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.
Additional Resources
The following tutorials explain how to perform other common operations in Python:
How to Add Rows to a Pandas DataFrame
How to Add Columns to a Pandas DataFrame
How to Count Occurrences of Specific Values in Pandas DataFrame