Function approx

src/_pytest/python_api.py:518–721

Assert that two numbers (or two sets of numbers) are equal to each other within some tolerance. Due to the :std:doc:`tutorial/floatingpoint`, numbers that we would intuitively expect to be equal are not always so:: >>> 0.1 + 0.2 == 0.3 False This problem is commonl

(expected, rel=None, abs=None, nan_ok: bool = False)

def approx(expected, rel=None, abs=None, nan_ok: bool = False) -> ApproxBase:
    """Assert that two numbers (or two sets of numbers) are equal to each other
    within some tolerance.

    Due to the :std:doc:`tutorial/floatingpoint`, numbers that we
    would intuitively expect to be equal are not always so::

        >>> 0.1 + 0.2 == 0.3
        False

    This problem is commonly encountered when writing tests, e.g. when making
    sure that floating-point values are what you expect them to be.  One way to
    deal with this problem is to assert that two floating-point numbers are
    equal to within some appropriate tolerance::

        >>> abs((0.1 + 0.2) - 0.3) < 1e-6
        True

    However, comparisons like this are tedious to write and difficult to
    understand.  Furthermore, absolute comparisons like the one above are
    usually discouraged because there&#x27;s no tolerance that works well for all
    situations.  ``1e-6`` is good for numbers around ``1``, but too small for
    very big numbers and too big for very small ones.  It&#x27;s better to express
    the tolerance as a fraction of the expected value, but relative comparisons
    like that are even more difficult to write correctly and concisely.

    The ``approx`` class performs floating-point comparisons using a syntax
    that&#x27;s as intuitive as possible::

        >>> from pytest import approx
        >>> 0.1 + 0.2 == approx(0.3)
        True

    The same syntax also works for sequences of numbers::

        >>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
        True

    Dictionary *values*::

        >>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6})
        True

    ``numpy`` arrays::

        >>> import numpy as np                                                          # doctest: +SKIP
        >>> np.array([0.1, 0.2]) + np.array([0.2, 0.4]) == approx(np.array([0.3, 0.6])) # doctest: +SKIP
        True

    And for a ``numpy`` array against a scalar::

        >>> import numpy as np                                         # doctest: +SKIP
        >>> np.array([0.1, 0.2]) + np.array([0.2, 0.1]) == approx(0.3) # doctest: +SKIP
        True

    By default, ``approx`` considers numbers within a relative tolerance of
    ``1e-6`` (i.e. one part in a million) of its expected value to be equal.
    This treatment would lead to surprising results if the expected value was