numpy.prod#

numpy.prod(a,axis=None,dtype=None,out=None,keepdims=<novalue>,initial=<novalue>,where=<novalue>)[source]#

Return the product of array elements over a given axis.

Parameters:
aarray_like

Input data.

axisNone 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 theinput array. If axis is negative it counts from the last to thefirst axis.

If axis is a tuple of ints, a product is performed on all of theaxes specified in the tuple instead of a single axis or all theaxes as before.

dtypedtype, optional

The type of the returned array, as well as of the accumulator inwhich the elements are multiplied. The dtype ofa is used bydefault unlessa has an integer dtype of less precision than thedefault platform integer. In that case, ifa is signed then theplatform integer is used while ifa is unsigned then an unsignedinteger of the same precision as the platform integer is used.

outndarray, optional

Alternative output array in which to place the result. It must havethe same shape as the expected output, but the type of the outputvalues will be cast if necessary.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in theresult as dimensions with size one. With this option, the resultwill broadcast correctly against the input array.

If the default value is passed, thenkeepdims will not bepassed through to theprod method of sub-classes ofndarray, however any non-default value will be. If thesub-class’ method does not implementkeepdims anyexceptions will be raised.

initialscalar, optional

The starting value for this product. Seereducefor details.

wherearray_like of bool, optional

Elements to include in the product. Seereducefor details.

Returns:
product_along_axisndarray, seedtype parameter above.

An array shaped asa but with the specified axis removed.Returns a reference toout if specified.

See also

ndarray.prod

equivalent method

Output type determination

Notes

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

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

Even when the input array is two-dimensional:

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

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

>>>np.prod(a,axis=1)array([  2.,  12.])>>>np.prod(a,axis=0)array([3., 8.])

Or select specific elements to include:

>>>np.prod([1.,np.nan,3.],where=[True,False,True])3.0

If the type ofx is unsigned, then the output type isthe unsigned platform integer:

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

Ifx is of a signed integer type, then the output typeis the default platform integer:

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

You can also start the product with a value other than one:

>>>np.prod([1,2],initial=5)10
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