flopscope.

flopscope.numpy.nanprod

fnp.nanprod(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[flopscope source][numpy source]

Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones.

Adapted from NumPy docs np.nanprod

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

Product ignoring NaNs.

One is returned for slices that are all-NaN or empty.

Parameters

a:array_like

Array containing numbers whose product is desired. If a is not an array, a conversion is attempted.

axis:{int, tuple of int, None}, optional

Axis or axes along which the product is computed. The default is to compute the product of the flattened array.

dtype:data-type, optional

The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of a is used. An exception is when a has an integer type with less precision than the platform (u)intp. In that case, the default will be either (u)int32 or (u)int64 depending on whether the platform is 32 or 64 bits. For inexact inputs, dtype must be inexact.

out:ndarray, optional

Alternate output array in which to place the result. The default is None. If provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details. The casting of NaN to integer can yield unexpected results.

keepdims:bool, optional

If 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.

initial:scalar, optional

The starting value for this product. See reduce for details.

Added in version 1.22.0.
where:array_like of bool, optional

Elements to include in the product. See reduce for details.

Added in version 1.22.0.

Returns

nanprod: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.nanprod(1)
1
>>> flops.nanprod([1])
1
>>> flops.nanprod([1, flops.nan])
1.0
>>> a = flops.array([[1, 2], [3, flops.nan]])
>>> flops.nanprod(a)
6.0
>>> flops.nanprod(a, axis=0)
array([3., 2.])