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numpy.divmod
- numpy.divmod(x1, x2, [out1, out2, ]/, [out=(None, None), ]*, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])=<ufunc 'divmod'>
-
Return element-wise quotient and remainder simultaneously.
New in version 1.13.0.
np.divmod(x, y)
is equivalent to(x // y, x % y)
, but faster because it avoids redundant work. It is used to implement the Python built-in functiondivmod
on NumPy arrays.- Parameters
-
- x1array_like
-
Dividend array.
- x2array_like
-
Divisor array. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output). - outndarray, None, or tuple of ndarray and None, optional
-
A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
- wherearray_like, optional
-
This condition is broadcast over the input. At locations where the condition is True, the
out
array will be set to the ufunc result. Elsewhere, theout
array will retain its original value. Note that if an uninitializedout
array is created via the defaultout=None
, locations within it where the condition is False will remain uninitialized. - **kwargs
-
For other keyword-only arguments, see the ufunc docs.
- Returns
-
- out1ndarray
-
Element-wise quotient resulting from floor division. This is a scalar if both
x1
andx2
are scalars. - out2ndarray
-
Element-wise remainder from floor division. This is a scalar if both
x1
andx2
are scalars.
See also
-
floor_divide
-
Equivalent to Python’s
//
operator. -
remainder
-
Equivalent to Python’s
%
operator. -
modf
-
Equivalent to
divmod(x, 1)
for positivex
with the return values switched.
Examples
>>> np.divmod(np.arange(5), 3) (array([0, 0, 0, 1, 1]), array([0, 1, 2, 0, 1]))
The
divmod
function can be used as a shorthand fornp.divmod
on ndarrays.>>> x = np.arange(5) >>> divmod(x, 3) (array([0, 0, 0, 1, 1]), array([0, 1, 2, 0, 1]))
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https://numpy.org/doc/1.23/reference/generated/numpy.divmod.html