- dask.array.mod(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'remainder'>¶
This docstring was copied from numpy.remainder.
Some inconsistencies with the Dask version may exist.
Returns the element-wise remainder of division.
Computes the remainder complementary to the floor_divide function. It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign as the divisor x2. The MATLAB function equivalent to
This should not be confused with:
Python 3.7’s math.remainder and C’s
remainder, which computes the IEEE remainder, which are the complement to
round(x1 / x2).
remfunction and or the C
%operator which is the complement to
int(x1 / x2).
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.
For other keyword-only arguments, see the ufunc docs.
The element-wise remainder of the quotient
floor_divide(x1, x2). This is a scalar if both x1 and x2 are scalars.
Equivalent of Python
Simultaneous floor division and remainder.
Equivalent of the MATLAB
Returns 0 when x2 is 0 and both x1 and x2 are (arrays of) integers.
modis an alias of
>>> np.remainder([4, 7], [2, 3]) array([0, 1]) >>> np.remainder(np.arange(7), 5) array([0, 1, 2, 3, 4, 0, 1])
%operator can be used as a shorthand for
>>> x1 = np.arange(7) >>> x1 % 5 array([0, 1, 2, 3, 4, 0, 1])