Mastering pandas - Second Edition

Applying multiple functions

For any DataFrame, a list of aggregations can be performed after applying groupby. In the following example, the mean and standard deviation have been computed for Sales and Quantity:

sales_data[["Sales", "Quantity", "Category"]].groupby("Category").agg([np.mean, np.std])

The following will be the output:

Multiple aggregations

Note that hierarchy has also been introduced in the column index. agg is a short form of aggregate. These aggregations will exclude any NAs found for computation.

In the preceding example, columns were created with the mean and std labels. Let's try renaming them. The rename argument maps ...