您可以轻松地做到这一点。例如：

import pandas as pd

df = pd.DataFrame([['Organic Search, Direct', 171], ['Organic Search, Direct, Not Found', 3], ['Direct, Organic Search', 389], ['Direct, Organic Search, Not Found', 8]], columns=['A', 'B'])

print(df)

Out:
      A                                 B
0   Organic Search, Direct             171
1   Organic Search, Direct, Not Found   3
2   Direct, Organic Search             389
3   Direct, Organic Search, Not Found   8

def foo(x):
    x = x.split(', ')
    x.sort()
    return tuple(x)

df.groupby(df.A.apply(foo))['B'].sum()

out:
               A
(Direct, Not Found, Organic Search)     11
(Direct, Organic Search)               560

我不能把这归功于我，但我不知道如何提出它以外的一个单独的答案。
我也是Python新手，使用padu's logic code，我已经根据你最初帖子的数据进行了修改。
我还用.reset_index()替换了['B']以保留原始列，因为求和之前与新分组的值合并在一个列中。我还添加了numeric_only=True，因为如果不指定要求和的列，通常会假设坚持使用数字列，但该假设现在是一个弃用的特性，根据我收到的以下消息：
DataFrameGroupBy.sum中numeric_only的默认值已弃用。在将来的版本中，numeric_only将默认为False。请指定numeric_only或仅选择对该函数有效的列。df = df.groupby（df['MCF通道分组路径_1'].apply（sort_groupings））.sum（）.reset_index（）
您可能还想在最后一行的开头添加df =，如下所示，以保持结果为原始变量名。错过这一步导致我在学习调试时损失了大量时间。

import pandas as pd

df = pd.DataFrame([
['Direct, Organic Search',171],
['Direct, Organic Search, Not Found',4],
['Direct, Organic Search, Not Found, Referral',12],
['Direct, Organic Search, Not Found, Referral, Email',2],
['Organic Search, Direct',389],
['Organic Search, Direct, Not Found',3],
['Organic Search, Direct, Referral',76],
['Organic Search, Direct, Referral, Social Network',1]],
columns =['MCF Channel Grouping Path_1', 'Conversions'])

def sort_groupings(row_group):
    row_group = row_group.split(', ') # Converts string into list using ', ' as the separator       
    row_group.sort()                  # Sorts newly, made list in ascending order
    return tuple(row_group)           # Converts list to a tuple (required by the 'apply' function), and returns it

df = df.groupby(df['MCF Channel Grouping Path_1'].apply(sort_groupings)).sum(numeric_only=True).reset_index()

# groupby: column in which to group common values
# apply: call a function to run on values specified
# reset_index: reinstates the first column in the final df