mirror of https://github.com/python/cpython
Minor edits to the descriptor guide (GH-123928)
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@ -389,7 +389,9 @@ Here are three practical data validation utilities:
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def validate(self, value):
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if value not in self.options:
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raise ValueError(f'Expected {value!r} to be one of {self.options!r}')
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raise ValueError(
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f'Expected {value!r} to be one of {self.options!r}'
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)
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class Number(Validator):
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@ -469,6 +471,7 @@ The descriptors prevent invalid instances from being created:
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Traceback (most recent call last):
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...
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ValueError: Expected -5 to be at least 0
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>>> Component('WIDGET', 'metal', 'V') # Blocked: 'V' isn't a number
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Traceback (most recent call last):
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...
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@ -1004,7 +1007,6 @@ here is a pure Python equivalent that implements most of the core functionality:
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if doc is None and fget is not None:
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doc = fget.__doc__
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self.__doc__ = doc
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self.__name__ = ''
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def __set_name__(self, owner, name):
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self.__name__ = name
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@ -1303,8 +1305,8 @@ mean, median, and other descriptive statistics that depend on the data. However,
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there may be useful functions which are conceptually related but do not depend
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on the data. For instance, ``erf(x)`` is handy conversion routine that comes up
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in statistical work but does not directly depend on a particular dataset.
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It can be called either from an object or the class: ``s.erf(1.5) --> .9332`` or
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``Sample.erf(1.5) --> .9332``.
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It can be called either from an object or the class: ``s.erf(1.5) --> 0.9332``
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or ``Sample.erf(1.5) --> 0.9332``.
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Since static methods return the underlying function with no changes, the
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example calls are unexciting:
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