我需要这样做：
74 buy 7395.41 0.000022 19.07.2018 16:00
75 sell 7380.11 0.011296 20.07.2018 06:45
76 buy 7503.56 0.011090 20.07.2018 14:30
77 sell 7272.64 0.011091 21.07.2018 02:30
78 buy 7363.81 0.010974 21.07.2018 14:00
79 sell 7450.58 0.010974 22.07.2018 13:45
80 buy 7497.86 0.010892 22.07.2018 17:30
81 sell 7405.44 0.010892 22.07.2018 23:45
82 buy 7605.28 0.010603 23.07.2018 02:30
83 sell 7742.51 0.001060 23.07.2018 18:23
84 sell 7743.68 0.001060 23.07.2018 18:24
85 sell 7748.71 0.001060 23.07.2018 18:24
86 sell 7751.81 0.001060 23.07.2018 18:25
87 sell 7755.07 0.001060 23.07.2018 18:25
88 sell 7758.39 0.001060 23.07.2018 18:26
89 sell 7745.54 0.001060 23.07.2018 18:26
90 sell 7748.11 0.001060 23.07.2018 18:27
91 sell 7751.05 0.001060 23.07.2018 18:27
92 sell 7748.11 0.001060 23.07.2018 18:28
93 buy 7774.71 0.001058 23.07.2018 18:32
94 buy 7779.51 0.001058 23.07.2018 18:33
95 buy 7769.85 0.001058 23.07.2018 18:33
96 buy 7748.35 0.001058 23.07.2018 18:34
97 buy 7730.00 0.001058 23.07.2018 18:34
98 buy 7649.00 0.001058 23.07.2018 18:35
99 buy 7644.99 0.001058 23.07.2018 18:35
100 buy 7696.30 0.001058 23.07.2018 18:36
101 buy 7695.00 0.001058 23.07.2018 18:36
102 buy 7702.84 0.001058 23.07.2018 18:37
此：
74 buy 7395.41 0.000022 19.07.2018 16:00
75 sell 7380.11 0.011296 20.07.2018 06:45
76 buy 7503.56 0.011090 20.07.2018 14:30
77 sell 7272.64 0.011091 21.07.2018 02:30
78 buy 7363.81 0.010974 21.07.2018 14:00
79 sell 7450.58 0.010974 22.07.2018 13:45
80 buy 7497.86 0.010892 22.07.2018 17:30
81 sell 7405.44 0.010892 22.07.2018 23:45
82 buy 7605.28 0.010603 23.07.2018 02:30
83 sell 7749.29 0.010600 23.07.2018 18:23
93 buy 7719.05 0.010580 23.07.2018 18:32
我需要通过按数量加权平均价格（列）和按数量总和的边栏（买\卖）交织来分组。 A可以通过并列显示全部内容。
答案 0 :(得分：1)
>>> df
col_1 col_2 col_3 col_4 col_5 col_6
0 74 buy 7395.41 0.000022 19.07.2018 16:00
1 75 sell 7380.11 0.011296 20.07.2018 6:45
2 76 buy 7503.56 0.011090 20.07.2018 14:30
3 77 sell 7272.64 0.011091 21.07.2018 2:30
4 78 buy 7363.81 0.010974 21.07.2018 14:00
5 79 sell 7450.58 0.010974 22.07.2018 13:45
6 80 buy 7497.86 0.010892 22.07.2018 17:30
7 81 sell 7405.44 0.010892 22.07.2018 23:45
8 82 buy 7605.28 0.010603 23.07.2018 2:30
9 83 sell 7742.51 0.001060 23.07.2018 18:23
10 84 sell 7743.68 0.001060 23.07.2018 18:24
11 85 sell 7748.71 0.001060 23.07.2018 18:24
12 86 sell 7751.81 0.001060 23.07.2018 18:25
13 87 sell 7755.07 0.001060 23.07.2018 18:25
14 88 sell 7758.39 0.001060 23.07.2018 18:26
15 89 sell 7745.54 0.001060 23.07.2018 18:26
16 90 sell 7748.11 0.001060 23.07.2018 18:27
17 91 sell 7751.05 0.001060 23.07.2018 18:27
18 92 sell 7748.11 0.001060 23.07.2018 18:28
19 93 buy 7774.71 0.001058 23.07.2018 18:32
20 94 buy 7779.51 0.001058 23.07.2018 18:33
21 95 buy 7769.85 0.001058 23.07.2018 18:33
22 96 buy 7748.35 0.001058 23.07.2018 18:34
23 97 buy 7730.00 0.001058 23.07.2018 18:34
24 98 buy 7649.00 0.001058 23.07.2018 18:35
