How Can I Pivot a Pandas Dataframe to Reshape Data by Specific Columns?

Transposing Pandas Dataframes for Data Pivoting

In data analysis, transposing a dataframe is crucial for organizing data into a more suitable format. One common use case is pivoting a dataframe based on specific column values.

For a CSV table containing data as follows:

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

you can pivot the dataframe to obtain this format:

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

To achieve this transformation, you can utilize the .pivot method as follows:

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

For data with duplicate label combinations, you can employ the .pivot_table method, which applies the mean calculation by default:

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

By utilizing the .rename_axis and .reset_index methods, you can restore the dataframe to a flat table format.

Refer to the Pandas user guide for in-depth documentation on reshaping and pivot tables.

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