The property-based testing library for Python

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  • License Expression: MPL-2.0
    SPDX License Expression
  • Author: David R. MacIver and Zac Hatfield-Dodds
  • Tags python , testing , fuzzing , property-based-testing
  • Requires: Python >=3.10
  • Provides-Extra: cli , codemods , ghostwriter , pytz , dateutil , lark , numpy , pandas , pytest , dpcontracts , redis , crosshair , zoneinfo , django , watchdog , all
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Project description

Hypothesis

Hypothesis is the property-based testing library for Python. With Hypothesis, you write tests which should pass for all inputs in whatever range you describe, and let Hypothesis randomly choose which of those inputs to check - including edge cases you might not have thought about. For example:

from hypothesis import given, strategies as st


@given(st.lists(st.integers()))
def test_matches_builtin(ls):
    assert sorted(ls) == my_sort(ls)

This randomized testing can catch bugs and edge cases that you didn't think of and wouldn't have found. In addition, when Hypothesis does find a bug, it doesn't just report any failing example — it reports the simplest possible one. This makes property-based tests a powerful tool for debugging, as well as testing.

For instance,

def my_sort(ls):
    return sorted(set(ls))

fails with the simplest possible failing example:

Falsifying example: test_matches_builtin(ls=[0, 0])

Installation

To install Hypothesis:

pip install hypothesis

There are also optional extras available.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for DRMacIver from gravatar.com DRMacIver Avatar for Zac-HD from gravatar.com Zac-HD

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License Expression: MPL-2.0
    SPDX License Expression
  • Author: David R. MacIver and Zac Hatfield-Dodds
  • Tags python , testing , fuzzing , property-based-testing
  • Requires: Python >=3.10
  • Provides-Extra: cli , codemods , ghostwriter , pytz , dateutil , lark , numpy , pandas , pytest , dpcontracts , redis , crosshair , zoneinfo , django , watchdog , all
Classifiers

