Python pandas，如何截断 DatetimeIndex 并仅在特定时间间隔内填充缺失的数据

Question

 2012-10-08 07:12:22            0.0    0          0  2315.6    0     0.0    0
 2012-10-08 09:14:00         2306.4   20  326586240  2306.4  472  2306.8    4
 2012-10-08 09:15:00         2306.8   34  249805440  2306.8  361  2308.0   26
 2012-10-08 09:15:01         2308.0    1   53309040  2307.4   77  2308.6    9
 2012-10-08 09:15:01.500000  2308.2    1  124630140  2307.0  180  2308.4    1
 2012-10-08 09:15:02         2307.0    5   85846260  2308.2  124  2308.0    9
 2012-10-08 09:15:02.500000  2307.0    3  128073540  2307.0  185  2307.6   11
 ......
 2012-10-09 07:19:30            0.0    0          0  2276.6    0     0.0    0
 2012-10-09 09:14:00         2283.2   80   98634240  2283.2  144  2283.4    1
 2012-10-09 09:15:00         2285.2   18  126814260  2285.2  185  2285.6    3
 2012-10-09 09:15:01         2285.8    6   98719560  2286.8  144  2287.0   25
 2012-10-09 09:15:01.500000  2287.0   36  144759420  2288.8  211  2289.0    4
 2012-10-09 09:15:02         2287.4    6  109829280  2287.4  160  2288.6    5
 ......

我有一个数据帧包含上述几天的交易所交易数据。我想要的数据来自9:00:00AM - 11:30:00AM和13:00:00 - 15:15:00，所以我想做两件事，

1. 对于数据帧中的每个日期，截断以仅包含数据9:00:00AM - 11:30:00AM和13:00:00 - 15:15:00范围
2. 范围为 1.，以 500 milliseconds 的频率填充缺失数据

熊猫截断函数只允许我根据日期截断，但我想根据 datetime.time 在这里截断。还有如何仅在我感兴趣的间隔内填充缺失的数据。

多谢。

Answer 1

1. 对于数据帧中的每个日期，截断以仅包含上午 9：00：00 - 上午 11：30：00 和 13：00：00 - 15：15：00 范围内的数据

为此使用索引切片，例如：

df = df[start_timestamp:end_timestamp]

1. 范围为 1.，以 500 毫秒的频率填充缺失数据

生成索引为 500 毫秒的新数据帧。 使用外部联接将此数据帧与原始数据帧合并。这将为你获取一个定期包含行的数据帧。缺失观测值的行将包含 NaN 值。然后用 fillna 填充缺失的 NaN 值。

例：

In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: data = pd.DataFrame({"value": np.arange(5)}, index=pd.date_range("2013/02/03", periods=5, freq="3Min"))
In [4]: data
Out[4]: 
                     value
2013-02-03 00:00:00      0
2013-02-03 00:03:00      1
2013-02-03 00:06:00      2
2013-02-03 00:09:00      3
2013-02-03 00:12:00      4
In [5]: filler = pd.DataFrame({"value": [100] * 15}, index=pd.date_range("2013/02/03", periods=15, freq="1Min"))                                                                           
In [6]: filler
Out[6]: 
                     value
2013-02-03 00:00:00    100
2013-02-03 00:01:00    100
2013-02-03 00:02:00    100
2013-02-03 00:03:00    100
2013-02-03 00:04:00    100
2013-02-03 00:05:00    100
2013-02-03 00:06:00    100
2013-02-03 00:07:00    100
2013-02-03 00:08:00    100
2013-02-03 00:09:00    100
2013-02-03 00:10:00    100
2013-02-03 00:11:00    100
2013-02-03 00:12:00    100
2013-02-03 00:13:00    100
2013-02-03 00:14:00    100
In [7]: merged = filler.merge(data, how='left', left_index=True, right_index=True)                                                                                                         
In [8]: merged["value"] = np.where(np.isfinite(merged.value_y), merged.value_y, merged.value_x)                                                                                            
In [9]: merged
Out[9]: 
                     value_x  value_y  value
2013-02-03 00:00:00      100        0      0
2013-02-03 00:01:00      100      NaN    100
2013-02-03 00:02:00      100      NaN    100
2013-02-03 00:03:00      100        1      1
2013-02-03 00:04:00      100      NaN    100
2013-02-03 00:05:00      100      NaN    100
2013-02-03 00:06:00      100        2      2
2013-02-03 00:07:00      100      NaN    100
2013-02-03 00:08:00      100      NaN    100
2013-02-03 00:09:00      100        3      3
2013-02-03 00:10:00      100      NaN    100
2013-02-03 00:11:00      100      NaN    100
2013-02-03 00:12:00      100        4      4
2013-02-03 00:13:00      100      NaN    100
2013-02-03 00:14:00      100      NaN    100
In [10]: merged['2013-02-03 00:01:00':'2013-02-03 00:10:00']                                                                                                                                
Out[10]: 
                     value_x  value_y  value
2013-02-03 00:01:00      100      NaN    100
2013-02-03 00:02:00      100      NaN    100
2013-02-03 00:03:00      100        1      1
2013-02-03 00:04:00      100      NaN    100
2013-02-03 00:05:00      100      NaN    100
2013-02-03 00:06:00      100        2      2
2013-02-03 00:07:00      100      NaN    100
2013-02-03 00:08:00      100      NaN    100
2013-02-03 00:09:00      100        3      3
2013-02-03 00:10:00      100      NaN    100