Sum and Mean - Data Handling in Pure Python

Accumulate the total and count of a list of numbers in a single loop, then divide to get the mean. This is the building block for every aggregate function in later books.

By hand

Walk the list once: add each value to total and bump count by one. After the loop, divide to produce mean.

naive.py

values = [5, 10, 15, 20, 25, 30, 35, 40]
total = 0
count = 0
for v in values:
    total = total + v
    count = count + 1
mean = total / count
print('RESULT:', round(mean, 10))

The Pythonic way

statistics.mean handles the accumulation internally and raises StatisticsError on an empty list instead of a silent divide-by-zero. The float() call normalises the return type for integer inputs.

library.py

import statistics
values = [5, 10, 15, 20, 25, 30, 35, 40]
mean = statistics.mean(values)
print('RESULT:', round(float(mean), 10))

RESULT: 22.5

Implementation notes

total and count stay as integers throughout the loop; the / operator produces a float at the final division step.
statistics.mean returns int when the mean divides evenly (e.g. mean([1, 2, 3]) → 2) and float otherwise; wrapping with float() normalises the result to always be a float regardless of the data.