When working with floating-point numbers in tests, comparing exact equality often fails due to inherent precision limitations, which results in flaky or unreliable tests.

You can use pytest.approx() to compare floating-point numbers with a reasonable tolerance, making your tests more robust and readable.

Here’s a code example to show its usefulness:

# Without pytest.approx - test fails
def test_calculation_without_approx():
    result = 0.1 + 0.2
    assert result == 0.3  # This fails!

# With pytest.approx - test passes
def test_calculation_with_approx():
    result = 0.1 + 0.2
    assert result == pytest.approx(0.3)  # This passes!

# Works with sequences too
def test_list_of_floats():
    results = [0.1 + 0.2, 0.2 + 0.4]
    expected = [0.3, 0.6]
    assert results == pytest.approx(expected)

In these examples:

• The first test fails because 0.1 + 0.2 in floating-point arithmetic doesn’t exactly equal 0.3.
• The second test passes because pytest.approx() allows for a small tolerance in the comparison (by default, relative tolerance of 1e-6).
• The third example shows how it works with lists of numbers, making it convenient for comparing multiple results at once.