/NumPy 1.17


numpy.mod(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'remainder'>

Return 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 np.remainder is mod.


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).
  • The MATLAB rem function and or the C % operator which is the complement to int(x1 / x2).
x1 : array_like

Dividend array.

x2 : array_like

Divisor array. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

out : ndarray, 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.

where : array_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.

y : ndarray

The element-wise remainder of the quotient floor_divide(x1, x2). This is a scalar if both x1 and x2 are scalars.

See also

Equivalent of Python // operator.
Simultaneous floor division and remainder.
Equivalent of the MATLAB rem function.

divide, floor


Returns 0 when x2 is 0 and both x1 and x2 are (arrays of) integers. mod is an alias of remainder.


>>> np.remainder([4, 7], [2, 3])
array([0, 1])
>>> np.remainder(np.arange(7), 5)
array([0, 1, 2, 3, 4, 0, 1])

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