如何避免使用for循环为大熊猫数据框创建条件新列？

Question

我有以下熊猫df，我想创建另一列df ['trade_percentage']，如果df ['trade'] ==“ ask”，则df ['trade_percentage'] = df ['trade_size '] / df ['ask_size']否则，如果df ['trade'] ==“ bid”，则df ['trade_percentage'] = df ['trade_size'] / df ['bid_size']。 忽略NaN。 我写了这些代码：

但是，我的数据框中有100万行，它已经运行了几个小时，并且仍在运行。 请问是否有其他方法可以简化而不是使用循环？ 谢谢！

for i in range(len(df)):
  if (df['trade'][i] == "ask"):
    df['trade_percentage'][i] = df['trade_size'][i]/df['ask_size'][i]
  elif (df['trade'][i] == "bid"):
    df['trade_percentage'][i] = df['trade_size'][i]/df['bid_size'][i]  
df.head()

             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   

Answer 1

我正在使用np.select

s1=df['trade'] == "ask"
s2=df['trade'] == "bid"
np.select([s1,s2],[df['trade_size']/df['ask_size'],df['trade_size']/df['bid_size']],default =np.nan)