如果我的表格如下所示，如何创建另一个名为 vwap 的列来计算 vwap？
time bid_size bid ask ask_size trade trade_size phase
0 2019-01-07 07:45:01.064515 495 152.52 152.54 19 NaN NaN OPEN
1 2019-01-07 07:45:01.110072 31 152.53 152.54 19 NaN NaN OPEN
2 2019-01-07 07:45:01.116596 32 152.53 152.54 19 NaN NaN OPEN
3 2019-01-07 07:45:01.116860 32 152.53 152.54 21 NaN NaN OPEN
4 2019-01-07 07:45:01.116905 34 152.53 152.54 21 NaN NaN OPEN
5 2019-01-07 07:45:01.116982 34 152.53 152.54 31 NaN NaN OPEN
6 2019-01-07 07:45:01.147901 38 152.53 152.54 31 NaN NaN OPEN
7 2019-01-07 07:45:01.189971 38 152.53 152.54 31 ask 15.0 OPEN
8 2019-01-07 07:45:01.189971 38 152.53 152.54 16 NaN NaN OPEN
9 2019-01-07 07:45:01.190766 37 152.53 152.54 16 NaN NaN OPEN
10 2019-01-07 07:45:01.190856 37 152.53 152.54 15 NaN NaN OPEN
11 2019-01-07 07:45:01.190856 37 152.53 152.54 16 ask 1.0 OPEN
12 2019-01-07 07:45:01.193938 37 152.53 152.55 108 NaN NaN OPEN
13 2019-01-07 07:45:01.193938 37 152.53 152.54 15 ask 15.0 OPEN
14 2019-01-07 07:45:01.194326 2 152.54 152.55 108 NaN NaN OPEN
15 2019-01-07 07:45:01.194453 2 152.54 152.55 97 NaN NaN OPEN
16 2019-01-07 07:45:01.194479 6 152.54 152.55 97 NaN NaN OPEN
17 2019-01-07 07:45:01.194507 19 152.54 152.55 97 NaN NaN OPEN
18 2019-01-07 07:45:01.194532 19 152.54 152.55 77 NaN NaN OPEN
19 2019-01-07 07:45:01.194598 19 152.54 152.55 79 NaN NaN OPEN
抱歉，表格不清楚，但最右边的第二列是 trade_size，左边是 trade，显示交易的方向（买价或卖价）。如果 trade_size 和 trade 均为 NaN，则表示在该时间戳没有发生交易。
如果 df['trade'] == "ask"，则交易价格将是 'ask' 列中的价格，如果 df['trade] == "bid"，则交易价格将是 'bid' 列中的价格。由于有2个价格，请问如何计算vwap，df['vwap']？
我的想法是使用 np.cumsum()。
您可以使用np.where https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html为您提供正确列中的价格（bid or ask）取决于中的值trade柱子。请注意，这会在没有交易发生时为您提供买入价，但因为随后会乘以NaN交易规模并不重要。我还转发了 VWAP。
volume = df['trade_size']
price = np.where(df['trade'].eq('ask'), df['ask'], df['bid'])
df = df.assign(VWAP=((volume * price).cumsum() / vol.cumsum()).ffill())
>>> df
time bid_size bid ask ask_size trade trade_size phase VWAP
0 2019-01-07 07:45:01.064515 495 152.52 152.54 19 NaN NaN OPEN NaN
1 2019-01-07 07:45:01.110072 31 152.53 152.54 19 NaN NaN OPEN NaN
2 2019-01-07 07:45:01.116596 32 152.53 152.54 19 NaN NaN OPEN NaN
3 2019-01-07 07:45:01.116860 32 152.53 152.54 21 NaN NaN OPEN NaN
4 2019-01-07 07:45:01.116905 34 152.53 152.54 21 NaN NaN OPEN NaN
5 2019-01-07 07:45:01.116982 34 152.53 152.54 31 NaN NaN OPEN NaN
6 2019-01-07 07:45:01.147901 38 152.53 152.54 31 NaN NaN OPEN NaN
7 2019-01-07 07:45:01.189971 38 152.53 152.54 31 ask 15.0 OPEN 152.54
8 2019-01-07 07:45:01.189971 38 152.53 152.54 16 NaN NaN OPEN 152.54
9 2019-01-07 07:45:01.190766 37 152.53 152.54 16 NaN NaN OPEN 152.54
10 2019-01-07 07:45:01.190856 37 152.53 152.54 15 NaN NaN OPEN 152.54
11 2019-01-07 07:45:01.190856 37 152.53 152.54 16 ask 1.0 OPEN 152.54
12 2019-01-07 07:45:01.193938 37 152.53 152.55 108 NaN NaN OPEN 152.54
13 2019-01-07 07:45:01.193938 37 152.53 152.54 15 ask 15.0 OPEN 152.54
14 2019-01-07 07:45:01.194326 2 152.54 152.55 108 NaN NaN OPEN 152.54
15 2019-01-07 07:45:01.194453 2 152.54 152.55 97 NaN NaN OPEN 152.54
16 2019-01-07 07:45:01.194479 6 152.54 152.55 97 NaN NaN OPEN 152.54
17 2019-01-07 07:45:01.194507 19 152.54 152.55 97 NaN NaN OPEN 152.54
18 2019-01-07 07:45:01.194532 19 152.54 152.55 77 NaN NaN OPEN 152.54
19 2019-01-07 07:45:01.194598 19 152.54 152.55 79 NaN NaN OPEN 152.54
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