How Can Pandas\' Melt Function Reshape Wide Dataframes into a Desired Long Format with Additional Columns?

Pandas Melt Function for Data Transformation

When faced with complex data restructuring tasks in Pandas, the melt function emerges as a powerful tool. It seamlessly transforms dataframes from wide formats with multiple columns into long formats with fewer columns.

In a practical scenario, consider a dataframe that requires reshaping:

df = pd.DataFrame([[2, 4, 7, 8, 1, 3, 2013], [9, 2, 4, 5, 5, 6, 2014]],
               columns=['Amy', 'Bob', 'Carl', 'Chris', 'Ben', 'Other', 'Year'])

   Amy  Bob  Carl  Chris  Ben  Other  Year
0    2    4     7      8    1      3  2013
1    9    2     4      5    5      6  2014

To transform this dataframe into a more structured format, you can employ the melt function:

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

This operation generates the following dataframe:

    Year   Name  value
0   2013    Amy      2
1   2014    Amy      9
2   2013    Bob      4
3   2014    Bob      2
4   2013   Carl      7
...

However, the desired output includes an additional column named "Group." To obtain this, reshape the dictionary:

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

The dataframe can now be updated with the "Group" column:

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

Finally, move 'Other' from the 'Name' column to the 'Group' column:

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

The resulting dataframe aligns perfectly with the desired output:

    Year   Name  value  Group
0   2013    Amy      2      A
1   2014    Amy      9      A
2   2013    Bob      4      B
3   2014    Bob      2      B
4   2013   Carl      7      C
...

In this manner, the Pandas melt function empowers data scientists with a versatile and efficient mechanism for reshaping dataframes, enabling them to effortlessly transform and reorganize data for various analytical and visualization purposes.

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