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Index level

As explained in Pandas Workout: 200 exercises to make you a stronger data analyst

An index level is one component of a hierarchical pandas index, containing labels for one dimension of row identification.

Think of a hierarchical pandas index as a set of labeled questions asked in order. Each index level supplies one dimension of row identification: Year, Season, Sport, or Event. section 4.10

Ask a question one level at a time

An Olympic-events DataFrame can use four columns as index levels, then sort rows by Year, Season, Sport, and Event. To find mean athlete age for Summer games from 1936 through 2000, slice the first level by year, select Summer in the second, and leave later levels unrestricted. The same pattern selects Archery teams or Tennis heights. section 4.10

How Pandas Workout: 200 exercises to make you a stronger data analyst covers it

How deep it goes
Explains four-level ordering and practical slicing, while stopping short of index implementation internals.
Examples
Uses Olympic athlete data to select years, seasons, sports, events, teams, ages, and heights.
What you can do after
Apply it via pandas to construct, sort, slice, and aggregate hierarchical DataFrame indexes.
How it connects
Connects index levels with MultiIndex, DataFrame, sort_index, label-based selection, slicing, and aggregation.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Pandas Workout: 200 exercises to make you a stronger data analyst. The section links point to the parts of the book it draws on.