Master pandas re‑aggregations: Compute column operations on grouped results

Rescue pandas series (8) – Re‑calculating column aggregates with themselves

Many data practitioners shy away from pandas; this article aims to rekindle interest by demonstrating how to perform secondary calculations on column aggregates after grouping.

Platform

Windows 10

Python 3.8

pandas >=1.2.4

Data requirement

When the aggregated values of a column need to be used again in calculations, e.g., after grouping by a category, apply arithmetic operations on the aggregated results.

Function sharing

This article does not solve a specific problem but provides a sample function that can be adapted.

def calculate_func(x, args):
    """
    Average value second calculation: use arithmetic selector passed via args,
    apply corresponding method to columns, returning a DataFrame with selected
    operations applied.
    :params x: pd.DataFrame
    :params args: tuple of operation names
    :return: pd.DataFrame with results
    example:
    df = pd.DataFrame([...])
    result = df.groupby(['day'])[['size','total_bill','tip']].apply(func, args=('sub','div','sub',))
    >>> result
               size  total_bill       tip
    day
    Fri  0   1.333333    1.249844  0.820000
         1  -0.666667    0.901369 -0.910000
         2  -0.666667    0.848787  0.090000
    Sat  3  -0.666667    1.034055  1.423333
         4   0.333333    0.983583  0.423333
         5   0.333333    0.982362 -1.846667
    Sun  6   2.333333    1.077870  1.166667
         7  -0.666667    1.014768 -0.333333
         8  -1.666667    0.907362 -0.833333
    Thur 9  -0.333333    1.085621 -0.056667
         10  0.666667    1.037270 -0.056667
         11 -0.333333    0.877109  0.113333
    """
    mean = x.mean()
    index = mean.index
    calculate_map = {
        'add': x.add(mean),
        'sub': x.sub(mean),
        'mul': x.mul(mean),
        'div': x.div(mean)
    }
    return pd.concat([calculate_map.get(i)[index[num]] for num, i in enumerate(args)], axis=1)

The function uses the aggregation result mean and its index to select columns based on the operations supplied in args. It builds a map of arithmetic methods, extracts the corresponding series, and concatenates them horizontally into a pd.DataFrame.

Summary

This short function demonstrates how to perform secondary arithmetic on grouped pandas data, illustrating the thought process behind creating reusable utilities for common data‑analysis tasks.