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Pandas reset_index(): How To Reset Indexes in Pandas

By Federico
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In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.

This article provides an in-depth exploration of the Pandas reset_index() method, explaining its importance, usage, and the scenarios where it’s useful.

What is Pandas reset_index() and when to use it?

Pandas reset_index() visualized as real pandas playing in the threes by Federico Trotta
Pandas playing in the threes. Image by Federico Trotta.

In Pandas, each DataFrame and Series has an index, which is a set of labels used for identifying each row or item uniquely.

The reset_index() method is used to reset the index of the DataFrame or Series, which can involve turning the index into a regular column, or discarding it entirely. This is particularly useful when the index needs reorganizing, or when integrating the index into DataFrame columns for further analysis.

The reset_index() is typically used in the following scenarios:

  • Reverting an index after group operations. Post-grouping operations might leave you with grouped or multi-level indexes which are sometimes inconvenient for further analysis.
  • Integrating the index as a feature. If the index itself carries valuable data (e.g., time stamps or unique identifiers), you might want to move it into a DataFrame column to use as a feature in data analysis or machine learning models.
  • Resetting after sorting or filtering. Sorting or filtering can alter the order or number of rows, and resetting the index can be necessary to maintain a contiguous, integer index.

How to use Pandas reset_index()

The basic syntax of reset_index() is as follows:

DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')

Each parameter has a specific function:

  • level. It Specifies which index levels to reset (for MultiIndex).
  • drop. If True, the old index is discarded and not added as a column in the new DataFrame.
  • inplace. If True, modifies the DataFrame in-place; otherwise, a new DataFrame is returned.
  • col_level, col_fill. Is used when the columns are a MultiIndex.

Usage examples

Basic reset:

import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
    'Data': [10, 20, 30, 40],
}, index=['a', 'b', 'c', 'd'])
print("Original DataFrame:")
# Reset the index
reset_df = df.reset_index()
print("\nDataFrame after reset_index():")

That results is:

Original DataFrame:
a    10
b    20
c    30
d    40
DataFrame after reset_index():
  index  Data
0     a    10
1     b    20
2     c    30
3     d    40

Dropping an index

If the index is irrelevant and not needed as a column, set the parameter drop=True:

reset_df_drop = df.reset_index(drop=True)

That results is:

0    10
1    20
2    30
3    40

Multi-index reset

# Create a MultiIndex DataFrame
mindex = pd.MultiIndex.from_tuples([(1, 'a'), (1, 'b'), (2, 'a'), (2, 'b')], names=['first', 'second'])
df_multi = pd.DataFrame({'Data': [100, 200, 300, 400]}, index=mindex)
print("Original MultiIndex DataFrame:")
# Reset the 'second' level of the index
reset_multi_df = df_multi.reset_index(level='second')
print("\nDataFrame after resetting 'second' level:")

That results in:

Original MultiIndex DataFrame:
first second      
1     a        100
      b        200
2     a        300
      b        400
DataFrame after resetting 'second' level:
      second  Data
1          a   100
1          b   200
2          a   300
2          b   400


Pandas reset_index() is a versatile tool in the Pandas library that provides essential functionality for DataFrame and Series index manipulation. Whether you’re preparing data for analysis, integrating index data as a feature, or simply organizing data post-transformation, understanding how it works will speed your processes up.

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Federico Trotta

Technical Writer: I document digital products and write articles about AI & Python programming.

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