How to Flatten a Pandas GroupBy MultiIndex DataFrame?

Converting a Pandas GroupBy MultiIndex Output Back to a DataFrame

When performing a groupby operation on a pandas DataFrame with multiple index columns, the resulting object is a DataFrame with a hierarchical index. This can be inconvenient if you want to access the data as individual rows.

Here's a simple example:

df1 = pd.DataFrame({"City": ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"], "Name": ["Alice", "Bob", "Mallory", "Mallory", "Bob", "Mallory"]})

g1 = df1.groupby(["Name", "City"]).count()

The output of g1 is a DataFrame with a hierarchical index:

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

To convert this back to a DataFrame with individual rows, you can use either the add_suffix and reset_index methods:

g1.add_suffix("_Count").reset_index()

This will add a suffix to the index columns and reset the index to create a flat DataFrame:

      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

Or, you can use the size method and reset_index to count the number of rows in each group and create a new DataFrame:

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

This will create a DataFrame with a single index column:

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

Which approach you use will depend on your specific needs.

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