How to Calculate Group-Wise Statistics in Pandas Using GroupBy?

How to Get Group-Wise Statistics for a Dataframe Using Pandas GroupBy

When working with data, it's often useful to be able to summarize and analyze data based on specific grouping criteria. Pandas, a powerful Python library for data manipulation and analysis, provides a convenient way to do this through its GroupBy functionality.

Quick Answer

To obtain row counts within each group, utilize the .size() method, which returns a Series:

df.groupby(['col1','col2']).size()

To convert this to a DataFrame form, employ:

df.groupby(['col1', 'col2']).size().reset_index(name='counts')

Alternatively, to calculate row counts and other statistics for each group, the following approach can be used:

df.groupby(['col1', 'col2'])[['col3', 'col4']].agg({
    'col3': ['mean', 'count'], 
    'col4': ['median', 'min', 'count']
})

Detailed Example

Suppose we have a dataframe named df with columns col1 to col4. To illustrate, let's calculate the row counts per group:

df.groupby(['col1', 'col2']).size()

The output will display the number of rows in each unique combination of col1 and col2 values.

To add these counts as a column to our DataFrame, we can utilize the .reset_index(name='counts') method:

df.groupby(['col1', 'col2']).size().reset_index(name='counts')

Including Results for Additional Statistics

If we want to calculate multiple statistics on the grouped data, we can use the agg() method. For instance, to calculate the mean and count for col3 and the median, minimum, and count for col4, we would use:

df.groupby(['col1', 'col2']).agg({
    'col3': ['mean', 'count'], 
    'col4': ['median', 'min', 'count']
})

This will return a DataFrame with the requested statistics for each unique combination of col1 and col2 values.

Conclusion

Pandas GroupBy is a powerful tool for analyzing data based on specific criteria. By utilizing the appropriate methods and aggregations, you can efficiently obtain group-wise statistics to gain insights and understand your data more thoroughly.

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