Series Basics

Series Aggregate

Reduce a list of values to a single sum and mean with a manual accumulator loop. The pandas version calls .sum() and .mean() directly on a Series, showing how aggregations collapse the entire index-and-values structure to a scalar.

By hand

With pandas

s.sum() and s.mean() reduce the Series to scalars. int() and float() convert the numpy scalars to plain Python types for a stable RESULT.

naive.py 
values = [4, 7, 2, 9, 3]
total = 0
for v in values:
    total += v
mean = round(total / len(values), 2)
print('RESULT:', (total, mean))
library.py 
import pandas as pd
from dalib.display import set_display
set_display()

values = [4, 7, 2, 9, 3]
s = pd.Series(values)
total = int(s.sum())
mean = round(float(s.mean()), 2)
print('index:', s.index.tolist())
print('values:', s.tolist())
print('dtype:', s.dtype)
print('sum:', total)
print('mean:', mean)
print('RESULT:', (total, mean))

index: [0, 1, 2, 3, 4]
values: [4, 7, 2, 9, 3]
dtype: int64
sum: 25
mean: 5.0
RESULT: (25, 5.0)

Implementation notes

  • s.sum() and s.mean() return numpy scalars (np.int64, np.float64). Wrapping with int() / float() converts them to Python natives, avoiding repr differences across numpy versions (see the NumPy sum-and-mean lesson).
  • Aggregation methods ignore the index and operate only on the values array. Any index — default RangeIndex or string labels — produces the same sum and mean.
  • Other common aggregations on a Series: .min(), .max(), .std(), .median(), .count(). All reduce to a scalar and share the same numpy-scalar return type pattern.
  • Cross-reference: sum-and-mean (python-numpy ch05) for the NumPy array version; list-sum-mean (python-data-basics) for the plain Python version.