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 = paddle.nanmean(x)
# 0.44999996
out2 = paddle.nanmean(x, axis=0)
# [0.1, 0.25, 0.5, 0.79999995]
out3 = paddle.nanmean(x, axis=0, keepdim=True)
# [[0.1, 0.25, 0.5, 0.79999995]]
out4 = paddle.nanmean(x, axis=1)
# [0.56666666 0.33333334]
out5 = paddle.nanmean(x, axis=1, keepdim=True)
# [[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])
# [2.66666675, 6.33333349]
out7 = paddle.nanmean(y, axis=[0, 1])
# [3., 6.]