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# torch.nanmedian

torch.nanmedian(input) → Tensor

  Returns the median of the values in `input`, ignoring `NaN` values.

  This function is identical to `torch.median()` when there are no `NaN` values in `input`. When `input` has one or more `NaN` values, `torch.median()` will always return `NaN`, while this function will return the median of the non-`NaN` elements in `input`. If all the elements in `input` are `NaN` it will also return `NaN`.

  Parameters:

    input (Tensor) – the input tensor.

  Example:

      >>> a = torch.tensor([1, float('nan'), 3, 2])
      >>> a.median()
      tensor(nan)
      >>> a.nanmedian()
      tensor(2.)

  torch.nanmedian(input, dim=- 1, keepdim=False, *, out=None)

  Returns a namedtuple `(values, indices)` where `values` contains the median of each row of `input` in the dimension `dim`, ignoring `NaN` values, and `indices` contains the index of the median values found in the dimension `dim`.

  This function is identical to `torch.median()` when there are no `NaN` values in a reduced row. When a reduced row has one or more `NaN` values, `torch.median()` will always reduce it to `NaN`, while this function will reduce it to the median of the non-`NaN` elements. If all the elements in a reduced row are `NaN` then it will be reduced to `NaN`, too.

  Parameters:

    - input (Tensor) – the input tensor.

    - dim (int) – the dimension to reduce.

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

  Keyword Arguments:

    out ((Tensor, Tensor), optional) – The first tensor will be populated with the median values and the second tensor, which must have dtype long, with their indices in the dimension `dim` of `input`.

  Example:

      >>> a = torch.tensor([[2, 3, 1], [float('nan'), 1, float('nan')]])
      >>> a
      tensor([[2., 3., 1.],
              [nan, 1., nan]])
      >>> a.median(0)
      torch.return_types.median(values=tensor([nan, 1., nan]), indices=tensor([1, 1, 1]))
      >>> a.nanmedian(0)
      torch.return_types.nanmedian(values=tensor([2., 1., 1.]), indices=tensor([0, 1, 0]))

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