Reductions

Min, Max, and Argmax

Find the minimum, maximum, and the index of the maximum in a single pass over a 6-element list. Two if-checks per iteration update the running extremes. The replay shows min_val, max_val, and max_idx updating only when a new extreme is found.

By hand

With NumPy

a.min() and a.max() return the extreme values; a.argmax() returns the integer index of the first occurrence of the maximum. The snapshot shows the input array followed by all three results on one line.

naive.py 
values = [3, 7, 2, 9, 1, 5]
min_val = values[0]
max_val = values[0]
max_idx = 0
for i in range(len(values)):
    if values[i] < min_val:
        min_val = values[i]
    if values[i] > max_val:
        max_val = values[i]
        max_idx = i
print('RESULT:', (min_val, max_val, max_idx))
library.py 
import numpy as np

values = [3, 7, 2, 9, 1, 5]
a = np.array(values)
mn = int(a.min())
mx = int(a.max())
idx = int(a.argmax())
print('shape:', a.shape)
print('dtype:', a.dtype)
print('values:', a.tolist())
print('min:', mn, 'max:', mx, 'argmax:', idx)
print('RESULT:', (mn, mx, idx))

shape: (6,)
dtype: int64
values: [3, 7, 2, 9, 1, 5]
min: 1 max: 9 argmax: 3
RESULT: (1, 9, 3)

Implementation notes

  • argmax returns the index of the first maximum. If the maximum value appears more than once, argmax returns the lowest index — the same behaviour as the hand-written loop here, which only updates max_idx on a strict > comparison.
  • a.argmin() works the same way for the minimum.
  • All three — min, max, argmax — are reductions: they collapse the array to a scalar (or an index). Called without an axis argument they operate over the entire array.
  • int() converts the NumPy scalar return values to plain Python ints for RESULT, avoiding repr differences across NumPy versions.
  • Shape, dtype, and values are shown explicitly here because ndarray.__repr__ output varies with NumPy version and print options.