numpy.nanprod#
- numpy.nanprod(a,axis=None,dtype=None,out=None,keepdims=<novalue>,initial=<novalue>,where=<novalue>)[source]#
Return the product of array elements over a given axis treating Not aNumbers (NaNs) as ones.
One is returned for slices that are all-NaN or empty.
- Parameters:
- aarray_like
Array containing numbers whose product is desired. Ifa is not anarray, a conversion is attempted.
- axis{int, tuple of int, None}, optional
Axis or axes along which the product is computed. The default is to computethe product of the flattened array.
- dtypedata-type, optional
The type of the returned array and of the accumulator in which theelements are summed. By default, the dtype ofa is used. Anexception is whena has an integer type with less precision thanthe platform (u)intp. In that case, the default will be either(u)int32 or (u)int64 depending on whether the platform is 32 or 64bits. For inexact inputs, dtype must be inexact.
- outndarray, optional
Alternate output array in which to place the result. The defaultis
None. If provided, it must have the same shape as theexpected output, but the type will be cast if necessary. SeeOutput type determination for more details. The casting of NaN to integercan yield unexpected results.- keepdimsbool, optional
If True, the axes which are reduced are left in the result asdimensions with size one. With this option, the result willbroadcast correctly against the originalarr.
- initialscalar, optional
The starting value for this product. See
reducefor details.New in version 1.22.0.
- wherearray_like of bool, optional
Elements to include in the product. See
reducefor details.New in version 1.22.0.
- Returns:
- nanprodndarray
A new array holding the result is returned unlessout isspecified, in which case it is returned.
See also
numpy.prodProduct across array propagating NaNs.
isnanShow which elements are NaN.
Examples
>>>importnumpyasnp>>>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.])