检查一个数据框列是否是另一列的子集

Question

我有一个包含列 Enrolled_Months 和 Eligible_Months 的数据框，描述如下：

month_list1 = [
    [(1, 2018), (2, 2018), (3, 2019)],
    [(7, 2018), (8, 2018), (10, 2018)],
    [(4, 2018), (5, 2018), (7, 2018)],
    [(1, 2019), (2, 2019), (4, 2019)]
]

month_list2 = [
    [(2, 2018), (3, 2019)],
    [(7, 2018), (8, 2018)],
    [(2, 2018), (3, 2019)],
    [(10, 2018), (11, 2019)]
]

EID = [1, 2, 3, 4]

df = pd.DataFrame({
    'EID': EID,
    'Enrolled_Months': month_list1,
    'Eligible_Months': month_list2
})
df

Out[6]: 
   EID                     Enrolled_Months           Eligible_Months
0    1   [(1, 2018), (2, 2018), (3, 2019)]    [(2, 2018), (3, 2019)]
1    2  [(7, 2018), (8, 2018), (10, 2018)]    [(7, 2018), (8, 2018)]
2    3   [(4, 2018), (5, 2018), (7, 2018)]    [(2, 2018), (3, 2019)]
3    4   [(1, 2019), (2, 2019), (4, 2019)]  [(10, 2018), (11, 2019)]

我想创建一个名为 Check 的新列，如果 Enrolled_Months 包含 Eligible_Months 的所有元素，则该列为真。我想要的输出如下：

Out[8]: 
   EID                     Enrolled_Months           Eligible_Months  Check
0    1   [(1, 2018), (2, 2018), (3, 2019)]    [(2, 2018), (3, 2019)]   True
1    2  [(7, 2018), (8, 2018), (10, 2018)]    [(7, 2018), (8, 2018)]   True
2    3   [(4, 2018), (5, 2018), (7, 2018)]    [(2, 2018), (3, 2019)]  False
3    4   [(1, 2019), (2, 2019), (4, 2019)]  [(10, 2018), (11, 2019)]  False

我试过以下方法：

df['Check'] = set(df['Eligible_Months']).issubset(df['Enrolled_Months'])

但最终得到错误 TypeError: unhashable type: 'list'。

有什么想法可以实现吗？

旁注：Enrolled_Months 数据最初采用非常不同的格式，每个月都有自己的二进制列，并且有一个单独的 Year 列指定年份（imo 设计真的很糟糕） .我创建了列表列，因为我认为它更容易使用，但如果原始格式更适合我想要实现的目标，请告诉我。

Answer 1

您可以使用一些 explodes 然后 eval 和 any:

df['Check'] = df.explode('Eligible_Months').explode('Enrolled_Months').eval('Enrolled_Months == Eligible_Months').groupby(level=0).any()

输出：

>>> df
   EID                     Enrolled_Months           Eligible_Months  Check
0    1   [(1, 2018), (2, 2018), (3, 2019)]    [(2, 2018), (3, 2019)]   True
1    2  [(7, 2018), (8, 2018), (10, 2018)]    [(7, 2018), (8, 2018)]   True
2    3   [(4, 2018), (5, 2018), (7, 2018)]    [(2, 2018), (3, 2019)]  False
3    4   [(1, 2019), (2, 2019), (4, 2019)]  [(10, 2018), (11, 2019)]  False

Answer 2

您可以使用 df.apply() 创建新列：

df['Check'] = df.apply(
    lambda row: set(row['Eligible_Months']).issubset(row['Enrolled_Months']), axis=1
)

这输出：

   EID                     Enrolled_Months           Eligible_Months  Check
0    1   [(1, 2018), (2, 2018), (3, 2019)]    [(2, 2018), (3, 2019)]   True
1    2  [(7, 2018), (8, 2018), (10, 2018)]    [(7, 2018), (8, 2018)]   True
2    3   [(4, 2018), (5, 2018), (7, 2018)]    [(2, 2018), (3, 2019)]  False
3    4   [(1, 2019), (2, 2019), (4, 2019)]  [(10, 2018), (11, 2019)]  False

Answer 3

列表理解工作正常：

df.assign(check = [set(l).issuperset(r) 
                   for l, r in 
                   zip(df.Enrolled_Months, df.Eligible_Months)])

   EID                     Enrolled_Months           Eligible_Months  check
0    1   [(1, 2018), (2, 2018), (3, 2019)]    [(2, 2018), (3, 2019)]   True
1    2  [(7, 2018), (8, 2018), (10, 2018)]    [(7, 2018), (8, 2018)]   True
2    3   [(4, 2018), (5, 2018), (7, 2018)]    [(2, 2018), (3, 2019)]  False
3    4   [(1, 2019), (2, 2019), (4, 2019)]  [(10, 2018), (11, 2019)]  False

检查一个数据框列是否是另一列的子集

Checking if one dataframe column is a subset of another column

python

tuples

list

dataframe

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