You can use the following basic syntax to convert a pandas DataFrame from a long format to a wide format:
df = pd.pivot(df, index='col1', columns='col2', values='col3')
In this scenario, col1 will become the index, col2 will become the columns, and col3 will be used as the values inside the DataFrame.
The following example shows how to use this syntax in practice.
Example: Reshape Pandas DataFrame from Long to Wide
Suppose we have the following pandas DataFrame in a long format:
import pandas as pd #create DataFrame in long format df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'player': [1, 2, 3, 4, 1, 2, 3, 4], 'points': [11, 8, 10, 6, 12, 5, 9, 4]}) #view DataFrame df team player points 0 A 1 11 1 A 2 8 2 A 3 10 3 A 4 6 4 B 1 12 5 B 2 5 6 B 3 9 7 B 4 4
We can use the following syntax to reshape this DataFrame from a long format to a wide format:
#reshape DataFrame from long format to wide format
df = pd.pivot(df, index='team', columns='player', values='points')
#view updated DataFrame
df
player 1 2 3 4
team
A 11 8 10 6
B 12 5 9 4
The DataFrame is now in a wide format.
We used ‘team’ as the index column, ‘player’ as the columns, and ‘points’ as the values inside of the DataFrame.
Note that we could instead use ‘player’ as the index column and ‘team’ as the columns if we’d like:
#reshape DataFrame from long format to wide format
df = pd.pivot(df, index='player', columns='team', values='points')
#view updated DataFrame
df
team A B
player
1 11 12
2 8 5
3 10 9
4 6 4
This DataFrame is also in a wide format.
Note: You can find the complete documentation for the pandas pivot() function here.
Additional Resources
The following tutorials explain how to perform other common operations in Python:
Pandas: How to Reshape DataFrame from Wide to Long
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