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4.4.5

4.4.4

4.4.3

4.4.2

4.4.1

4.4.0

4.3.0

4.2.0

4.1.2

4.1.1

4.1.0

4.0.2

4.0.1

4.0.0

3.88.3

3.88.2

3.88.1

3.88.0

3.87.0

3.86.9

3.86.8

3.86.7

3.86.6

3.86.5

3.86.4

3.86.3

3.86.2

3.86.1

3.86.0

3.85.3

3.85.2

3.85.1

3.85.0

3.84.6

3.84.5

3.84.4

3.84.3

3.84.2

3.84.1

3.84.0

3.83.2

3.83.1

3.83.0

3.82.6

3.82.5

3.82.4

3.82.3

3.82.2

3.82.1

3.82.0

3.81.0

3.80.0

3.79.4

3.79.3

3.79.2

3.79.1

3.79.0

3.78.0

3.77.0

3.76.1

3.76.0

3.75.4

3.75.3

3.75.2

3.75.1

3.75.0

3.74.3

3.74.2

3.74.1

3.74.0

3.73.5

3.73.4

3.73.3

3.73.2

3.73.1

3.73.0

3.72.0

3.71.11

3.71.10

3.71.9

3.71.8

3.71.7

3.71.6

3.71.5

3.71.4

3.71.3

3.71.2

3.71.1

3.71.0

3.70.4

3.70.3

3.70.2

3.70.1

3.70.0

3.69.12

3.69.11

3.69.10

3.69.9

3.69.8

3.69.7

3.69.6

3.69.5

3.69.4

3.69.3

3.69.2

3.69.1

3.69.0

3.68.3

3.68.2

3.68.1

3.68.0

3.67.1

3.67.0

3.66.33

3.66.32

3.66.31

3.66.30

3.66.29

3.66.28

3.66.27

3.66.26

3.66.25

3.66.24

3.66.23

3.66.22

3.66.21

3.66.20

3.66.19

3.66.18

3.66.17

3.66.16

3.66.15

3.66.14

3.66.13

3.66.12

3.66.11

3.66.10

3.66.9

3.66.8

3.66.7

3.66.6

3.66.5

3.66.4

3.66.3

3.66.2

3.66.1

3.66.0

3.65.3

3.65.2

3.65.1

3.65.0

3.64.2

3.64.1

3.64.0

3.63.0

3.62.0

3.61.0

3.60.1

3.60.0

3.59.3

3.59.2

3.59.1

3.59.0

3.58.1

3.58.0

3.57.0

3.56.10

3.56.9

3.56.8

3.56.7

3.56.6

3.56.5

3.56.4

3.56.3

3.56.2

3.56.1

3.56.0

3.55.6

3.55.5

3.55.4

3.55.3

3.55.2

3.55.1

3.55.0

3.54.0

3.53.0

3.52.3

3.52.2

3.52.1

3.52.0

3.51.0

3.50.3

3.50.2

3.50.1

3.50.0

3.49.1

3.49.0

3.48.1

3.48.0

3.47.0

3.46.2

3.46.1

3.46.0

3.45.5

3.45.4

3.45.3

3.45.2

3.45.1

3.45.0

3.44.26

3.44.25

3.44.24

3.44.23

3.44.22

3.44.21

3.44.20

3.44.19

3.44.18

3.44.16

3.44.15

3.44.14

3.44.13

3.44.12

3.44.11

3.44.10

3.44.9

3.44.8

3.44.7

3.44.6

3.44.5

3.44.4

3.44.3

3.44.2

3.44.1

3.44.0

3.43.1

3.43.0

3.42.2

3.42.1

3.42.0

3.41.0

3.40.1

3.40.0

3.39.0

3.38.9

3.38.8

3.38.7

3.38.6

3.38.5

3.38.4

3.38.3

3.38.2

3.38.1

3.38.0

3.37.0

3.36.1

3.36.0

3.35.0

3.34.1

3.34.0

3.33.1

3.33.0

3.32.2

3.32.1

3.32.0

3.31.6

3.31.5

3.31.4

3.31.3

3.31.2

3.31.1

3.31.0

3.30.4

3.30.3

3.30.2

3.30.1

3.30.0

3.29.0

3.28.3

3.28.2

3.28.1

3.28.0

3.27.1

3.27.0

3.26.0

3.25.1

3.25.0

3.24.2

3.24.1

3.24.0

3.23.3

3.23.2

3.23.1

3.23.0

3.22.2

3.22.1

3.22.0

3.21.3

3.21.2

3.21.1

3.21.0

3.20.0

3.19.3

3.19.2

3.19.1

3.19.0

3.18.5

3.18.4

3.18.3

3.18.2

3.18.1

3.18.0

3.17.0

3.16.1

3.16.0

3.15.0

3.14.2

3.14.1

3.14.0

3.13.1

3.13.0

3.12.0

3.11.6

3.11.5

3.11.4

3.11.3

3.11.2

3.11.1

3.11.0

3.10.0

3.9.1

3.9.0

3.8.5

3.8.4

3.8.3

3.8.2

3.8.1

3.8.0

3.7.4

3.7.3

3.7.2

3.7.1

3.7.0

3.6.1

3.6.0

3.5.3

3.5.2

3.5.1

3.5.0

3.4.2

3.4.1

3.4.0

3.3.0

3.2.0

3.1.3

3.1.2

3.1.1

3.1.0

3.0.5

3.0.4

3.0.3

3.0.2

3.0.1

3.0.0

2.0.0

1.19.0

1.18.1

1.18.0

1.17.1

1.17.0

1.16.0

1.15.0

1.14.0

1.13.0

1.12.0

1.11.4

1.11.3

1.11.2

1.11.1

1.11.0

1.10.6

1.10.5

1.10.4

1.10.3

1.10.2

1.10.1

1.10.0

1.9.0

1.8.5

1.8.4

1.8.3

1.8.2

1.8.1

1.8.0

1.7.2

1.7.1

1.6.2

1.6.1

1.6.0

1.5.0

1.4.1

1.4.0

1.3.0

1.2.1

1.2

1.1.1

1.1.0

1.0.0

0.9.2

0.9.1

0.9.0

0.7.2

0.7.1

0.7.0

0.6.0

0.5.0

0.4.3

0.4.2

0.4.1

0.4.0

0.3.2

0.3.1

0.3.0

0.2.2

0.2.1

0.2.0

0.1.4

0.1.3

0.1.2

0.1.1

0.1.0

0.0.5

0.0.4

0.0.3

0.0.2

0.0.1

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