A library for property-based testing

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Project description

Hypothesis is an advanced testing library for Python. It lets you write tests which are parametrized by a source of examples, and then generates simple and comprehensible examples that make your tests fail. This lets you find more bugs in your code with less work.

e.g.

@given(st.lists(st.floats(allow_nan=False, allow_infinity=False), min_size=1))
def test_mean(xs):
    assert min(xs) <= mean(xs) <= max(xs)
Falsifying example: test_mean(
  xs=[1.7976321109618856e+308, 6.102390043022755e+303]
)

Hypothesis is extremely practical and advances the state of the art of unit testing by some way. It’s easy to use, stable, and powerful. If you’re not using Hypothesis to test your project then you’re missing out.

Quick Start/Installation

If you just want to get started:

pip install hypothesis

Project details

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Maintainers
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Project links
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6.152.7

6.152.6

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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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