Reductions

Sum and Mean

Sum a 6-element list in a single accumulator loop, then divide by the count to get the mean. The replay shows total growing with each addition before mean is computed in one final step.

By hand

With NumPy

a.sum() and a.mean() are reduction methods that collapse the entire array to a scalar. The snapshot shows the input array's shape and dtype, then the scalar results.

naive.py 
values = [2, 5, 1, 8, 3, 5]
total = 0
for v in values:
    total += v
mean = round(total / len(values), 2)
print('RESULT:', (total, mean))
library.py 
import numpy as np

values = [2, 5, 1, 8, 3, 5]
a = np.array(values)
total = int(a.sum())
mean = round(float(a.mean()), 2)
print('shape:', a.shape)
print('dtype:', a.dtype)
print('sum:', total)
print('mean:', mean)
print('RESULT:', (total, mean))

shape: (6,)
dtype: int64
sum: 24
mean: 4.0
RESULT: (24, 4.0)

Implementation notes

  • A reduction collapses an array along one or more axes to a smaller shape. Both sum() and mean() called without arguments reduce the entire array to a single scalar.
  • a.sum() and a.mean() return NumPy scalars. Using int() and float() converts them to plain Python types for RESULT, avoiding repr differences across NumPy versions (e.g. np.int64(24) vs 24).
  • For the equivalent pure-Python idiom see list-sum-mean in the python-data-basics book, and arithmetic-mean in the statistics chapter.
  • Shape, dtype, and values are shown explicitly here because ndarray.__repr__ output varies with NumPy version and print options.