如果您更多地依赖duck-typing，则可以重写函数以返回与输出相同的输入类型。
这将使您不必在调用.map之前显式地返回到.index

import pandas as pd

df = pd.DataFrame({'ID': ['A', 'A', 'A', 'X', 'X', 'Y'})

def get_weights_inverse_num_of_samples(label_counts, power=1):
    """Using object methods here instead of coercing to numpy ndarray"""

    no_of_classes = len(label_counts)
    weights_for_samples = 1 / (label_counts ** power)
    return weights_for_samples / weights_for_samples.sum() * no_of_classes

# select the column before using `.value_counts()`
#   this saves us from ending up with a `MultiIndex` Series
freq = df['ID'].value_counts() 

weights = get_weights_inverse_num_of_samples(freq)

print(weights)
# A    0.545455
# X    0.818182
# Y    1.636364

# note that now our weights are still a `pd.Series` 
#  that we can align directly against our `"ID"` column

df['sample_weight'] = df['ID'].map(weights)

print(df)
#   ID  sample_weight
# 0  A       0.545455
# 1  A       0.545455
# 2  A       0.545455
# 3  X       0.818182
# 4  X       0.818182
# 5  Y       1.636364

您可以map这些值：

df['sample_weight'] = df['ID'].map(dict(zip(freq.index.get_level_values(0), weights)))

• 注意：value_counts返回一个单级别的MultiIndex，因此需要get_level_values。*

如@ScottBoston所述，更好的方法是使用：

freq = df['ID'].value_counts()

df['sample_weight'] = df['ID'].map(dict(zip(freq.index, weights)))

输出：

ID  sample_weight
0  A       0.545455
1  A       0.545455
2  A       0.545455
3  X       0.818182
4  X       0.818182
5  Y       1.636364