dask.array.nanprod
dask.array.nanprod¶
- dask.array.nanprod(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None)[source]¶
Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones.
This docstring was copied from numpy.nanprod.
Some inconsistencies with the Dask version may exist.
One is returned for slices that are all-NaN or empty.
New in version 1.10.0.
- Parameters
- aarray_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.
- dtypedata-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.
- outndarray, 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 Output type determination for more details. The casting of NaN to integer can yield unexpected results.- keepdimsbool, 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.
- initialscalar, optional (Not supported in Dask)
The starting value for this product. See ~numpy.ufunc.reduce for details.
New in version 1.22.0.
- wherearray_like of bool, optional (Not supported in Dask)
Elements to include in the product. See ~numpy.ufunc.reduce for details.
New in version 1.22.0.
- Returns
- nanprodndarray
A new array holding the result is returned unless out is specified, in which case it is returned.
See also
numpy.prod
Product across array propagating NaNs.
isnan
Show which elements are NaN.
Examples
>>> np.nanprod(1) 1 >>> np.nanprod([1]) 1 >>> np.nanprod([1, np.nan]) 1.0 >>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nanprod(a) 6.0 >>> np.nanprod(a, axis=0) array([3., 2.])