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# torch.nanmean

torch.nanmean(input, dim=None, keepdim=False, *, dtype=None, out=None) → Tensor

  Computes the mean of all non-NaN elements along the specified dimensions.

  This function is identical to `torch.mean()` when there are no NaN values in the `input` tensor. In the presence of NaN, `torch.mean()` will propagate the NaN to the output whereas `torch.nanmean()` will ignore the NaN values (torch.nanmean(a) is equivalent to torch.mean(a[~a.isnan()])).

  If `keepdim` is `True`, the output tensor is of the same size as `input` except in the dimension(s) `dim` where it is of size 1. Otherwise, `dim` is squeezed (see `torch.squeeze()`), resulting in the output tensor having 1 (or `len(dim)`) fewer dimension(s).

  Parameters:

    - input (Tensor) – the input tensor.

    - dim (int or tuple of ints, optional) – the dimension or dimensions to reduce. If `None`, all dimensions are reduced.

    - keepdim (bool) – whether the output tensor has `dim` retained or not.

  Keyword Arguments:

    - dtype (`torch.dtype`, optional) – the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None.

    - out (Tensor, optional) – the output tensor.

  See also

  `torch.mean()` computes the mean value, propagating NaN.

  Example:

      >>> x = torch.tensor([[torch.nan, 1, 2], [1, 2, 3]])
      >>> x.mean()
      tensor(nan)
      >>> x.nanmean()
      tensor(1.8000)
      >>> x.mean(dim=0)
      tensor([   nan, 1.5000, 2.5000])
      >>> x.nanmean(dim=0)
      tensor([1.0000, 1.5000, 2.5000])

      # If all elements in the reduced dimensions are NaN then the result is NaN
      >>> torch.tensor([torch.nan]).nanmean()
      tensor(nan)

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