25 99 buy 7644.99 0.001058 23.07.2018 18:35
26 100 buy 7696.30 0.001058 23.07.2018 18:36
27 101 buy 7695.00 0.001058 23.07.2018 18:36
28 102 buy 7702.84 0.001058 23.07.2018 18:37
您可以使用
shift从自身上方的行中获取一行的值并将其存储为col_2b：
>>> df['col_2b'] = df['col_2'].shift(1)
>>> df
col_1 col_2 col_3 col_4 col_5 col_6 col_2b
0 74 buy 7395.41 0.000022 19.07.2018 16:00 NaN
1 75 sell 7380.11 0.011296 20.07.2018 6:45 buy
2 76 buy 7503.56 0.011090 20.07.2018 14:30 sell
3 77 sell 7272.64 0.011091 21.07.2018 2:30 buy
4 78 buy 7363.81 0.010974 21.07.2018 14:00 sell
5 79 sell 7450.58 0.010974 22.07.2018 13:45 buy
6 80 buy 7497.86 0.010892 22.07.2018 17:30 sell
7 81 sell 7405.44 0.010892 22.07.2018 23:45 buy
8 82 buy 7605.28 0.010603 23.07.2018 2:30 sell
9 83 sell 7742.51 0.001060 23.07.2018 18:23 buy
10 84 sell 7743.68 0.001060 23.07.2018 18:24 sell
11 85 sell 7748.71 0.001060 23.07.2018 18:24 sell
12 86 sell 7751.81 0.001060 23.07.2018 18:25 sell
13 87 sell 7755.07 0.001060 23.07.2018 18:25 sell
14 88 sell 7758.39 0.001060 23.07.2018 18:26 sell
15 89 sell 7745.54 0.001060 23.07.2018 18:26 sell
16 90 sell 7748.11 0.001060 23.07.2018 18:27 sell
17 91 sell 7751.05 0.001060 23.07.2018 18:27 sell
18 92 sell 7748.11 0.001060 23.07.2018 18:28 sell
19 93 buy 7774.71 0.001058 23.07.2018 18:32 sell
20 94 buy 7779.51 0.001058 23.07.2018 18:33 buy
21 95 buy 7769.85 0.001058 23.07.2018 18:33 buy
22 96 buy 7748.35 0.001058 23.07.2018 18:34 buy
23 97 buy 7730.00 0.001058 23.07.2018 18:34 buy
24 98 buy 7649.00 0.001058 23.07.2018 18:35 buy
25 99 buy 7644.99 0.001058 23.07.2018 18:35 buy
26 100 buy 7696.30 0.001058 23.07.2018 18:36 buy
27 101 buy 7695.00 0.001058 23.07.2018 18:36 buy
28 102 buy 7702.84 0.001058 23.07.2018 18:37 buy
然后，只需过滤
col_2和col_2b中具有不同值的行：
>>> df = df[df['col_2b'] != df['col_2']]
>>> df
col_1 col_2 col_3 col_4 col_5 col_6 col_2b
0 74 buy 7395.41 0.000022 19.07.2018 16:00 NaN
1 75 sell 7380.11 0.011296 20.07.2018 6:45 buy
2 76 buy 7503.56 0.011090 20.07.2018 14:30 sell
3 77 sell 7272.64 0.011091 21.07.2018 2:30 buy
4 78 buy 7363.81 0.010974 21.07.2018 14:00 sell
5 79 sell 7450.58 0.010974 22.07.2018 13:45 buy
6 80 buy 7497.86 0.010892 22.07.2018 17:30 sell
7 81 sell 7405.44 0.010892 22.07.2018 23:45 buy
8 82 buy 7605.28 0.010603 23.07.2018 2:30 sell
9 83 sell 7742.51 0.001060 23.07.2018 18:23 buy
19 93 buy 7774.71 0.001058 23.07.2018 18:32 sell
最后，删除
col_2b：
>>> del df['col_2b']
>>> df
col_1 col_2 col_3 col_4 col_5 col_6
0 74 buy 7395.41 0.000022 19.07.2018 16:00
1 75 sell 7380.11 0.011296 20.07.2018 6:45
2 76 buy 7503.56 0.011090 20.07.2018 14:30
3 77 sell 7272.64 0.011091 21.07.2018 2:30
4 78 buy 7363.81 0.010974 21.07.2018 14:00
5 79 sell 7450.58 0.010974 22.07.2018 13:45
6 80 buy 7497.86 0.010892 22.07.2018 17:30
7 81 sell 7405.44 0.010892 22.07.2018 23:45
8 82 buy 7605.28 0.010603 23.07.2018 2:30
9 83 sell 7742.51 0.001060 23.07.2018 18:23
19 93 buy 7774.71 0.001058 23.07.2018 18:32
希望这会有所帮助！