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# numpy.prod

`numpy.``prod`(a, axis=None, dtype=None, out=None)

  Return the product of array elements over a given axis.

  Parameters: | a : array_like Input data. axis : int, optional Axis over which the product is taken. By default, the product of all elements is calculated. dtype : data-type, optional The data-type of the returned array, as well as of the accumulator in which the elements are multiplied. By default, if a is of integer type, dtype is the default platform integer. (Note: if the type of a is unsigned, then so is dtype.) Otherwise, the dtype is the same as that of a. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary.
  Returns: | product_along_axis : ndarray, see dtype parameter above. An array shaped as a but with the specified axis removed. Returns a reference to out if specified.

  See also

  `ndarray.prod`
    equivalent method

  Notes

  Arithmetic is modular when using integer types, and no error is raised on overflow. That means that, on a 32-bit platform:

      >>> x = np.array([536870910, 536870910, 536870910, 536870910])
      >>> np.prod(x) #random
      16

  Examples

  By default, calculate the product of all elements:

      >>> np.prod([1.,2.])
      2.0

  Even when the input array is two-dimensional:

      >>> np.prod([[1.,2.],[3.,4.]])
      24.0

  But we can also specify the axis over which to multiply:

      >>> np.prod([[1.,2.],[3.,4.]], axis=1)
      array([  2.,  12.])

  If the type of x is unsigned, then the output type is the unsigned platform integer:

      >>> x = np.array([1, 2, 3], dtype=np.uint8)
      >>> np.prod(x).dtype == np.uint
      True

  If x is of a signed integer type, then the output type is the default platform integer:

      >>> x = np.array([1, 2, 3], dtype=np.int8)
      >>> np.prod(x).dtype == np.int
      True

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