Why Every Software Tester Should Learn NumPy in 2026

import numpy as np

# Floating-point surprise
print(0.1 + 0.2 == 0.3)            # False

# Tolerant single-value check
assert np.isclose(0.1 + 0.2, 0.3)  # passes

# Whole array of results from the system under test
expected = np.array([19.98, 24.00, 30.00])
actual   = np.array([19.980001, 23.999998, 30.0])
assert np.allclose(actual, expected, atol=1e-4)

import numpy as np
import pytest
from pricing import line_totals   # system under test

def test_line_totals_match_expected():
    actual = np.array(line_totals(qty=[2, 3], price=[9.99, 8.00]))
    expected = np.array([19.98, 24.00])
    assert np.allclose(actual, expected, atol=0.005), (actual, expected)

import numpy as np

# Boundary and typical quantities
quantities = np.array([0, 1, 2, 10, 100, 999])

# Evenly spaced prices from 1 to 1000 (10 values)
prices = np.linspace(1, 1000, 10)

# All combinations of quantity and price to test
qs, ps = np.meshgrid(quantities, prices)
cases = np.column_stack([qs.ravel(), ps.ravel()])
print(cases.shape, "test cases")

import numpy as np

rng = np.random.default_rng(42)          # fixed seed = repeatable
amounts = rng.uniform(1, 5000, size=1000)   # 1000 random amounts
discounts = rng.integers(0, 51, size=1000)  # 0-50 percent

# Feed these into the system under test and check results
# stay within valid ranges, etc.
print(amounts[:3], discounts[:3])

import numpy as np

# Response times in ms loaded from a performance test (e.g. a CSV column)
rt = np.loadtxt("response_times.csv", delimiter=",", skiprows=1, usecols=1)

print("count :", rt.size)
print("mean  :", round(rt.mean(), 1))
print("median:", round(np.median(rt), 1))
print("p95   :", round(np.percentile(rt, 95), 1))
print("p99   :", round(np.percentile(rt, 99), 1))
print("max   :", round(rt.max(), 1))

import numpy as np

old = np.loadtxt("rt_build_1.csv", delimiter=",", skiprows=1, usecols=1)
new = np.loadtxt("rt_build_2.csv", delimiter=",", skiprows=1, usecols=1)

old_p95 = np.percentile(old, 95)
new_p95 = np.percentile(new, 95)
change = (new_p95 - old_p95) / old_p95 * 100
print(f"p95: {old_p95:.0f} -> {new_p95:.0f} ms ({change:+.1f}%)")
# A clear regression signal to raise with developers.

Day 1: Arrays, indexing, basic maths on arrays
Day 2: np.isclose and np.allclose for comparisons
Day 3: arange, linspace, meshgrid for test data
Day 4: default_rng for reproducible random data
Day 5: mean, median, percentile, std
Day 6: loadtxt / genfromtxt to read results files
Day 7: Write 5 numeric tests on a real feature

import numpy as np

np.isclose(a, b, atol=0.005)          # tolerant single compare
np.allclose(actual, expected)         # tolerant array compare
np.linspace(0, 100, 11)               # 0,10,...,100
np.random.default_rng(7).integers(1, 7, 10)  # dice rolls, repeatable
np.percentile(data, [50, 95, 99])     # key percentiles

- Verified numeric outputs with NumPy tolerant comparisons
  (isclose/allclose), generated reproducible test data, and
  summarised performance results with percentiles