Data Archive

Multi-level index

Multi-level indexing in pandas—also called hierarchical indexing—enables you to work with higher-dimensional data in a 2D DataFrame. It’s particularly useful for working with grouped or nested data structures.

Why use multi-level index:

  • MultiIndex makes your data interoperable
  • Enables systematic slicing and aggregation
  • Logical grouping of variables

Operations like .stack() and .unstack() rely on MultiIndex to move between long and wide formats.

  • In a flat DataFrame, reshaping often requires column renaming or pivoting.
  • With MultiIndex, it’s structured and reversible.
  • Stack can be used to make a multi index from a flat dataframe.

If you need frequent slicing/aggregation across multiple levels, MultiIndex saves effort and code.

When not to use it

  • If your data is simple or small.
  • If you’re just loading, cleaning, and exporting CSVs.
  • If you don’t need .groupby(level=...), .stack(), or .xs() operations.

Similar to:

  • SQL composite keys
  • Python nested dictionaries
  • JSON hierarchical structures

Related:

In DE_Tools see:

How this mimics a 3D array:

  • You can think of each (Product, Store) pair as defining a “slice” of a 2D array.
  • The columns (Jan, Feb) represent time-like progression (3rd axis).
  • Visually, it’s like you’ve flattened a cube into a matrix while retaining the ability to slice along all original axes.
Month             Jan  Feb
Product   Store            
Product A Store X  100  110
          Store Y  120  115
Product B Store X   90  105
          Store Y   95  100

Backlinks