一只甜甜圈
这里的解决方案将根据每个玩家的级别返回交集。这还使用了defaultdict，因为这对于这种情况非常方便。我将解释内联代码from itertools import combinationsimport pandasfrom collections import defaultdictfrom pprint import pprint # only needed for pretty printing of dictionarydf = pandas.read_csv('df.csv', sep='\s+') # assuming the data frame is in a file df.csv# group by account_id to get subframes which only refer to one account.data_agg2 = df.groupby(['account_id'])# a defaultdict is a dictionary, where when no key is present, the function defined# is used to create the element. This eliminates the check, if a key is# already present or to set all combinations in advance.games_played_2 = defaultdict(int)# iterate over all accountsfor el in data_agg2.groups: # extract the sub-dataframe from the gouped function tmp = data_agg2.get_group(el) # print(tmp) # you can uncomment this to see each account # This is in principle the same loop as suggested before. However, as not every # player has played all variants, one only has to create the number of combinations # necessary for that player for i in range(len(tmp.loc[:, 'no_of_games'])): # As now the game_variant is a column and not the index, the first part of zip # is slightly adapted. This loops over all combinations of variants for the # current account. for comb, combsum in zip(combinations(tmp.loc[:, 'game_variant'], i+1), combinations(tmp.loc[:, 'no_of_games'].values, i+1)): # Here, each variant combination gets a unique key. Comb is sorted, as the # variants might be not in alphabetic order. The number of games played for # each variant for that player are added to the value of all players before. games_played_2['_'.join(sorted(comb))] += sum(combsum)pprint (games_played_2)# returns>> defaultdict(<class 'int'>, {'a': 5, 'a_b': 6, 'a_b_c': 7, 'a_c': 3, 'b': 9, 'b_c': 11, 'c': 4})由于您已经提取了它们的变体所玩的游戏数量，因此您可以简单地将它们相加。如果您想自动执行此操作，则可以itertools.combinations在循环中使用它，该循环会迭代所有可能的组合长度：from itertools import combinationsimport pandasimport numpy as npfrom pprint import pprint # only needed for pretty printing of dictionarydf = pandas.read_csv('df.csv', sep='\s+') # assuming the data frame is in a file df.csvdata_agg = df.groupby(['game_variant']).agg({'no_of_games':[np.sum]})games_played = {}for i in range(len(data_agg.loc[:, 'no_of_games'])): for comb, combsum in zip(combinations(data_agg.index, i+1), combinations(data_agg.loc[:, 'no_of_games'].values, i+1)): games_played['_'.join(comb)] = sum(combsum)pprint(games_played)返回：>> {'a': array([5], dtype=int64),>> 'a_b': array([14], dtype=int64),>> 'a_b_c': array([18], dtype=int64),>> 'a_c': array([9], dtype=int64),>> 'b': array([9], dtype=int64),>> 'b_c': array([13], dtype=int64),>> 'c': array([4], dtype=int64)}'combinations(sequence, number)'number返回中所有元素组合的迭代器sequence。因此，要获得所有可能的组合，您必须迭代所有numbersfrom1到len(sequence。这就是第一个 for 循环的作用。下一个for循环由两个迭代器组成：一个迭代器覆盖聚合数据的索引 ( combinations(data_agg.index, i+1))，一个迭代器覆盖每个变体中实际玩的游戏数量 ( combinations(data_agg.loc[:, 'no_of_games'].values, i+1))。因此comb应该始终是变体列表，并汇总每个变体所玩游戏数量的列表。这里请注意，要获取所有值，您必须使用.loc[:, 'no_games']，而不是.loc['no_games']，因为后者搜索名为 的索引'no_games'，而它是列名。然后，我将字典的键设置为变体列表的组合字符串，并将值设置为玩过的游戏数量的元素之和。