Pandas: Understanding DataFrame.reindex_like() method

Updated: February 20, 2024 By: Guest Contributor Post a comment


The DataFrame.reindex_like() method in Pandas is a powerful tool that allows you to reshape your data frame to match the indexes of another data frame or series. This method is crucial for data preparation and cleaning when you want to align two datasets by their indexes. Understanding how to use this method effectively can significantly streamline your data manipulation tasks. In this guide, we’ll explore the reindex_like() method with various code examples, moving from basic applications to more advanced use cases.

When to Use reindex_like()?

The reindex_like() function is used to conform a data frame to the same index and column structure as another. This is very useful when working with time series data, combining datasets of different lengths, or aligning features before performing operations such as concatenation, merging, or comparison.

Let’s start with some basic code examples and gradually move to more complex applications.

Basic Usage

Imagine we have two data frames, df1 and df2, with different sets of columns and rows. Our goal is to reshape df1 to have the same structure as df2.

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2, 3],
                     'B': [4, 5, 6]})

df2 = pd.DataFrame({'C': [7, 8],
                     'D': [9, 10],
                     'E': [11, 12]},
                     index=[1, 2])

# Using reindex_like to match df1 with df2 structure
reindexed_df1 = df1.reindex_like(df2)



    A    B
1 NaN  NaN
2 NaN  NaN

In this basic example, we see that reindexed_df1 has the same index as df2 but does not have the same columns, resulting in populated NaN values where the data does not exist.

Matching Columns and Indexes

For a more effective application of reindex_like(), it’s important to ensure that both dataframes have some overlap in columns or indexes to avoid an excessive amount of NaN values. Let’s modify our first example to include a common column:

df1 = pd.DataFrame({'A': [1, 2, 3, 4],
                     'C': [5, 6, 7, 8]})

df2 = pd.DataFrame({'C': [9, 10],
                     'D': [11, 12]},
                     index=[2, 3])

reindexed_df1 = df1.reindex_like(df2)



     A    C
2  3.0  7.0
3  4.0  8.0

This time, reindexed_df1 not only matched the index of df2 but also contained common columns, leading to more meaningful data alignment.

Advanced Applications

Now that we understand the basics of reindexing, let’s dive into some advanced applications. One powerful feature of reindex_like() is the ability to fill missing values during the reindexing process using various methods such as forward fill (ffill), backward fill (bfill), or specifying a fill value.

Let’s see how to use ffill to handle missing values:

df1 = pd.DataFrame({'A': [1, 2, 3, 4],
                     'B': [5, 6, 7, 8]},
                     index=[0, 1, 2, 3])

df2 = pd.DataFrame({'A': [0], 'B': [0]}, index=[0])

# Reindex df1 like df2 and forward fill missing values
reindexed_df1 = df1.reindex_like(df2).ffill()


Although reindexed_df1 was initially reindexed to match only the first row of df2, the ffill method then fills the subsequent missing values with the last available value, leading to an accurate and useful reindexed DataFrame.

Handling Data Types During Reindexing

It’s also important to consider data types when using reindex_like(). If the original and target data frames have different data types for their respective columns, unexpected type conversions can occur. To illustrate, let’s modify our example:

# Assuming df1 and df2 from previous examples
# Modify df2 to have a column with different data type

# Reindex df1 to match df2's structure and data types
reindexed_df1 = df1.reindex_like(df2, dtype='float')


Notice how specifying a dtype during reindexing can ensure that the resulting data frame conforms not only in structure but also in the expected data types.


The DataFrame.reindex_like() method is a versatile tool for data alignment and manipulation in Pandas. By understanding how to effectively use it, you can ensure that your data sets are properly prepared for analysis, leading to more reliable and insightful results.