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

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

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

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

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

  • 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 – If this is set to 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 a.

combine_size: int, optional

The number of chunks to combine.


nansum – A new tensor holding the result is returned unless out is specified, in which it is returned. The result has the same size as a, and the same shape as a if axis is not None or a is a 1-d array.

Return type


See also


Sum across tensor propagating NaNs.


Show which elements are NaN.


Show which elements are not NaN or +/-inf.


If both positive and negative infinity are present, the sum will be Not A Number (NaN).


>>> import mars.tensor as mt
>>> mt.nansum(1).execute()
>>> mt.nansum([1]).execute()
>>> mt.nansum([1, mt.nan]).execute()
>>> a = mt.array([[1, 1], [1, mt.nan]])
>>> mt.nansum(a).execute()
>>> mt.nansum(a, axis=0).execute()
array([ 2.,  1.])
>>> mt.nansum([1, mt.nan, mt.inf]).execute()
>>> mt.nansum([1, mt.nan, mt.NINF]).execute()
>>> mt.nansum([1, mt.nan, mt.inf, -mt.inf]).execute() # both +/- infinity present