nanquantile¶

paddle. nanquantile ( x, q, axis=None, keepdim=False ) [source]

Compute the quantile of the input as if NaN values in input did not exist. If all values in a reduced row are NaN, then the quantiles for that reduction will be NaN.

Parameters
• x (Tensor) – The input Tensor, it’s data type can be float32, float64, int32, int64.

• q (int|float|list) – The q for calculate quantile, which should be in range [0, 1]. If q is a list, each q will be calculated and the first dimension of output is same to the number of `q` .

• axis (int|list, optional) – The axis along which to calculate quantile. `axis` should be int or list of int. `axis` should be in range [-D, D), where D is the dimensions of `x` . If `axis` is less than 0, it works the same way as \(axis + D\). If `axis` is a list, quantile is calculated over all elements of given axises. If `axis` is None, quantile 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 quantile along `axis` of `x`. In order to obtain higher precision, data type of results will be float64.

Examples

```import paddle

[[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]],
dtype="float32")
x[0,0] = float("nan")

y1 = paddle.nanquantile(x, q=0.5, axis=[0, 1])
#        5.)

#        [2.50000000, 7.        ])

y3 = paddle.nanquantile(x, q=[0.3, 0.5], axis=0)
# Tensor(shape=[2, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
#        [[5.        , 2.50000000, 3.50000000, 4.50000000, 5.50000000],
#         [5.        , 3.50000000, 4.50000000, 5.50000000, 6.50000000]])

y4 = paddle.nanquantile(x, q=0.8, axis=1, keepdim=True)
# Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=True,
#        [[3.40000000],
#         [8.20000000]])