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Introduction to Data Science with Python

Hierarchical Indexing

Explore hierarchical indexing in pandas to efficiently manage and analyze multi-level indexed data. Understand how to select data across multiple index levels using methods like loc and xs for organized data manipulation.

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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. It enables working with higher-dimensional data in a lower-dimensional representation.

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)

Hierarchical indexing supports partial indexing to select subsets of data. For example, selecting a from the level 1 index returns all associated data along with the ...