numpy.prod¶

numpy.
prod
(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>)[source]¶ 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.
New in version 1.7.0.
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 ofndarray
, 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.New in version 1.15.0.
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) # random 16
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.])
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