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 ofx
, unlessx
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 toout
is returned. The result has the same shape asx
ifinclude_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]])