# nanmean¶

paddle. nanmean ( x, axis=None, keepdim=False, name=None ) [source]

Compute the arithmetic mean along the specified axis, ignoring NaNs.

Parameters
• x (Tensor) – The input Tensor with data type uint16, float16, float32, float64.

• axis (int|list|tuple, optional) – The axis along which to perform nanmean calculations. `axis` should be int, list(int) or tuple(int). If `axis` is a list/tuple of dimension(s), nanmean is calculated along all element(s) of `axis` . `axis` or element(s) of `axis` should be in range [-D, D), where D is the dimensions of `x` . If `axis` or element(s) of `axis` is less than 0, it works the same way as \(axis + D\) . If `axis` is None, nanmean is calculated over all elements of `x`. Default is None.

• keepdim (bool, optional) – Whether to reserve the reduced dimension(s) in the output Tensor. If `keepdim` is True, the dimensions of the output Tensor is the same as `x` except in the reduced dimensions(it is of size 1 in this case). Otherwise, the shape of the output Tensor is squeezed in `axis` . Default is False.

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor, results of arithmetic mean along `axis` of `x`, with the same data type as `x`.

Examples

```>>> import paddle
>>> # x is a 2-D Tensor:
>>> x = paddle.to_tensor([[float('nan'), 0.3, 0.5, 0.9],
...                       [0.1, 0.2, float('-nan'), 0.7]])
>>> out1
0.44999996)
>>> out2
[0.10000000, 0.25000000, 0.50000000, 0.79999995])
>>> out3 = paddle.nanmean(x, axis=0, keepdim=True)
>>> out3
[[0.10000000, 0.25000000, 0.50000000, 0.79999995]])
>>> out4
[0.56666666, 0.33333334])
>>> out5 = paddle.nanmean(x, axis=1, keepdim=True)
>>> out5
[[0.56666666],
[0.33333334]])

>>> # y is a 3-D Tensor:
>>> y = paddle.to_tensor([[[1, float('nan')], [3, 4]],
...                       [[5, 6], [float('-nan'), 8]]])
>>> out6 = paddle.nanmean(y, axis=[1, 2])
>>> out6