How Can Pandas\' `melt` Function Reshape a DataFrame with Additional Group and Name Columns?

Pandas Melt Function: A Reshaping Tool

Problem

Consider a DataFrame df and a dictionary d. You aim to reshape df into a table with additional columns, namely Group and Name. The desired output should resemble:

    Group   Name  Year  Value
 0      A    Amy  2013      2
 1      A    Amy  2014      9
 2      B    Bob  2013      4
 3      B    Bob  2014      2
 4      B    Ben  2013      1
 5      B    Ben  2014      5
 6      C   Carl  2013      7
 7      C   Carl  2014      4
 8      C  Chris  2013      8
 9      C  Chris  2014      5
10  Other         2013      3
11  Other         2014      6

Solution

To achieve this reshape, we will utilize the Pandas melt function.

m = pd.melt(df, id_vars=['Year'], var_name='Name')

This will create a melted DataFrame m with the columns Year, Name, and value. To add the Group column, we reshape d as follows:

d2 = {}
for k, v in d.items():
  for item in v:
    d2[item] = k

We then map d2 to m['Name'] to populate the Group column.

m['Group'] = m['Name'].map(d2)

Finally, we move the 'Other' values from Name to Group:

mask = m['Name'] == 'Other'
m.loc[mask, 'Name'] = ''
m.loc[mask, 'Group'] = 'Other'

The resulting DataFrame m will match the desired output.

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