超出范围的数据下降时间值:寻求效率提升

Question

需要排除9:30-16:00之外带有Time的行(包括9:30和16:00)。

            Symbol                Time    Open    High     Low   Close  Volume  LOD  Sessions
0         AEHR 2021-08-11 04:33:00    6.52    6.52    6.52    6.52     200  NaN       NaN
1         AEHR 2021-08-11 05:43:00    6.46    6.46    6.46    6.46     100  NaN       NaN
2         AEHR 2021-08-11 07:53:00    6.34    6.34    6.34    6.34     300  NaN       NaN
3         AEHR 2021-08-11 08:02:00    6.06    6.08    6.06    6.08     402  NaN       NaN
4         AEHR 2021-08-11 08:05:00    6.15    6.15    6.15    6.15     100  NaN       NaN
...        ...                 ...     ...     ...     ...     ...     ...  ...       ...

输出：

        Symbol                Time   Open   High    Low  Close  Volume  LOD  Sessions
0         AEHR 2019-09-23 09:31:00   1.42   1.42   1.42   1.42     200  NaN       NaN
1         AEHR 2019-09-23 09:43:00   1.35   1.35   1.34   1.34    6062  NaN       NaN
2         AEHR 2019-09-23 09:58:00   1.35   1.35   1.29   1.30    8665  NaN       NaN
3         AEHR 2019-09-23 09:59:00   1.32   1.32   1.32   1.32     100  NaN       NaN
4         AEHR 2019-09-23 10:00:00   1.35   1.35   1.35   1.35     400  NaN       NaN
...        ...                 ...    ...    ...    ...    ...     ...  ...       ...

解决方案可以工作，但非常慢吗：

df['time'] = pd.to_datetime(df['Time']).dt.time

#Drop non-market time data (i.e outside 9:30-4:00)
df['after_open'] = df['time'] >= datetime.time(9, 30)
df['before_close'] = df['time'] <= datetime.time(16, 00)
filt = (df['after_open'] == False)
df = df.drop(index=df[filt].index)
filt2 = (df['before_close'] == False)
df = df.drop(index=df[filt2].index)

Answer 1

试试这两条线：

df['Time'] = pd.to_datetime(df['Time'])
df = df.loc[df['Time'].ge("09:30:00") & df['Time'].le("16:00:00")]

print(df)