numpy.mod¶

numpy.
mod
(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'remainder'>¶ Return elementwise 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 tonp.remainder
ismod
.Warning
This should not be confused with:
Python 3.7’s
math.remainder
and C’sremainder
, which computes the IEEE remainder, which are the complement toround(x1 / x2)
.The MATLAB
rem
function and or the C%
operator which is the complement toint(x1 / x2)
.
 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 freshlyallocated 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 keywordonly arguments, see the ufunc docs.
 Returns
 yndarray
The elementwise remainder of the quotient
floor_divide(x1, x2)
. This is a scalar if both x1 and x2 are scalars.
See also
floor_divide
Equivalent of Python
//
operator.divmod
Simultaneous floor division and remainder.
fmod
Equivalent of the MATLAB
rem
function.
Notes
Returns 0 when x2 is 0 and both x1 and x2 are (arrays of) integers.
mod
is an alias ofremainder
.Examples
>>> np.remainder([4, 7], [2, 3]) array([0, 1]) >>> np.remainder(np.arange(7), 5) array([0, 1, 2, 3, 4, 0, 1])