在Pandas DataFrame中跨多个列的映射方法 [英] Mapping methods across multiple columns in a Pandas DataFrame

问题描述

我有一个Pandas数据框，其中的值是列表:

I have a Pandas dataframe where the values are lists:

import pandas as pd

DF = pd.DataFrame({'X':[[1, 5], [1, 2]], 'Y':[[1, 2, 5], [1, 3, 5]]})
DF
         X          Y
0   [1, 5]  [1, 2, 5]
1   [1, 2]  [1, 3, 5]

我想检查X中的列表是否是Y中列表的子集.对于单个列表，我们可以使用set(x).issubset(set(y))进行.但是，我们如何在Pandas数据列中做到这一点?

I want to check if the lists in X are subsets of the lists in Y. With individual lists, we can do this using set(x).issubset(set(y)). But how would we do this across Pandas data columns?

到目前为止，我唯一想到的就是使用单个列表作为解决方法，然后将结果转换回Pandas.这个任务似乎有点复杂:

So far, the only thing I've come up with is to use the individual lists as a workaround, then convert the result back to Pandas. Seems a bit complicated for this task:

foo = [set(DF['X'][i]).issubset(set(DF['Y'][i])) for i in range(len(DF['X']))]

foo = pd.DataFrame(foo)
foo.columns = ['x_sub_y']
pd.merge(DF, foo, how = 'inner', left_index = True, right_index = True)

         X          Y   x_sub_y
0   [1, 5]  [1, 2, 5]   True
1   [1, 2]  [1, 3, 5]   False

有没有更简单的方法来实现这一目标?可能使用.map或.apply吗?

Is there a easier way to achieve this? Possibly using .map or .apply?

推荐答案

使用set和issubset:

DF.assign(x_sub_y = DF.apply(lambda x: set(x.X).issubset(set(x.Y)), axis=1))

输出:

        X          Y  x_sub_y
0  [1, 5]  [1, 2, 5]     True
1  [1, 2]  [1, 3, 5]    False

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