python - np.quantile 计算错误？

Question

在某些设置下，np.quantile 在确定正确的分位数时会出错。这是错误吗？

x = np.array([374, 358, 341, 355, 342, 334, 353, 346, 355, 344,
              349, 330, 352, 328, 336, 359, 361, 345, 324, 386,
              334, 370, 349, 327, 342, 354, 361, 354, 377, 324])

q = np.quantile(x, 0.25)

print(q)

print(len(x[x<=q]) / len(x))

print(len(x[x>=q]) / len(x))

输出:

337.25

0.26666666666666666

0.7333333333333333

0.73 表示只有 73% 的值大于或等于确定的分位数；根据定义，它应该是 >= 75%

Answer 1

https://github.com/numpy/numpy/blob/v1.15.1/numpy/lib/function_base.py#L3543-L3644

default value is linear
    interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
        This optional parameter specifies the interpolation method to
        use when the desired quantile lies between two data points
        ``i < j``:
            * linear: ``i + (j - i) * fraction``, where ``fraction``
              is the fractional part of the index surrounded by ``i``
              and ``j``.
            * lower: ``i``.
            * higher: ``j``.
            * nearest: ``i`` or ``j``, whichever is nearest.
            * midpoint: ``(i + j) / 2``.

如果你选择“更高”，你会得到你想要的