Understanding pandas.Series.reindex_like() method through examples

Updated: February 18, 2024 By: Guest Contributor Post a comment


In this tutorial, we’re going to dive deep into the pandas.Series.reindex_like() method through comprehensive examples. pandas is a cornerstone in the Python data manipulation and analysis ecosystem, offering powerful tools to manipulate large and complex datasets efficiently. The reindex_like() method is one such tool, allowing for the reindexing of a series to match the index of another object. This tutorial aims to put forth a clear understanding of how to use this method effectively, escalating from basic to more advanced use cases.

Getting Started

Before we delve into examples, it’s crucial to understand the basics of the pandas library and the Series object. If you’re not familiar with pandas, it’s advisable to take a quick tour of its functionalities. Assuming basic familiarity, let’s start by importing pandas:

import pandas as pd

And now, let’s create two simple Series objects for our examples:

s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([4, 5], index=['a', 'b'])

Basic Usage

The first example demonstrates the basic usage of reindex_like(). Let’s say we want s2 to have the same index as s1:

s2_reindexed = s2.reindex_like(s1)


a    4.0
b    5.0
c    NaN
data type: float64

This example shows that the reindexing process can introduce missing values (NaN) for indices that exist in the target (s1) but not in the source (s2). This is a fundamental concept when working with reindex_like().

Handling Missing Values

In many scenarios, you may want to fill missing values introduced during reindexing. Let’s see how to handle this:

s2_reindexed_filled = s2.reindex_like(s1).fillna(0)


a    4.0
b    5.0
c    0.0
data type: float64

Here, we used the fillna(0) method to replace all NaN values with 0. This technique is useful in maintaining data integrity when dealing with numerical datasets where missing values can be logically replaced.

Advanced Usage

Now, let’s look into a more complex example involving a third Series object with a different index structure:

s3 = pd.Series([7, 8, 9], index=['c', 'd', 'e'])
s3_reindexed = s3.reindex_like(s1)


a    NaN
b    NaN
c    7.0
data type: float64

This illustrates a situation where the source Series (`s3`) has a partially overlapping index with the target (`s1`). The method accurately aligns the indices where possible and introduces NaN values for the non-overlapping parts.

Custom Index Reordering

Another advanced use case involves creating a custom index that doesn’t directly align with either the source or the target, but still uses reindex_like() to achieve the desired structure. Here’s how:

s_custom = pd.Series([10, 11, 12], index=['x', 'y', 'z'])
s2_custom_reindexed = s2.reindex_like(s_custom)


x    NaN
y    NaN
z    NaN
data type: float64

This reveals that reindexing against a completely different index results in an essentially blank series (in the context of the original data), showcasing the versatility of this method to adhere to any index structure given, even if it means populating it with NaN values.


The pandas.Series.reindex_like() method is an intricate tool in the data manipulation toolkit, capable of adapting a series’ index to match that of another object, introducing a realm of possibilities in data analysis. The examples provided showcase how it can manage both simple and complex index alignments, with the flexibility to handle missing values or completely realign data structures. Understanding and leveraging this functionality can greatly enhance data preprocessing and analysis tasks.