How to Convert a Pandas GroupBy Multi-Index Series Output Back into a DataFrame?

Retrieving Rows from a Pandas GroupBy MultiIndex Series Output

Given a DataFrame with a multi-index, you may perform a GroupBy operation to count or aggregate the values. However, the resulting output is a Series with a hierarchical index, making it difficult to view the raw rows. This article addresses how to convert this output back into a DataFrame containing the original rows.

Question:

How can you transform a Pandas GroupBy multi-index Series output, such as:

                  City  Name
Name    City
Alice   Seattle      1     1
Bob     Seattle      2     2
Mallory Portland     2     2
        Seattle      1     1

into another DataFrame that retains all the original rows?

Answer:

The key to converting the Series back into a DataFrame is to handle the hierarchical index. Here are two approaches:

1. Using add_suffix and reset_index

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

This method adds a suffix to the column names and resets the hierarchical index to create a new DataFrame with a flat index.

Output:

      Name      City  City_Count  Name_Count
0    Alice   Seattle           1           1
1      Bob   Seattle           2           2
2  Mallory  Portland           2           2
3  Mallory   Seattle           1           1

2. Using DataFrame and reset_index

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

This method creates a new DataFrame from the grouped sizes and resets the hierarchical index to obtain a flat DataFrame.

Output:

      Name      City  count
0    Alice   Seattle      1
1      Bob   Seattle      2
2  Mallory  Portland      2
3  Mallory   Seattle      1

These approaches allow you to extract the rows from the GroupBy multi-index Series output and reconstruct a DataFrame that contains all the original data.

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