How to Unpivot a Pandas DataFrame (With Example)

In pandas, you can use the melt() function to unpivot a DataFrame – converting it from a wide format to a long format.

This function uses the following basic syntax:

df_unpivot = pd.melt(df, id_vars='col1', value_vars=['col2', 'col3', ...])

where:

id_vars: The columns to use as identifiers
value_vars: The columns to unpivot

The following example shows how to use this syntax in practice.

Example: Unpivot a Pandas DataFrame

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E'],
                   'points': [18, 22, 19, 14, 14],
                   'assists': [5, 7, 7, 9, 12],
                   'rebounds': [11, 8, 10, 6, 6]})

#view DataFrame
print(df)

  team  points  assists  rebounds
0    A      18        5        11
1    B      22        7         8
2    C      19        7        10
3    D      14        9         6
4    E      14       12         6

We can use the following syntax to “unpivot” the DataFrame:

#unpivot DataFrame from wide format to long format
df_unpivot = pd.melt(df, id_vars='team', value_vars=['points', 'assists', 'rebounds'])

#view updated DataFrame
print(df_unpivot)

   team  variable  value
0     A    points     18
1     B    points     22
2     C    points     19
3     D    points     14
4     E    points     14
5     A   assists      5
6     B   assists      7
7     C   assists      7
8     D   assists      9
9     E   assists     12
10    A  rebounds     11
11    B  rebounds      8
12    C  rebounds     10
13    D  rebounds      6
14    E  rebounds      6

We used the team column as the identifier column and we chose to unpivot the points, assists, and rebounds columns.

The result is a DataFrame in a long format.

Note that we can also use the var_name and value_name arguments to specify the names of the columns in the unpivoted DataFrame:

#unpivot DataFrame from wide format to long format 
df_unpivot = pd.melt(df, id_vars='team', value_vars=['points', 'assists', 'rebounds'],
             var_name='metric', value_name='amount')

#view updated DataFrame
print(df_unpivot)

   team    metric  amount
0     A    points      18
1     B    points      22
2     C    points      19
3     D    points      14
4     E    points      14
5     A   assists       5
6     B   assists       7
7     C   assists       7
8     D   assists       9
9     E   assists      12
10    A  rebounds      11
11    B  rebounds       8
12    C  rebounds      10
13    D  rebounds       6
14    E  rebounds       6

Notice that the new columns are now labeled metric and amount.