This patch improves the docstrings of several Hypothesis strategies, by clarifying markup and adding cross-references. There is no runtime change.
Thanks to Elizabeth Williams and Serah Njambi Rono for their contributions at the SciPy 2019 sprints!
4.28.1
Not secure
-------------------
This patch improves the behaviour of the :func:`~hypothesis.strategies.text` strategy when passed an ``alphabet`` which is not a strategy. The value is now interpreted as ``include_characters`` to :func:`~hypothesis.strategies.characters` instead of a sequence for :func:`~hypothesis.strategies.sampled_from`, which standardises the distribution of examples and the shrinking behaviour.
You can get the previous behaviour by using ``lists(sampled_from(alphabet)).map("".map)`` instead.
4.28.0
Not secure
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This release deprecates ``find()``. The ``.example()`` method is a better replacement if you want *an* example, and for the rare occasions where you want the *minimal* example you can get it from :func:`given <hypothesis.given>`.
:func:`given <hypothesis.given>` has steadily outstripped ``find()`` in both features and performance over recent years, and as we do not have the resources to maintain and test both we think it is better to focus on just one.
4.27.0
Not secure
-------------------
This release refactors the implementation of the ``.example()`` method, to more accurately represent the data which will be generated by :func:`given <hypothesis.given>`.
As a result, calling ``s.example()`` on an empty strategy ``s`` (such as :func:`~hypothesis.strategies.nothing`) now raises ``Unsatisfiable`` instead of the old ``NoExamples`` exception.
4.26.4
Not secure
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This patch ensures that the Pandas extra will keep working when Python 3.8 removes abstract base classes from the top-level :obj:`python:collections` namespace. This also fixes the relevant warning in Python 3.7, but there is no other difference in behaviour and you do not need to do anything.
4.26.3
Not secure
-------------------
This release fixes :issue:`2027`, by changing the way Hypothesis tries to generate distinct examples to be more efficient.
This may result in slightly different data distribution, and should improve generation performance in general, but should otherwise have minimal user impact.