How to Convert a Pandas GroupBy MultiIndex Output from Series to DataFrame?

Converting a Pandas GroupBy MultiIndex Output from Series to DataFrame

Consider the following DataFrame:

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

After performing a grouping operation using groupby(), the output is a hierarchical index DataFrame, as shown below:

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

print(g1)
                   City  Name
Name    City
Alice   Seattle      1     1
Bob     Seattle      2     2
Mallory Portland     2     2
        Seattle      1     1

To retrieve the desired DataFrame structure, there are several approaches.

Adding a Suffix and Resetting the Index

This involves adding a suffix to the column names and resetting the hierarchical index:

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

print(
    pd.DataFrame({'City_Count': g1['City'],
                  'Name_Count': g1['Name']
                  })
)
          City_Count  Name_Count
Alice   Seattle      1           1
       Portland     2           2
Bob     Seattle      2           2
Mallory Portland     2           2
        Seattle      1           1

Using the size() Method

An alternative approach is to use the size() method to count the occurrences for each group and reset the index:

df1.groupby(['Name', 'City']).size().reset_index()

print(
    pd.DataFrame({'Name': g1.index.get_level_values(0),
                  'City': g1.index.get_level_values(1),
                  'Count': g1['City']
                  })
)
      Name      City  Count
0     Alice   Seattle      1
1       Bob   Seattle      2
2   Mallory  Portland      2
3   Mallory   Seattle      1

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