Hypothesis¶
Hypothesis is a library that allows you to write tests that are parameterised from a source of examples. It then generates simple and understandable examples that can be used to make your tests fail and find bugs with little effort.
Install Hypothesis:
$ python -m pip install hypothesis
C:> python -m pip install hypothesis
Alternatively, Hypothesis can also be installed with extensions, for example:
$ python -m pip install hypothesis[numpy,pandas]
C:> python -m pip install hypothesis[numpy,pandas]
Write a test:
Imports:
1import pytest 2from hypothesis import given 3from hypothesis.strategies import floats, lists
Test:
6@given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1)) 7def test_mean(ls): 8 mean = sum(ls) / len(ls) 9 assert min(ls) <= mean <= max(ls)
Perform test:
$ python -m pytest test_hypothesis.py ============================= test session starts ============================== platform darwin -- Python 3.13.0, pytest-8.3.3, pluggy-1.5.0 rootdir: /Users/veit/cusy/trn/python-basics/docs/test plugins: hypothesis-6.114.1 collected 1 item test_hypothesis.py F [100%] =================================== FAILURES =================================== __________________________________ test_mean ___________________________________ @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1)) > def test_mean(ls): test_hypothesis.py:6: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ls = [9.9792015476736e+291, 1.7976931348623157e+308] @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1)) def test_mean(ls): mean = sum(ls) / len(ls) > assert min(ls) <= mean <= max(ls) E assert inf <= 1.7976931348623157e+308 E + where 1.7976931348623157e+308 = max([9.9792015476736e+291, 1.7976931348623157e+308]) test_hypothesis.py:8: AssertionError ---------------------------------- Hypothesis ---------------------------------- Falsifying example: test_mean( ls=[9.9792015476736e+291, 1.7976931348623157e+308], ) =========================== short test summary info ============================ FAILED test_hypothesis.py::test_mean - assert inf <= 1.7976931348623157e+308 ============================== 1 failed in 0.44s ===============================
C:> python -m pytest test_hypothesis.py ============================= test session starts ============================== platform win32 -- Python 3.13.0, pytest-8.3.3, pluggy-1.5.0 rootdir: C:\Users\veit\python-basics\docs\test plugins: plugins: hypothesis-6.114.1 collected 1 item test_hypothesis.py F [100%] =================================== FAILURES =================================== __________________________________ test_mean ___________________________________ @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1)) > def test_mean(ls): test_hypothesis.py:6: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ls = [9.9792015476736e+291, 1.7976931348623157e+308] @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1)) def test_mean(ls): mean = sum(ls) / len(ls) > assert min(ls) <= mean <= max(ls) E assert inf <= 1.7976931348623157e+308 E + where 1.7976931348623157e+308 = max([9.9792015476736e+291, 1.7976931348623157e+308]) test_hypothesis.py:8: AssertionError ---------------------------------- Hypothesis ---------------------------------- Falsifying example: test_mean( ls=[9.9792015476736e+291, 1.7976931348623157e+308], ) =========================== short test summary info ============================ FAILED test_hypothesis.py::test_mean - assert inf <= 1.7976931348623157e+308 ============================== 1 failed in 0.44s ===============================
See also