How to Convert a Pandas MultiIndex DataFrame Back to its Original Single-Index Form?

Converting a Pandas MultiIndex DataFrame Back to Original Form

When working with Pandas DataFrames, it's common to perform grouping operations to aggregate data. However, after grouping, the resulting DataFrame may have a multi-index hierarchy, which can be challenging to work with. This article discusses a method to convert a multi-index DataFrame back to its original form, with a simple demonstration using a sample DataFrame.

The Problem

The given sample DataFrame contains several rows of data with columns for "City" and "Name". We perform a GroupBy operation on the DataFrame, aggregating by "Name" and "City" using the count() function. The resulting grouped DataFrame has a multi-index of ("Name", "City").

The Solution

To convert the multi-index DataFrame back to its original form, we can use the add_suffix() and reset_index() functions. The add_suffix() function adds a suffix to the column names, and the reset_index() function converts the multi-index to a single-index DataFrame.

g1.add_suffix('_Count').reset_index()

The resulting DataFrame will contain the original rows with additional columns "_Count" to represent the counts for each combination of "Name" and "City".

Alternative Method

Another approach to convert the multi-index DataFrame is to create a new DataFrame using the DataFrame() function and the size() function to count the rows for each combination of "Name" and "City".

DataFrame({'count' : df1.groupby( [ "Name", "City"] ).size()}).reset_index()

This method doesn't require the use of the add_suffix() function, but it results in a DataFrame with a single "count" column instead of separate count columns for each level of the multi-index.

By utilizing these methods, it's easy to convert a multi-index DataFrame back to its original form, facilitating further data manipulation and analysis tasks.

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