问题描述
我怀疑很多从事时间序列数据工作的人已经遇到过这个问题，而 Pandas 似乎没有提供一个简单的解决方案(还没有！):
I suspect many people working on timeseries data have already come across this issue, and pandas doesn't seem to provide a straightforward solution (yet!):
假设:
- 您有一个包含收盘价的每日数据时间序列，按日期(天)编制索引.
- 今天是 6 月 19 日.上次关闭数据值为 18JUN.
- 您想将每日数据重新采样到 OHLC 柱状图中，并以某个给定的频率(比如 M 或 2M)在 18JUN 结束.
所以对于 M freq，最后一个柱线是 19MAY-18JUN，前一个柱线是 19APR-18MAY，依此类推...
So for M freq, last bar would be 19MAY-18JUN, previous one 19APR-18MAY, and so on...
ts.resample('M', how='ohlc')
将进行重新采样，但M"是end_of_month"期间，因此结果将为 2014-05 提供整月和 2014-06 为 2 周的时间，因此您的最后一个栏不会是 '每月酒吧'.那不是我们想要的！
will do the resampling, but 'M' is 'end_of_month' period so the result will give a full month for 2014-05 and a 2-week period for 2014-06, so your last bar won't be a 'monthly bar'. That's not what we want!
使用 2M 频率，给定我的样本时间序列，我的测试给了我最后一个标记为 2014-07-31(之前标记为 2014-05-31)的条形，这是非常具有误导性的，因为有不是 7 月的数据.... 假设的最后 2 个月柱线再次仅涵盖最近 2 周.
With 2M frequency, given my sample timeseries, my test gives me last bar labelled as 2014-07-31 (and previous labelled as 2014-05-31), which is quite misleading since there's not data on JUL.... The supposed last 2-Month bar is again just covering the most recent 2 weeks.
正确的 DatetimeIndex 很容易创建:
The correct DatetimeIndex is easily created with:
pandas.date_range(end='2014-06-18', freq='2M', periods=300) + datetime.timedelta(days=18)
(Pandas 文档更喜欢通过
(Pandas documentation prefers to do the same thing via
pandas.date_range(end='2014-06-18', freq='2M', periods=300) + pandas.tseries.offsets.DateOffset(days=18)
但我的测试表明这种方法，虽然更多的 'pandaïc' 慢了 2 倍！)
but my tests shows that this method, though more 'pandaïc' is 2x slower!)
无论哪种方式，我们都无法将正确的 DatetimeIndex 应用于 ts.resample().
Either way we can't apply the right DatetimeIndex to ts.resample().
似乎熊猫开发团队(熊猫中的日期范围)是意识到这个问题，但与此同时，您如何解决它以在时间序列的最后一天获得滚动频率锚定的 OHLC?
It seems that pandas dev team (Date ranges in Pandas) is aware of this issue, but in the meantime, how could you solve it to get OHLC with rolling frequency anchored on the last day in the timeseries?
推荐答案
这基本上是从复制/粘贴中拼凑出来的，我肯定在某些情况下会失败 - 但下面是一些用于锚定的自定义 Offset 的起始代码到一个月中的某一天.
This is basically hacked together from copy/paste, and I'm sure fails on some cases - but below is some starting code for a custom Offset that is anchored to a particular day in a month.
from pandas.tseries.offsets import (as_datetime, as_timestamp, apply_nat,
DateOffset, relativedelta, datetime)
class MonthAnchor(DateOffset):
"""DateOffset Anchored to day in month
Arguments:
day_anchor: day to be anchored to
"""
def __init__(self, n=1, **kwds):
super(MonthAnchor, self).__init__(n)
self.kwds = kwds
self._dayanchor = self.kwds['day_anchor']
@apply_nat
def apply(self, other):
n = self.n
if other.day > self._dayanchor and n <= 0: # then roll forward if n<=0
n += 1
elif other.day < self._dayanchor and n > 0:
n -= 1
other = as_datetime(other) + relativedelta(months=n)
other = datetime(other.year, other.month, self._dayanchor)
return as_timestamp(other)
def onOffset(self, dt):
return dt.day == self._dayanchor
_prefix = ''
示例用法:
In [28]: df = pd.DataFrame(data=np.linspace(50, 100, 200), index=pd.date_range(end='2014-06-18', periods=200), columns=['value'])
In [29]: df.head()
Out[29]:
value
2013-12-01 50.000000
2013-12-02 50.251256
2013-12-03 50.502513
2013-12-04 50.753769
2013-12-05 51.005025
In [61]: month_offset = MonthAnchor(day_anchor = df.index[-1].day + 1)
In [62]: df.resample(month_offset, how='ohlc')
Out[62]:
value
open high low close
2013-11-19 50.000000 54.271357 50.000000 54.271357
2013-12-19 54.522613 62.060302 54.522613 62.060302
2014-01-19 62.311558 69.849246 62.311558 69.849246
2014-02-19 70.100503 76.884422 70.100503 76.884422
2014-03-19 77.135678 84.673367 77.135678 84.673367
2014-04-19 84.924623 92.211055 84.924623 92.211055
2014-05-19 92.462312 100.000000 92.462312 100.000000
这篇关于 pandas 时间序列重采样结束给定的一天的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！