nanprod(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None)
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.
Array containing numbers whose product is desired. If a is not an array, a conversion is attempted.
Axis or axes along which the product is computed. The default is to compute the product of the flattened array.
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.
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.
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 :None:None:`arr`.
The starting value for this product. See :None:None:`~numpy.ufunc.reduce` for details.
Elements to include in the product. See :None:None:`~numpy.ufunc.reduce` for details.
A new array holding the result is returned unless :None:None:`out` is specified, in which case it is returned.
Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones.
isnan
Show which elements are NaN.
numpy.prod
Product across array propagating NaNs.
>>> np.nanprod(1) # doctest: +SKIP 1This example is valid syntax, but we were not able to check execution
>>> np.nanprod([1]) # doctest: +SKIP 1This example is valid syntax, but we were not able to check execution
>>> np.nanprod([1, np.nan]) # doctest: +SKIP 1.0This example is valid syntax, but we were not able to check execution
>>> a = np.array([[1, 2], [3, np.nan]]) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... np.nanprod(a) # doctest: +SKIP 6.0
>>> np.nanprod(a, axis=0) # doctest: +SKIP array([3., 2.])See :
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them