A detailed guide to DataFrame.reindex() method in Pandas

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


The DataFrame.reindex() method in Pandas is a fundamental tool for data manipulation and analysis, allowing users to conform an existing DataFrame to a new index. It facilitates the reordering of data to match a given set of labels, the insertion of missing values in places where no data is available for a particular label, and much more. This detailed guide will take you through the ins and outs of reindex(), from basic usage to more advanced applications.

Syntax & Parameters

At its core, reindex() allows for the alignment of data according to a new set of labels. This is particularly useful in situations where you might have data from different sources that need to be combined, or when applying operations that require a specific order of rows or columns.

The basic syntax of reindex() is:

    fill_value=np.NaN,  # Assuming import numpy as np


  • labels: New labels / index to conform the axis specified by axis.
  • axis: Index or columns. Axis to reindex.
  • method: Method to use for filling holes in reindexed DataFrame.
  • level: Align on this level of a MultiIndex.
  • copy: Return a new object, even if the passed indexes are the same.
  • limit: Maximum number of consecutive elements to forward or backward fill.
  • tolerance: Maximum distance between original and new labels for forward or backward filling to work.
  • fill_value: Value to use for missing values. Defaults to np.NaN.
  • numeric_only: Only apply to numeric columns when axis=0 (columns).

Basic Usage

Let’s start by creating a simple DataFrame:

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': range(3), 'B': range(3, 6)})

This produces:

   A  B
0  0  3
1  1  4
2  2  5

Now let’s reindex the DataFrame to add a missing index:

df_reindexed = df.reindex([0, 1, 2, 3])

The result will show that the new row at index 3 contains NaN values, as expected:

     A    B
0  0.0  3.0
1  1.0  4.0
2  2.0  5.0
3  NaN  NaN

Reindexing Columns

Next, we demonstrate how to reindex columns. Suppose we want to add an additional column ‘C’ to our DataFrame:

df_reindexed = df.reindex(columns=['A', 'B', 'C'])

This adds the new column ‘C’ with NaN values:

   A  B   C
0  0  3 NaN
1  1  4 NaN
2  2  5 NaN

Advanced Usage

Moving towards more advanced scenarios, the reindex() method also supports a method parameter. This parameter can be particularly useful for filling missing values in a more sophisticated manner than simply inserting NaNs. The available methods include ‘pad’ / ‘ffill’ for forward filling and ‘bfill’ / ‘backfill’ for backward filling:

df_reindexed = df.reindex([0, 1, 2, 3], method='pad')

This code snippet performs forward fill:

     A    B
0  0.0  3.0
1  1.0  4.0
2  2.0  5.0
3  2.0  5.0

Combining indices and columns reindexing can lead to complex reshaping of DataFrames. For example:

new_index = [0, 1, 2, 3]
new_columns = ['A', 'B', 'C', 'D']
df_complex_reindexed = df.reindex(index=new_index, columns=new_columns, fill_value=0)

This more complex example specifies both new indices and columns, filling missing entries with zeros:

     A    B  C  D
0  0.0  3.0  0  0
1  1.0  4.0  0  0
2  2.0  5.0  0  0
3  NaN  NaN  0  0

Handling Data Types with Reindexing

When working with reindexing, it is also important to consider the data type of the new index. For example, if you are assigning a column with numeric values as an index, ensure that the operations you plan to conduct are compatible with numeric index types.


The DataFrame.reindex() method is a versatile tool in Pandas, allowing for the flexible manipulation and analysis of data. From adding missing indices or columns to strategically filling in values based on different methods, this function can accommodate a wide range of data manipulation needs. By mastering reindex(), you can significantly enhance your data analysis capabilities in Python.