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Introduction to Data Science with Python

Hierarchical Indexing

Explore hierarchical indexing in pandas to manage multi-level indexes on DataFrame axes. Learn to select subsets and single values using loc and xs methods for efficient data analysis.

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Hierarchical indexing is another very important feature of pandas. It makes it possible to have multiple (two or more) index levels on an axis. Putting it somewhat abstractly, it provides a way to work with higher-dimensional data in a lower-dimensional form.

This simple example helps to illustrate this point:

Python 3.5
# Importing numpy and Pandas first
import numpy as np
import pandas as pd
# Set a random seed for reproducibility
np.random.seed(42)  # You can use any integer as the seed value
# Create a Series with a list of lists (or arrays) as the index: 
index = [['a','a','a','b','b','b','c','c','d','d'], # level 1 index 
         [1,2,3,1,2,3,1,2,1,2]] # level 2 index
# Let's create a Series "ser" with multi-level index (2 in this example)
ser = pd.Series(np.random.randn(10), index=index)
print(ser)

With a hierarchically indexed object, we can carry out so-called partial indexing, which enables concise selection of subsets of the data. For example, grabbing a, ...