我有一个非常大的数据集，它具有以下索引和列标题。
+------+------------------------+------------------------------+--------------------------+------------------------------------+-------------------------------------+------------------------+--------------------------+--------------------------------+----------------------------+--------------------------------------+---------------------------------------+--------------------------+
| | count: interaction_eis | count: interaction_eis_reply | count: interaction_match | count: interaction_single_message_ | count: interaction_single_message_1 | count: interaction_yes | dc(uid): interaction_eis | dc(uid): interaction_eis_reply | dc(uid): interaction_match | dc(uid): interaction_single_message_ | dc(uid): interaction_single_message_1 | dc(uid): interaction_yes |
+------+------------------------+------------------------------+--------------------------+------------------------------------+-------------------------------------+------------------------+--------------------------+--------------------------------+----------------------------+--------------------------------------+---------------------------------------+--------------------------+
| uid | | | | | | | | | | | | |
| 38 | 36 | 0 | 0 | 14 | 0 | 163 | 1 | 0 | 0 | 1 | 0 | 1 |
| 66 | 63 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 1466 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1709 | 51 | 0 | 0 | 1 | 0 | 9 | 1 | 0 | 0 | 1 | 0 | 1 |
| 1844 | 66 | 0 | 1 | 3 | 1 | 17 | 1 | 0 | 1 | 1 | 1 | 1 |
+------+------------------------+------------------------------+--------------------------+------------------------------------+-------------------------------------+------------------------+--------------------------+--------------------------------+----------------------------+--------------------------------------+---------------------------------------+--------------------------+
我正在尝试按收到的交互类型对UID进行分组，如果用户只有一种特定类型的交互，那么它们只会与只有该特定交互类型的其他用户进行分组。
为了做到这一点，我首先采用了每个交互类型只有1“点击”的所有dc（uid）列，如果交互类型从未发生过，则为0并将它们聚合到组中像这样一行一行：
cols = [i for i in list(all_f_rm.columns) if i[0]=="d"]
def aggregate(row):
key = ""
for i in cols:
key+=str(row[i])
if key not in results:
results[key] = []
results[key].append(row.name)
results = {}
all_f_rm.apply(aggregate, axis=1)
results.keys()是所有潜在的交互类型组合（其中35个），每个键的值是属于该组合的每个索引（UID）。它看起来像这样：
{'001101': [141168, 153845, 172598, 254401, 448276,...
接下来，我做了一个函数来过滤掉每个组合/键的所有不匹配的行：
def tableFor(key):
return all_f_rm[all_f_rm.apply(lambda row: row.name in results[key], axis=1)]
tableFor('001101')显示我想要的确切数据框。
我的问题是，我写了一个列表理解来循环遍历所有35个组合，比如这个[tableFor(x) for x in results.keys()]，但它需要永远（1小时以上但还没有完成）我需要在另外5个上执行此操作数据集。有没有更有效的方法来完成我想要做的事情？
答案 0 :(得分：1)
IIUC，你可以做你想要的groupby。构建像你这样的玩具数据框：
df = pd.DataFrame({"uid": np.arange(10**6)})
for col in range(6):
df["dc{}".format(col)] = np.random.randint(0,2,len(df))
我们可以按感兴趣的列进行分组，并快速获取相关的ID号：
>>> dcs = [col for col in df.columns if col.startswith("dc")]
>>> df.groupby(dcs)["uid"].unique()
dc0 dc1 dc2 dc3 dc4 dc5
0 0 0 0 0 0 [302, 357, 383, 474, 526, 614, 802, 812, 865, ...
1 [7, 96, 190, 220, 405, 453, 534, 598, 606, 866...
1 0 [16, 209, 289, 355, 430, 620, 634, 736, 780, 7...
1 [9, 79, 166, 268, 408, 434, 435, 447, 572, 749...
1 0 0 [60, 120, 196, 222, 238, 346, 426, 486, 536, 5...
1 [2, 53, 228, 264, 315, 517, 557, 621, 626, 630...
1 0 [42, 124, 287, 292, 300, 338, 341, 350, 500, 5...
1 [33, 95, 140, 192, 225, 282, 328, 339, 365, 44...
1 0 0 0 [1, 59, 108, 134, 506, 551, 781, 823, 836, 861...
1 [149, 215, 380, 394, 436, 482, 570, 600, 631, ...
1 0 [77, 133, 247, 333, 374, 782, 809, 892, 1096, ...
1 [14, 275, 312, 326, 343, 444, 569, 692, 770, 7...
1 0 0 [69, 104, 143, 404, 431, 468, 636, 639, 657, 7...
1 [178, 224, 367, 402, 664, 666, 739, 807, 871, ...
[...]
如果您更喜欢关联的群组，您也可以从中获取列表或词典，而不是简单地删除索引：
>>> groups = list(df.groupby(dcs, as_index=False))
>>> print(groups[0][0])
(0, 0, 0, 0, 0, 0)
>>> print(groups[0][1])
uid dc0 dc1 dc2 dc3 dc4 dc5
302 302 0 0 0 0 0 0
357 357 0 0 0 0 0 0
383 383 0 0 0 0 0 0
[...]
999730 999730 0 0 0 0 0 0
999945 999945 0 0 0 0 0 0
999971 999971 0 0 0 0 0 0
[15357 rows x 7 columns]
等等。