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# numpy.divide

`numpy.``divide`(x1, x2[, out]) = <ufunc 'divide'>

  Divide arguments element-wise.

  Parameters: | x1 : array_like Dividend array. x2 : array_like Divisor array. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs.
  Returns: | y : ndarray or 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`.

  In Python 2, when both `x1` and `x2` are of an integer type, `divide` will behave like `floor_divide`. In Python 3, it behaves like `true_divide`.

  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 (Python 2 only):

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

  Division by zero always yields zero in integer arithmetic (again, Python 2 only), 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
      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