Question

我想计算熊猫数据帧每一行的加权中值。

我发现了这个不错的功能（https://stackoverflow.com/a/29677616/10588967），但似乎无法传递2d数组。

def weighted_quantile(values, quantiles, sample_weight=None, values_sorted=False, old_style=False):
""" Very close to numpy.percentile, but supports weights.
NOTE: quantiles should be in [0, 1]!
:param values: numpy.array with data
:param quantiles: array-like with many quantiles needed
:param sample_weight: array-like of the same length as `array`
:param values_sorted: bool, if True, then will avoid sorting of initial array
:param old_style: if True, will correct output to be consistent with numpy.percentile.
:return: numpy.array with computed quantiles.
"""
values = numpy.array(values)
quantiles = numpy.array(quantiles)
if sample_weight is None:
    sample_weight = numpy.ones(len(values))
sample_weight = numpy.array(sample_weight)
assert numpy.all(quantiles >= 0) and numpy.all(quantiles <= 1), 'quantiles should be in [0, 1]'

if not values_sorted:
    sorter = numpy.argsort(values)
    values = values[sorter]
    sample_weight = sample_weight[sorter]

weighted_quantiles = numpy.cumsum(sample_weight) - 0.5 * sample_weight
if old_style:
    # To be convenient with numpy.percentile
    weighted_quantiles -= weighted_quantiles[0]
    weighted_quantiles /= weighted_quantiles[-1]
else:
    weighted_quantiles /= numpy.sum(sample_weight)
return numpy.interp(quantiles, weighted_quantiles, values)

使用链接中的代码，可以完成以下操作：

weighted_quantile([1, 2, 9, 3.2, 4], [0.0, 0.5, 1.])

但是，这不起作用：

values = numpy.random.randn(10,5)
quantiles = [0.0, 0.5, 1.]
sample_weight = numpy.random.randn(10,5)
weighted_quantile(values, quantiles, sample_weight)

我收到以下错误：

weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight

ValueError：操作数不能与形状（250，）（10,5,5）一起广播

问题是否有可能以向量化的方式在数据帧上应用此加权分位数函数，或者我只能使用.apply（）实现此目的？

非常感谢您的光临！

Answer 1

 np.cumsum(sample_weight)

返回一维列表。所以您想使用

将其重塑为（10,5,5）

np.cumsum(sample_weight).reshape(10,5,5)

Python：数组的每一行的加权百分位数

1 个答案: