8000 ENH: add initial and out parameters to bincount by arthurfeeney · Pull Request #22907 · numpy/numpy · GitHub
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ENH: add initial and out parameters to bincount #22907

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ENH: add initial and out parameters to bincount #22907

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23 changes: 17 additions & 6 deletions numpy/core/multiarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -889,16 +889,17 @@ def vdot(a, b):


@array_function_from_c_func_and_dispatcher(_multiarray_umath.bincount)
def bincount(x, weights=None, minlength=None):
def bincount(x, weights=None, minlength=None, initial=None, out=None):
"""
bincount(x, /, weights=None, minlength=0)
bincount(x, /, weights=None, minlength=0, initial=None, out=None)

Count number of occurrences of each value in array of non-negative ints.

The number of bins (of size 1) is one larger than the largest value in
`x`. If `minlength` is specified, there will be at least this number
of bins in the output array (though it will be longer if necessary,
depending on the contents of `x`).
depending on the contents of `x`). If `out` is specified, its size must
be large enough to contain all of the bins.
Each bin gives the number of occurrences of its index value in `x`.
If `weights` is specified the input array is weighted by it, i.e. if a
value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
Expand All @@ -912,22 +913,32 @@ def bincount(x, weights=None, minlength=None):
Weights, array of the same shape as `x`.
minlength : int, optional
A minimum number of bins for the output array.
initial: ndarray, 1 dimension, optional
Array of initial values for each bin. It must have the same shape and
buffer length as the expected output
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I think ideally it would just broadcast to the output (i.e., effectively the default is initial=0).

out: ndarray, 1 dimenion, optional
Alternative output array in which to place the result. It must have the
same shape and buffer length as the expected output but the type will
be cast when safe.

.. versionadded:: 1.6.0

Returns
-------
out : ndarray of ints
The result of binning the input array.
The length of `out` is equal to ``np.amax(x)+1``.
The length of `out` is equal to ``max(minlength, np.amax(x)+1)``.
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Strictly, it can be even larger if the input out has a larger size.


Raises
------
ValueError
If the input is not 1-dimensional, or contains elements with negative
values, or if `minlength` is negative.
values, or if `minlength` is negative, or initial or out do not have
sufficient sizes, or initial and out have different sizes.
TypeError
If the type of the input is float or complex.
If the type of the input is float or complex, or `minlength` cannot be
converted to int, or `initial` or `out` cannot be safely converted to
the expected type.

See Also
--------
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2 changes: 2 additions & 0 deletions numpy/core/multiarray.pyi
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Expand Up @@ -417,6 +417,8 @@ def bincount(
/,
weights: None | ArrayLike = ...,
minlength: SupportsIndex = ...,
initial: None | NDArray[Any] = ...,
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So, I think scalar would be OK in principle.

out: None | NDArray[Any] = ...,
) -> NDArray[intp]: ...

def copyto(
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