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Updated on 2025-04-16

How to remove multi-level indexes after using pivot tables in Pandas

introduction

Pandas is a powerful and universal Python library for data manipulation and analysis. One of its most useful features is the Pivot Table, which allows you to reshape and aggregate data. However, using pivot tables often results in multi-level (hierarchical) indexing, which can be cumbersome. In this article, we will explore how to remove multi-level indexes after using pivot tables in Pandas to make your data easier to process and analyze.

Pivot Tables in Pandas Pivot Tables

PivotTables are a powerful data analysis tool that allows you to transform and summarize data in a way that is easier to understand and analyze. In Pandas, the pivot_table function is used to create a pivot table. It provides a flexible way to group, aggregate and reshape data.

Create a Pivot Table

Use the pivot_table function to create a pivot table. The basic syntax is as follows:

pivot_table(data, values, index, columns, aggfunc='mean', fill_value=None)
  • data: Original DataFrame.
  • values: The column names or list of column names to aggregate.
  • index: Column name or column name list as row index of the new DataFrame.
  • columns: Column name or column name list as column index of the new DataFrame.
  • aggfunc: The aggregation function, defaults to 'mean', can also be a list, and different aggregation functions are used for different columns.
  • fill_value: The value used to fill in missing values.

Example

Suppose we have a DataFramedf, including columns'A', 'B', 'C', and'D', we want to use the column'A'and'B'To summarize the columns'C'Mean value:

import pandas as pd

# Sample datadata = {
    'A': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'],
    'B': ['one', 'one', 'two', 'two', 'one', 'one'],
    'C': [1, 2, 3, 4, 5, 6],
    'D': [7, 8, 9, 10, 11, 12]
}

df = (data)

# Create a Pivot Tablepivot_table = df.pivot_table(values='C', index=['A', 'B'], aggfunc='mean')

Pivot Tables are a very useful tool in data analysis, which can help you quickly view and analyze data from different angles.

Several ways to remove multi-level indexes

If you want to remove these multi-level indexes, there are several ways to do this:

Reset Index：
usereset_indexMethods can quickly remove the multi-level index of DataFrame and convert it into normal columns. If you want to remove only part of the index, you can specifylevelparameter.

df_pivot = df.pivot_table(values='value', index='index1', columns='index2')
df_reset = df_pivot.reset_index()

Selective reset of index：
If you just want to reset the index at certain levels, you can set itlevelParameters, reset only specific index levels.

df_reset = df_pivot.reset_index(level='index1')

Convert to a single index：
If you want to keep the index but convert it to a single index, you can set itdropThe parameters areFalse。

df_reset = df_pivot.reset_index(drop=False)

usestackandunstack：
If your pivot table has multiple index levels and you want to convert them to columns, you can usestackmethod. Then, if needed, useunstackThe method converts the data back to the DataFrame, but this time there is only one index level.

df_stacked = df_pivot.stack()
df_unstacked = df_stacked.unstack()

Selectively delete columns：
If you just want to delete certain index columns, you can use the column deletion method directly.

df_reset = df_pivot.drop(columns=['index1', 'index2'])

usemeltmethod：
meltMethods can convert a wide format DataFrame back to a long format and can specify which columns are indexed and which columns are values.

df_melted = df_pivot.melt(id_vars=['index1'], value_vars=['index2'], var_name='index2', value_name='value')

Which method to choose depends on your specific needs and data structure. generally,reset_indexIt is the easiest and straightforward way, but if you need to keep index information, you may need to consider other methods.

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