Return the product of array elements over a given axis.
Parameters: 

a : array_like 
Input data. 
axis : None or int or tuple of ints, optional 
Axis or axes along which a product is performed. The default, axis=None, will calculate the product of all the elements in the input array. If axis is negative it counts from the last to the first axis. If axis is a tuple of ints, a product is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before. 
dtype : dtype, optional 
The type of the returned array, as well as of the accumulator in which the elements are multiplied. The dtype of a is used by default unless a has an integer dtype of less precision than the default platform integer. In that case, if a is signed then the platform integer is used while if a is unsigned then an unsigned integer of the same precision as the platform integer is used. 
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. 
keepdims : bool, optional 
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the prod method of subclasses of ndarray , however any nondefault value will be. If the subclass’ method does not implement keepdims any exceptions will be raised. 
initial : scalar, optional 
The starting value for this product. See reduce for details. 
where : array_like of bool, optional 
Elements to include in the product. See reduce for details. 
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

numpy.doc.ufuncs
 Section “Output arguments”
Notes
Arithmetic is modular when using integer types, and no error is raised on overflow. That means that, on a 32bit platform:
>>> x = np.array([536870910, 536870910, 536870910, 536870910])
>>> np.prod(x)
16 # may vary
The product of an empty array is the neutral element 1:
>>> np.prod([])
1.0
Examples
By default, calculate the product of all elements:
>>> np.prod([1.,2.])
2.0
Even when the input array is twodimensional:
>>> 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.])
Or select specific elements to include:
>>> np.prod([1., np.nan, 3.], where=[True, False, True])
3.0
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 == int
True
You can also start the product with a value other than one:
>>> np.prod([1, 2], initial=5)
10