
Benefits: Compact representation, efficient grouping operations, natural representation of hierarchical data, easier to reshape and pivot data.
Creating MultiIndex: from product (list of tuples), from arrays (levels + codes), from existing columns (set_index).
Operations on MultiIndex: Partial indexing (df.loc["City"]), groupby across levels, level-specific operations, swaplevel to reorder hierarchy.
Pandas MultiIndex — Hierarchical Data Structures — Syntax
# Creating MultiIndex # df = df.set_index(['city', 'product']) # 2-level index # df.loc['Delhi'] # select all Delhi rows # df.groupby(level='product').mean() # group by product level # df.unstack() # pivot: move level to columns
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