How to Pivot a Dataframe in Pandas?

Pivot a Dataframe in Pandas

To pivot a dataframe in Pandas, you can use the .pivot method. This method takes the specified values as columns and rearranges the dataframe accordingly.

Suppose you have a dataframe with columns Indicator, Country, Year, and Value. To transpose the table so that the values in the Indicator column become the new columns, use the following code:

out = df.pivot(index=['Country', 'Year'], columns='Indicator', values='Value')
print(out)

The resulting output will be:

Indicator     1   2   3   4  5
Country Year
Angola  2005  6  13  10  11  5
        2006  3   2   7   3  6

To convert the pivoted dataframe back to a flat table, you can use .rename_axis to remove the Indicator column and .reset_index to revert Country and Year to normal columns:

print(out.rename_axis(columns=None).reset_index())

The output will be:

  Country  Year  1   2   3   4  5
0  Angola  2005  6  13  10  11  5
1  Angola  2006  3   2   7   3  6

If your data has duplicate combinations of labels (e.g., Country, Year, Indicator), you can use .pivot_table instead. By default, it takes the mean of repeated values:

out = df.pivot_table(
    index=['Country', 'Year'],
    columns='Indicator',
    values='Value')
print(out.rename_axis(columns=None).reset_index())

The output will be:

  Country  Year    1     2     3     4    5
0  Angola  2005  6.0  13.0  10.0  11.0  5.0
1  Angola  2006  3.0   2.0   7.0   3.0  6.0

For more information on reshaping and pivot tables in Pandas, refer to the user guide.

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