8000 DOC: clarify int type constraint in numpy.bincount by onitake · Pull Request #23552 · numpy/numpy · GitHub
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DOC: clarify int type constraint in numpy.bincount #23552

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DOC: clarify int type constraint in numpy.bincount #23552

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07954a0
DOC: clarify int type constraint in numpy.bincount
onitake Apr 7, 2023
a7d5de1
DOC: clarify upper limit of bincount input dtype
onitake Apr 8, 2023
782351e
DOC: correct text as requested in review
onitake Apr 9, 2023
b037f8a
DOC: Corrected one more instance of 'integer' vs 'array index'
onitake May 11, 2025
ace6dbd
DOC: clarified what the native index type is
onitake May 11, 2025
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DOC: correct text as requested in review
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@onitake
onitake committed May 11, 2025
commit 782351eb109a2fb0610ad655049c675557d7e0af
11 changes: 3 additions & 8 deletions numpy/_core/multiarray.py
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Expand Up @@ -953,7 +953,8 @@ def bincount(x, weights=None, minlength=None):
If the input is not 1-dimensional, or contains elements with negative
values, or if `minlength` is negative.
TypeError
If the type of the input is float or complex.
If the type of the input is float or complex, or larger than the
native integer type.

See Also
--------
Expand All @@ -972,7 +973,7 @@ def bincount(x, weights=None, minlength=None):
True

The input array needs have an integer dtype smaller or equal to the
native Python array index type, otherwise a TypeError is raised:
native array index type, otherwise a TypeError is raised:

>>> np.bincount(np.arange(5, dtype=float))
Traceback (most recent call last):
Expand All @@ -988,12 +989,6 @@ def bincount(x, weights=None, minlength=None):
... # TypeError: Cannot cast array data from dtype('int64') to
... # dtype('int32') according to the rule 'safe'

The easiest way to solve this is by always using the Python int type
(or np.int_):

>>> np.bincount(np.arange(5, dtype=int))
array([1, 1, 1, 1, 1])

A possible use of ``bincount`` is to perform sums over
variable-size chunks of an array, using the ``weights`` keyword.

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