GroupBy Mean - Pandas Step by Step

Split a table of rows into groups by a category column, then compute the mean of a value column within each group. Groupby-mean is one of the most common one-liner operations in data analysis.

By hand

Maintain two dicts: totals accumulates the sum per category, counts accumulates the row count. After the loop, divide to get the mean for each category. Sort the keys before printing so the output is deterministic.

naive.py

cats = ['a', 'b', 'a', 'b', 'a', 'b']
vals = [10, 20, 30, 40, 50, 60]
totals = {}
counts = {}
for cat, val in zip(cats, vals):
    totals[cat] = totals.get(cat, 0) + val
    counts[cat] = counts.get(cat, 0) + 1
means = {cat: totals[cat] / counts[cat] for cat in sorted(totals)}
print('RESULT:', means)

With Pandas

pd.DataFrame loads the list of dicts into a typed table. groupby('cat') partitions the rows, ['val'] selects the column, and .mean() reduces each partition to its mean — all in one chain. The result is a Series indexed by category.

library.py

import pandas as pd
from dalib.display import set_display
set_display()

data = pd.DataFrame({
    'cat': ['a', 'b', 'a', 'b', 'a', 'b'],
    'val': [10, 20, 30, 40, 50, 60],
})
means = data.groupby('cat')['val'].mean()
result = {k: float(means[k]) for k in sorted(means.index)}
print('index:', sorted(means.index.tolist()))
print('values:', [float(means[k]) for k in sorted(means.index)])
print('dtype:', means.dtype)
print('RESULT:', result)

index: ['a', 'b']
values: [30.0, 40.0]
dtype: float64
RESULT: {'a': 30.0, 'b': 40.0}