Python pandas，如何截断DatetimeIndex并仅在特定间隔内填充缺失数据

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 DataFrame，其中包含如上所述的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，所以我会喜欢做两件事，

1. 对于 DataFrame 中的每个日期，截断为仅包含9:00:00AM - 11:30:00AM 和 13:00:00 - 15:15:00
范围
2. 使用1.中的范围，以500毫秒的频率填充缺失数据

pandas 截断函数只允许我根据日期截断，但我想在这里根据 datetime.time 截断。还有如何仅在我感兴趣的时间间隔内填充缺失的数据。

非常感谢。

Answer 1

1. for each date in the DataFrame truncate to only have data in the range of 9:00:00AM - 11:30:00AM and 13:00:00 - 15:15:00

使用索引slicing为此，例如:

df = df[start_timestamp:end_timestamp]

1. with the range in 1., fill missing data with a frequency of 500 milliseconds

在 500 毫秒时生成一个带有索引的新数据帧。 Merge该数据框与使用外连接的原始数据框。这将为您提供一个包含定期行的数据框。缺失观测值的行将包含 NaN 值。然后用 fillna 填充缺失的 NaN 值.

示例:

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