Understanding pandas.Series.xs() method (through examples)

Updated: February 17, 2024 By: Guest Contributor Post a comment


The pandas library in Python is a powerful tool for data analysis and manipulation. Among its many features is the Series.xs() method, which can be incredibly useful for extracting data from series with multi-level indices. This tutorial will guide you through the use of the Series.xs() method with detailed examples, ranging from basic to advanced use cases.

Getting Started with pandas.Series.xs()

The xs() method stands for ‘cross section’ and allows for selecting data at a particular level of a multi-level index. It’s a versatile method that can simplify extracting specific slices from a pandas Series object. Before diving into examples, it’s crucial to have pandas installed and a basic understanding of pandas Series. If you haven’t installed pandas, you can do so using pip:

pip install pandas

Once pandas is installed, let’s start by exploring the method through progressively complex examples.

Basic Usage

First, let’s create a simple pandas Series with a multi-level index to see how xs() can be used to extract data:

import pandas as pd

data = {'a': 1, 'b': 2, 'c': 3}
index = pd.MultiIndex.from_tuples([('d', '1'), ('d', '2'), ('e', '1')], names=['level_1', 'level_2'])
series = pd.Series(data, index=index)


d  1    1
   2    2
e  1    3
dtype: int64

Using xs() to extract data associated with ‘d’ in ‘level_1’ of the index:

print(series.xs('d', level='level_1'))


1    1
2    2
dtype: int64

Applying .xs() on Multi-Level Series

As our understanding of the basics settles, let’s delve deeper into using xs() on a series with a more complex multi-level index structure. This will illustrate the flexibility and power of the method when working with hierarchical data.

data = range(1, 7)
index = pd.MultiIndex.from_product([['a', 'b', 'c'], ['1', '2']], names=['level_1', 'level_2'])
series = pd.Series(data, index=index)
print(series.xs('2', level='level_2'))


a    2
b    4
c    6
dtype: int64

This example demonstrates how xs() can be effectively used to select data from a specific secondary level across all primary levels in the index.

Advanced Examples

Moving towards more complex scenarios, let’s consider a Series with even more levels in its index and how we can apply xs() in such situations.

data = list(range(1, 9))
index = pd.MultiIndex.from_product([['a', 'b'], ['1', '2'], ['x', 'y']], names=['L1', 'L2', 'L3'])
series = pd.Series(data, index=index)

# Extracting data where 'L2' is '2' and 'L3' is 'y'
print(series.xs(('2', 'y'), level=['L2', 'L3']))


a    4
b    8
dtype: int64

This example showcases the ability to extract slices of data based on multiple levels of indexing, illustrating the nuanced power of the xs() method.

Handling Missing Index Levels

One might wonder what happens if the specified key or level does not exist. The xs() method is equipped to handle such scenarios gracefully, returning an empty Series if no matching data is found. Let’s see this in action:

print(series.xs('3', level='L2'))


Series([], dtype: int64)

This behavior is particularly useful in data analysis tasks, where the absence of expected data can be just as informative as its presence.


The pandas.Series.xs() method is a robust tool for extracting specific slices of data from series with multi-level indices. Through several examples, we’ve seen how it can simplify the process of accessing hierarchical data structures. Its ability to extract data based on single or multiple levels of indexing adds a layer of flexibility that can immensely benefit data manipulation and analysis tasks. Understanding how to effectively use the xs() method can enhance your data processing workflows, making data extraction both straightforward and versatile.