我有一小段代码需要修改，我无法确切地找到为什么np.ean()在特定情况下工作，而np.min()在Pandas列由嵌套列表组成的情况下不能工作。也许这里有人能澄清一下？
下面这段代码可以完美地工作：

import pandas as pd
import numpy as np

def transformation(custom_df):
    dic = dict(zip(custom_df['customers'], custom_df['values']))
    custom_df['values'] = np.where(custom_df['values'].isna() & (custom_df['valid_neighbors'] >= 1),
                                   custom_df['neighbors'].apply(
                                       lambda row: np.mean([dic[v] for v in row if dic.get(v)])),
                                   custom_df['values'])
    return custom_df

customers = [1, 2, 3, 4, 5, 6]
values = [np.nan, np.nan, 10, np.nan, 11, 12]
neighbors = [[6], [3], [], [3, 5], [6], [5]]
vn = [1, 1, 0, 2, 1, 1]
df2 = pd.DataFrame({'customers': customers, 'values': values, 'neighbors': neighbors, 'valid_neighbors': vn})

   customers  values neighbors  valid_neighbors
0          1     NaN       [6]                1
1          2     NaN       [3]                1
2          3    10.0        []                0
3          4     NaN    [3, 5]                2
4          5    11.0       [6]                1
5          6    12.0       [5]                1

df2 = transformation(df2)

结果是：

customers  values neighbors  valid_neighbors
0          1    12.0       [6]                1
1          2    10.0       [3]                1
2          3    10.0        []                0
3          4    10.5    [3, 5]                2
4          5    11.0       [6]                1
5          6    12.0       [5]                1

但是，如果我在“change()”函数上将np.ean()更改为np.min()，它将返回一个ValueError，这让我纳闷为什么在调用np.ean()函数时没有发生这种情况：

ValueError: zero-size array to reduction operation minimum which has no identity

我想知道我没有满足哪些条件，我可以做些什么来获得预期的结果，这将是：

customers  values neighbors  valid_neighbors
0          1    12.0       [6]                1
1          2    10.0       [3]                1
2          3    10.0        []                0
3          4    10.0    [3, 5]                2
4          5    11.0       [6]                1
5          6    12.0       [5]                1