如何在索引中没有的日期对 pandas 时间序列进行切片？

Question

I have a time series indexed by datetime.date. Here are the first knots of the series:

1999-12-31  0
2000-06-30  170382.118454
2000-12-29  -319260.443362

I want to slice from the beginning of the series until Dec 28th 2000, but this doesn't work since that date is not in the index (I get a KeyError when I try original_series[:datetime.date(2000,12,28)] . I've also tried converting the index to timestamps, but that gives very spurious results (it manufactures fake knots, see below), so I wondered if there's a good approach to this problem.

test = pd.Series(original_series.values, map(pd.Timestamp, original_series.index))

At a first glance, this looks alright:

1999-12-31         0.000000
2000-06-30    170382.118454
2000-12-29   -319260.443362

But then I try to do my slicing (where do those extra days in January 2000 come from?):

In [84]: test[:'2000-12-28']
Out[84]: 
1999-12-31         0.000000
2000-06-30    170382.118454
2000-01-03    -71073.979016
2000-01-04    100498.744748
2000-01-05     91104.743684
2000-01-06     82290.255459

Answer 1

You can simply do, if ts is your time.serie :

In [77]: ts = pd.Series([99,65],index=pd.to_datetime(['2000-12-24','2000-12-30']))

In [78]: ts
Out[78]:
2000-12-24    99
2000-12-30    65
dtype: int64

In [79]: ts[ts.index<=pd.to_datetime('2000-12-28')]
Out[79]:
2000-12-24    99
dtype: int64

If you have index as string just proceed with:

ts[ts.index.map(pd.to_datetime)<=pd.to_datetime('2000-12-28')]

Answer 2

There is a simple way to do it without converting it into a time-series object.

df["date"] = pd.to_datetime(df["date"])