Question

我正在玩大熊猫中的一些金融时间序列数据，我正在尝试对某些时间戳数据进行重新采样。这是起始数据：

start_data

Out[12]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 749880 entries, 2012-07-06 03:00:00 to 2013-09-11 23:59:00
Data columns (total 1 columns):
TickMean    749880  non-null values
dtypes: float64(1)

start_data.TickMean
Out[18]: 
2012-07-06 03:00:00    1.541194
2012-07-06 03:01:00    1.541216
2012-07-06 03:02:00    1.541201
2012-07-06 03:03:00    1.541088
2012-07-06 03:04:00    1.540999
2012-07-06 03:05:00    1.541011
2012-07-06 03:06:00    1.541090
2012-07-06 03:07:00    1.541256
2012-07-06 03:08:00    1.541341
2012-07-06 03:09:00    1.541386
2012-07-06 03:10:00    1.541511
2012-07-06 03:11:00    1.541469
2012-07-06 03:12:00    1.541506
2012-07-06 03:13:00    1.541584
2012-07-06 03:14:00    1.541453
...
2013-09-11 23:45:00    1.602015
2013-09-11 23:46:00    1.602015
2013-09-11 23:47:00    1.602015
2013-09-11 23:48:00    1.602015
2013-09-11 23:49:00    1.602015
2013-09-11 23:50:00    1.602015
2013-09-11 23:51:00    1.602015
2013-09-11 23:52:00    1.602015
2013-09-11 23:53:00    1.602015
2013-09-11 23:54:00    1.602015
2013-09-11 23:55:00    1.602015
2013-09-11 23:56:00    1.602015
2013-09-11 23:57:00    1.602015
2013-09-11 23:58:00    1.602015
2013-09-11 23:59:00    1.602015
Name: TickMean, Length: 749880, dtype: float64

当我尝试40分钟的重采样时，时间范围会扩大：

start_data = start_data.resample('40min')

start_data
Out[14]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 25344 entries, 2012-01-07 00:00:00 to 2013-12-10 23:20:00
Freq: 40T
Data columns (total 1 columns):
TickMean    18749  non-null values
dtypes: float64(1)

start_data.TickMean

Out[15]: 
2012-01-07 00:00:00    1.5706
2012-01-07 00:40:00    1.5706
2012-01-07 01:20:00    1.5706
2012-01-07 02:00:00    1.5706
2012-01-07 02:40:00    1.5706
2012-01-07 03:20:00    1.5706
2012-01-07 04:00:00    1.5706
2012-01-07 04:40:00    1.5706
2012-01-07 05:20:00    1.5706
2012-01-07 06:00:00    1.5706
2012-01-07 06:40:00    1.5706
2012-01-07 07:20:00    1.5706
2012-01-07 08:00:00    1.5706
2012-01-07 08:40:00    1.5706
2012-01-07 09:20:00    1.5706
...
2013-12-10 14:00:00    1.594563
2013-12-10 14:40:00    1.594796
2013-12-10 15:20:00    1.594766
2013-12-10 16:00:00    1.593523
2013-12-10 16:40:00    1.593171
2013-12-10 17:20:00    1.593702
2013-12-10 18:00:00    1.595145
2013-12-10 18:40:00    1.595796
2013-12-10 19:20:00    1.595527
2013-12-10 20:00:00    1.595099
2013-12-10 20:40:00    1.595060
2013-12-10 21:20:00    1.595575
2013-12-10 22:00:00    1.595575
2013-12-10 22:40:00    1.595575
2013-12-10 23:20:00    1.595575
Freq: 40T, Name: TickMean, Length: 25344, dtype: float64

我觉得我错过了一些明显的东西。为什么要这样做？

快速编辑：我知道40分钟的频率很奇怪，但其他频率也有同样的效果。

编辑2：是的，这是愚蠢的事。我以为索引会被排序。

start_data

Out[23]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 749880 entries, 2012-07-06 03:00:00 to 2013-09-11 23:59:00
Data columns (total 1 columns):
TickMean    749880  non-null values
dtypes: float64(1)

start_data.index.min()
Out[24]: Timestamp('2012-01-07 00:00:00', tz=None)

start_data.index.max()
Out[25]: Timestamp('2013-12-10 23:59:00', tz=None)

编辑3：作为遇到类似奇怪问题的人的奖励，我的日期数据是第一天而不是第一个月。所以也把一切都扔了。这是使用dayfirst = True选项解决的。

ask_data.index = pd.to_datetime(ask_data.index, dayfirst=True)

ask_data
Out[34]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 749880 entries, 2012-06-07 03:00:00 to 2013-11-09 23:59:00
Data columns (total 5 columns):
Open      749880  non-null values
High      749880  non-null values
Low       749880  non-null values
Close     749880  non-null values
Volume    749880  non-null values
dtypes: float64(5)

ask_data.index.min()
Out[35]: Timestamp('2012-06-07 03:00:00', tz=None)

ask_data.index.max()
Out[36]: Timestamp('2013-11-09 23:59:00', tz=None)

Answer 1

您确定您的索引有序吗？你可以通过以下方式检查：

print start_data.index.min(), start_data.index.max(), start_data.index.is_monotonic

Python pandas时间序列resample函数扩展时间索引

1 个答案: