mars.tensor.nanprod(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None)[source]#

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

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

  • a (array_like) – Tensor containing numbers whose product is desired. If a is not an tensor, a conversion is attempted.

  • axis (int, optional) – Axis along which the product is computed. The default is to compute the product of the flattened tensor.

  • dtype (data-type, optional) – The type of the returned tensor 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 (Tensor, optional) – Alternate output tensor 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 doc.ufuncs for 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.

  • combine_size (int, optional) – The number of chunks to combine.


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

Return type


See also

Product across array propagating NaNs.


Show which elements are NaN.


>>> import mars.tensor as mt
>>> mt.nanprod(1).execute()
>>> mt.nanprod([1]).execute()
>>> mt.nanprod([1, mt.nan]).execute()
>>> a = mt.array([[1, 2], [3, mt.nan]])
>>> mt.nanprod(a).execute()
>>> mt.nanprod(a, axis=0).execute()
array([ 3.,  2.])