两柱的Frozenset联合

Question

我有一个包含两列的数据集。现在我想合并/接受这些冷冻设备的结合。我可以使用for循环来完成这个任务，但是我的数据集包含超过2700万行，所以我正在寻找一种避免for循环的方法。有人有什么想法吗？

数据

import pandas as pd
import numpy as np
d = {'ID1': [frozenset(['a', 'b']), frozenset(['a','c']), frozenset(['c','d'])],
    'ID2': [frozenset(['c', 'g']), frozenset(['i','f']), frozenset(['t','l'])]}
df = pd.DataFrame(data=d)

带循环的码

from functools import reduce
df['frozenset']=0
for i in range(len(df)):
    df['frozenset'].iloc[i] = reduce(frozenset.union, [df['ID1'][i],df['ID2'][i]])

期望输出

    ID1      ID2     frozenset
0   (a, b)  (c, g)  (a, c, g, b)
1   (a, c)  (f, i)  (a, c, f, i)
2   (c, d)  (t, l)  (c, d, t, l)

Answer 1

似乎不需要在这里使用functools.reduce。与每对冷冻设备直接结合就足够了。

如果您希望这类操作尽可能快，我建议查看列表理解(详细讨论请参阅For loops with pandas - When should I care? )。

df['union'] = [x | y for x, y in zip(df['ID1'], df['ID2'])]
df

      ID1     ID2         union
0  (a, b)  (c, g)  (c, a, b, g)
1  (c, a)  (f, i)  (c, a, i, f)
2  (c, d)  (l, t)  (c, l, d, t)

如果希望将其概括为多个列，则可以使用frozenset.union()合并它们。

df['union2'] = [frozenset.union(*X) for X in df[['ID1', 'ID2']].values]
df

      ID1     ID2         union        union2
0  (a, b)  (c, g)  (c, a, b, g)  (c, a, b, g)
1  (c, a)  (f, i)  (c, a, i, f)  (c, a, i, f)
2  (c, d)  (l, t)  (c, l, d, t)  (c, l, d, t)

Answer 2

你可以试试：

import pandas as pd
import numpy as np

d = {'ID1': [frozenset(['a', 'b']), frozenset(['a','c']), frozenset(['c','d'])],
    'ID2': [frozenset(['c', 'g']), frozenset(['i','f']), frozenset(['t','l'])]}
df = pd.DataFrame(data=d)
from functools import reduce
df['frozenset']=0

add = []
for i in range(len(df)):
    df['frozenset'].iloc[i] = reduce(frozenset.union, [df['ID1'][i],df['ID2'][i]])
add.append(df)
print(add)