如何仅使用数据框操作获取特定的唯一组合？
我有一些数据，记录了两个玩家轮流玩游戏的情况。每当他们各自赢或输后，就会得到一个共享的分数（这里的逻辑不重要，数字都是随机的，这只是用来说明我想要的内容）。
所以，对于玩家P1和P2的每一种可能结果，都有对应的分数。
游戏的具体规则不重要，我只想知道是否可以用我最初的数据框，创建一个新的数据框，包含这4个玩家所有独特的组合。也就是说，计算这4个玩家一起玩时的所有可能组合的分数，假设他们的总分是相加的。
举个例子：
Player_1 Player_2 Player_3 Player_4 Outcome_1 Outcome_2 Outcome_3 Outcome_4 Score
P1 P2 P3 P4 win win win win 72
还有其他可能的独特组合。
关键是从P1和P2都赢的组合中得到30分，从P3和P4都赢的组合中得到42分，然后把这两个分数相加，得到如果这4个玩家都赢了的总分。
我可以通过生成独特组合等方法来做到这一点，但在实际应用中，如果参数更大，代码会变得很长，而且难以阅读。我想知道有没有办法只用一些操作，比如合并、分组、连接、聚合等，来实现这个目标。
import pandas as pd
data = {
"Player_1": ["P1", "P1", "P1", "P1", "P2", "P2", "P2", "P2", "P1", "P1", "P1", "P1", "P3", "P3", "P3", "P3"],
"Player_2": ["P2", "P2", "P2", "P2", "P3", "P3", "P3", "P3", "P4", "P4", "P4", "P4", "P4", "P4", "P4", "P4"],
"Outcome_1": ["win", "win", "lose", "lose", "win", "win", "lose", "lose", "win", "win", "lose", "lose", "win", "win", "lose", "lose"],
"Outcome_2": ["win", "lose", "win", "lose", "win", "lose", "win", "lose", "win", "lose", "win", "lose", "win", "lose", "win", "lose"],
"Score": [30, 45, 12, 78, 56, 21, 67, 90, 15, 32, 68, 88, 42, 74, 8, 93]
}
df = pd.DataFrame(data)
print(df)
Player_1 Player_2 Outcome_1 Outcome_2 Score
0 P1 P2 win win 30
1 P1 P2 win lose 45
2 P1 P2 lose win 12
3 P1 P2 lose lose 78
4 P2 P3 win win 56
5 P2 P3 win lose 21
6 P2 P3 lose win 67
7 P2 P3 lose lose 90
8 P1 P4 win win 15
9 P1 P4 win lose 32
10 P1 P4 lose win 68
11 P1 P4 lose lose 88
12 P3 P4 win win 42
13 P3 P4 win lose 74
14 P3 P4 lose win 8
15 P3 P4 lose lose 93
1 个回答
3
我希望我理解了你的问题。从评论来看，我猜测你的数据表里总共有4个玩家：
from itertools import product
p1, p2, p3, p4 = np.unique(df[["Player_1", "Player_2"]].values)
df = df.set_index(["Player_1", "Player_2", "Outcome_1", "Outcome_2"])
all_data = []
for p1o1, p2o2, p3o1, p4o2 in product(["win", "lose"], repeat=4):
all_data.append(
(
p1,
p2,
p3,
p4,
p1o1,
p2o2,
p3o1,
p4o2,
df.loc[(p1, p2, p1o1, p2o2), "Score"]
+ df.loc[(p3, p4, p3o1, p4o2), "Score"],
)
)
out = pd.DataFrame(
all_data,
columns=[
"Player_1",
"Player_2",
"Player_3",
"Player_4",
"Outcome_1",
"Outcome_2",
"Outcome_3",
"Outcome_4",
"Score",
],
)
打印结果：
Player_1 Player_2 Player_3 Player_4 Outcome_1 Outcome_2 Outcome_3 Outcome_4 Score
0 P1 P2 P3 P4 win win win win 72
1 P1 P2 P3 P4 win win win lose 104
2 P1 P2 P3 P4 win win lose win 38
3 P1 P2 P3 P4 win win lose lose 123
4 P1 P2 P3 P4 win lose win win 87
5 P1 P2 P3 P4 win lose win lose 119
6 P1 P2 P3 P4 win lose lose win 53
7 P1 P2 P3 P4 win lose lose lose 138
8 P1 P2 P3 P4 lose win win win 54
9 P1 P2 P3 P4 lose win win lose 86
10 P1 P2 P3 P4 lose win lose win 20
11 P1 P2 P3 P4 lose win lose lose 105
12 P1 P2 P3 P4 lose lose win win 120
13 P1 P2 P3 P4 lose lose win lose 152
14 P1 P2 P3 P4 lose lose lose win 86
15 P1 P2 P3 P4 lose lose lose lose 171