How can I transpose a DataFrame to pivot its rows and columns in Pandas?

Transposing DataFrames for Pivoting

Pivoting a DataFrame refers to transposing its rows and columns to achieve a different representation of the data. In Pandas, the pivot method can be employed for this purpose.

Consider the following DataFrame:

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

To pivot this DataFrame to have indicator values as new columns:

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

This results in:

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

To revert to an unpivoted format:

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

This produces:

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

Using .pivot_table

If duplicate label combinations exist, pivot_table can be utilized. It calculates the mean by default:

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

Resulting in:

  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 further information, refer to the Pandas user guide on reshaping and pivot tables.

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