一列列表上的 Pandas groupby

问题描述

我有一个 pandas 数据框，其中有一列包含 lists:

I have a pandas dataframe with a column that contains lists:

df = pd.DataFrame({'List': [['once', 'upon'], ['once', 'upon'], ['a', 'time'], ['there', 'was'], ['a', 'time']], 'Count': [2, 3, 4, 1, 2]})

Count   List
2    [once, upon]
3    [once, upon]
4    [a, time]
1    [there, was]
2    [a, time]

如何组合 List 列并对 Count 列求和?预期结果是:

How can I combine the List columns and sum the Count columns? The expected result is:

Count   List
5     [once, upon]
6     [a, time]
1     [there, was]

我试过了:

df.groupby('List')['Count'].sum()

导致:

TypeError: unhashable type: 'list'

推荐答案

一种方法是先转换为元组.这是因为 pandas.groupby 要求键是可散列的.元组是不可变和可散列的，但列表不是.

One way is to convert to tuples first. This is because pandas.groupby requires keys to be hashable. Tuples are immutable and hashable, but lists are not.

res = df.groupby(df['List'].map(tuple))['Count'].sum()

结果:

List
(a, time)       6
(once, upon)    5
(there, was)    1
Name: Count, dtype: int64

如果您需要数据帧中的列表形式的结果，您可以转换回来:

If you need the result as lists in a dataframe, you can convert back:

res = df.groupby(df['List'].map(tuple))['Count'].sum()
res['List'] = res['List'].map(list)

#            List  Count
# 0     [a, time]      6
# 1  [once, upon]      5
# 2  [there, was]      1

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