numpy.prod¶
- numpy.prod(a, axis=None, dtype=None, out=None, keepdims=False)[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) is perform a product over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis.
New in version 1.7.0.
If this is a tuple of ints, a product is performed on multiple axes, instead of a single axis or all the axes as before.
dtype : data-type, optional
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 original arr.
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 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