The pivot function in pandas is used to reshape the given data frame based on specific columns. Specified columns act as pivots of the data frame. An important thing to note is that the pivot function does not support data aggregation. Instead, multiple columns will return the data frame, becoming multi-indexed.
The pivot function has the following syntax:
pandas.pivot(data, index=None, columns=None, values=None)
The pivot function has the following parameters:
| Parameters | Description |
|---|---|
data |
The data frame passed. |
index |
The columns that will be used to make the index of the new data frame. By default, it is None. The existing index is used in that case. |
columns |
The columns that will exist in the new data frame. |
values |
The column(s) to use to populate the new frame’s values. If unspecified, all remaining columns will be used. |
The
dataandcolumnsparameters are compulsory.
All other parameters are optional.
The pivot function returns a reshaped data frame according to the provided columns.
An error occurs when there are any index or column combinations with multiple values.
The code snippet below shows how we can use the pivot function in pandas:
import pandas as pdimport numpy as npdf = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two','two'],'bar': ['A', 'B', 'C', 'A', 'B', 'C'],'baz': [1, 2, 3, 4, 5, 6],'zoo': ['x', 'y', 'z', 'q', 'w', 't']})print("Original Dataframe")print(df)print('\n')print('Reshaped')print(df.pivot(index ='foo', columns = 'bar', values = 'baz'))print('\n')
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