dask.array.divmod
dask.array.divmod¶
- dask.array.divmod(x1, x2, [out1, out2, ]/, [out=(None, None), ]*, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])[source]¶
This docstring was copied from numpy.divmod.
Some inconsistencies with the Dask version may exist.
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, the out array will retain its original value. Note that if an uninitialized out array is created via the default
out=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 and x2 are scalars.
- out2ndarray
Element-wise remainder from floor division. This is a scalar if both x1 and x2 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 for
np.divmod
on ndarrays.>>> x = np.arange(5) >>> divmod(x, 3) (array([0, 0, 0, 1, 1]), array([0, 1, 2, 0, 1]))