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# numpy.divide

`numpy.``divide`(x1, x2[, out])

  Divide arguments element-wise.

  Parameters: | x1 : array_like Dividend array. x2 : array_like Divisor array.
  Returns: | y : {ndarray, scalar} The quotient x1/x2, element-wise. Returns a scalar if both x1 and x2 are scalars.

  See also

  `seterr`
    Set whether to raise or warn on overflow, underflow and division by zero.

  Notes

  Equivalent to x1 / x2 in terms of array-broadcasting.

  Behavior on division by zero can be changed using seterr.

  When both x1 and x2 are of an integer type, divide will return integers and throw away the fractional part. Moreover, division by zero always yields zero in integer arithmetic.

  Examples

      >>> np.divide(2.0, 4.0)
      0.5
      >>> x1 = np.arange(9.0).reshape((3, 3))
      >>> x2 = np.arange(3.0)
      >>> np.divide(x1, x2)
      array([[ NaN,  1. ,  1. ],
             [ Inf,  4. ,  2.5],
             [ Inf,  7. ,  4. ]])

  Note the behavior with integer types:

      >>> np.divide(2, 4)
      0
      >>> np.divide(2, 4.)
      0.5

  Division by zero always yields zero in integer arithmetic, and does not raise an exception or a warning:

      >>> np.divide(np.array([0, 1], dtype=int), np.array([0, 0], dtype=int))
      array([0, 0])

  Division by zero can, however, be caught using seterr:

      >>> old_err_state = np.seterr(divide='raise')
      >>> np.divide(1, 0)
      Traceback (most recent call last):
        File "<stdin>", line 1, in <module>
      FloatingPointError: divide by zero encountered in divide

      >>> ignored_states = np.seterr(**old_err_state)
      >>> np.divide(1, 0)
      0

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