jax.numpy.nancumprodΒΆ

jax.numpy.nancumprod(a, axis: Union[int, Tuple[int, ...], None] = None, dtype=None, out=None)ΒΆ

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.

LAX-backend implementation of nancumprod(). Original docstring below.

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

New in version 1.12.0.

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

Return type

ndarray

See also

numpy.cumprod()

Cumulative product across array propagating NaNs.

isnan()

Show which elements are NaN.

Examples

>>> np.nancumprod(1)
array([1])
>>> np.nancumprod([1])
array([1])
>>> np.nancumprod([1, np.nan])
array([1.,  1.])
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nancumprod(a)
array([1.,  2.,  6.,  6.])
>>> np.nancumprod(a, axis=0)
array([[1.,  2.],
       [3.,  2.]])
>>> np.nancumprod(a, axis=1)
array([[1.,  2.],
       [3.,  3.]])