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# numpy.fmod

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

  Returns the element-wise remainder of division.

  This is the NumPy implementation of the C library function fmod, the remainder has the same sign as the dividend x1. It is equivalent to the Matlab(TM) `rem` function and should not be confused with the Python modulus operator `x1 % x2`.

  Parameters:
    x1 array_like

      Dividend.

    x2 array_like

      Divisor. 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.

    **kwargs

      For other keyword-only arguments, see the ufunc docs.

  Returns:
    y array_like

      The remainder of the division of x1 by x2. This is a scalar if both x1 and x2 are scalars.

  See also

  `remainder`

    Equivalent to the Python `%` operator.

  `divide`

  Notes

  The result of the modulo operation for negative dividend and divisors is bound by conventions. For `fmod`, the sign of result is the sign of the dividend, while for `remainder` the sign of the result is the sign of the divisor. The `fmod` function is equivalent to the Matlab(TM) `rem` function.

  Examples

      >>> import numpy as np
      >>> np.fmod([-3, -2, -1, 1, 2, 3], 2)
      array([-1,  0, -1,  1,  0,  1])
      >>> np.remainder([-3, -2, -1, 1, 2, 3], 2)
      array([1, 0, 1, 1, 0, 1])

      >>> np.fmod([5, 3], [2, 2.])
      array([ 1.,  1.])
      >>> a = np.arange(-3, 3).reshape(3, 2)
      >>> a
      array([[-3, -2],
             [-1,  0],
             [ 1,  2]])
      >>> np.fmod(a, [2,2])
      array([[-1,  0],
             [-1,  0],
             [ 1,  0]])