flopscope.

flopscope.numpy.nancumprod

fnp.nancumprod(a, axis=None, dtype=None, out=None)[flopscope source][numpy source]

Return the cumulative product of array elements over a given axis treating Not a Numbers (NaNs) as one. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones.

Adapted from NumPy docs np.nancumprod

Areacore
Typecounted
NumPy Refnp.nancumprod
Cost
numel(input)\text{numel}(\text{input})
Flopscope Context

Cumulative product ignoring NaNs.

Ones are returned for slices that are all-NaN or empty.

Parameters

a:array_like

Input array.

axis:int, optional

Axis along which the cumulative product is computed. By default the input is flattened.

dtype:dtype, 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 a, unless a 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.

out:ndarray, 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.

Returns

nancumprod:ndarray

A new array holding the result is returned unless out is specified, in which case it is returned.

See also

Examples

>>> import flopscope.numpy as fnp
>>> flops.nancumprod(1)
array([1])
>>> flops.nancumprod([1])
array([1])
>>> flops.nancumprod([1, flops.nan])
array([1.,  1.])
>>> a = flops.array([[1, 2], [3, flops.nan]])
>>> flops.nancumprod(a)
array([1.,  2.,  6.,  6.])
>>> flops.nancumprod(a, axis=0)
array([[1.,  2.],
       [3.,  2.]])
>>> flops.nancumprod(a, axis=1)
array([[1.,  2.],
       [3.,  3.]])