numpy.cumulative_prod#

numpy.cumulative_prod(x, /, *, axis=None, dtype=None, out=None, include_initial=False)[source]#

Return the cumulative product of elements along a given axis.

This function is an Array API compatible alternative to numpy.cumprod.

Parameters:
xarray_like

Input array.

axisint, optional

Axis along which the cumulative product is computed. The default (None) is only allowed for one-dimensional arrays. For arrays with more than one dimension axis is required.

dtypedtype, optional

Type of the returned array, as well as of the accumulator in which the elements are multiplied. If dtype is not specified, it defaults to the dtype of x, unless x has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.

outndarray, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type of the resulting values will be cast if necessary. See Output type determination for more details.

include_initialbool, optional

Boolean indicating whether to include the initial value (ones) as the first value in the output. With include_initial=True the shape of the output is different than the shape of the input. Default: False.

Returns:
cumulative_prod_along_axisndarray

A new array holding the result is returned unless out is specified, in which case a reference to out is returned. The result has the same shape as x if include_initial=False.

Notes

Arithmetic is modular when using integer types, and no error is raised on overflow.

Examples

>>> a = np.array([1, 2, 3])
>>> np.cumulative_prod(a)  # intermediate results 1, 1*2
...                        # total product 1*2*3 = 6
array([1, 2, 6])
>>> a = np.array([1, 2, 3, 4, 5, 6])
>>> np.cumulative_prod(a, dtype=float) # specify type of output
array([   1.,    2.,    6.,   24.,  120.,  720.])

The cumulative product for each column (i.e., over the rows) of b:

>>> b = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.cumulative_prod(b, axis=0)
array([[ 1,  2,  3],
       [ 4, 10, 18]])

The cumulative product for each row (i.e. over the columns) of b:

>>> np.cumulative_prod(b, axis=1)
array([[  1,   2,   6],
       [  4,  20, 120]])
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