I am trying to use combine_first to join two pandas series, so that one has precedence over the other. But it fails on the DST change day. I put together this demonstration:
import pandas as pd
import numpy as np
fr1 = pd.date_range(pd.to_datetime('2020-10-25').tz_localize('Europe/Berlin'), pd.to_datetime('2020-10-26').tz_localize('Europe/Berlin'), freq='H')
fr2 = fr1 + pd.DateOffset(hours=12)
d1 = pd.Series(data=np.random.randint(0,10, len(fr1)), index = fr1)
d2 = pd.Series(data=np.random.randint(0,10, len(fr2)), index = fr2)
d2.combine_first(d1)
output:
ValueError: cannot reindex from a duplicate axis
Why is this? How can this be fixed? I can of course manually slice the series and concatenate.
As you can see looking at the first items of your DateTimeIndex, the timezone as +xxxx changes at the moment of DST change:
>>> date_range = pd.date_range(pd.to_datetime('2020-10-25').tz_localize('Europe/Berlin'), pd.to_datetime('2020-10-26').tz_localize('Europe/Berlin'), freq='H')
>>> date_range[:4]
DatetimeIndex(['2020-10-25 00:00:00+02:00', '2020-10-25 01:00:00+02:00',
'2020-10-25 02:00:00+02:00', '2020-10-25 02:00:00+01:00'],
When you add the 12 hours, the dates change their timezones too:
DatetimeIndex(['2020-10-25 12:00:00+01:00', '2020-10-25 13:00:00+01:00',
'2020-10-25 14:00:00+01:00', '2020-10-25 14:00:00+01:00'],
dtype='datetime64[ns, Europe/Berlin]', freq=None)
However the difference in time is not 12 hours anymore, it is 11 due to the timezone change. That is because when adding DateOffset to a DatetimeIndex, you ask for a « apparent » change of 12 hours, as in « the 2 different clock readings are 12 hours apart ». This means that then 2 timestamps are mapped to the same timestamp that appears 12 hours later.
It is a specificity of DateOffset to allow to express non-constant deltas, i.e. if you add « 1 month », you would similarly not add the same number of seconds to a date depending which month you’re in.
If you want a shift in 12 actual hours, use Timedelta:
>>> (date_range[:4] + pd.Timedelta(hours=12))
DatetimeIndex(['2020-10-25 11:00:00+01:00', '2020-10-25 12:00:00+01:00',
'2020-10-25 13:00:00+01:00', '2020-10-25 14:00:00+01:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='H')
>>> (date_range + pd.Timedelta(hours=12)).is_unique
True
Now that the index is unique, combine_first will also work:
>>> d1 = pd.Series(data=np.random.randint(0, 10, 26), index=date_range)
>>> d2 = pd.Series(data=np.random.randint(0, 10, 26), index=date_range + pd.Timedelta(hours=12))
>>> d2.combine_first(d1)
2020-10-25 00:00:00+02:00 0.0
2020-10-25 01:00:00+02:00 1.0
2020-10-25 02:00:00+02:00 2.0
2020-10-25 02:00:00+01:00 1.0
2020-10-25 03:00:00+01:00 7.0
2020-10-25 04:00:00+01:00 4.0
2020-10-25 05:00:00+01:00 0.0
2020-10-25 06:00:00+01:00 1.0
2020-10-25 07:00:00+01:00 1.0
2020-10-25 08:00:00+01:00 1.0
2020-10-25 09:00:00+01:00 6.0
2020-10-25 10:00:00+01:00 7.0
2020-10-25 11:00:00+01:00 4.0
2020-10-25 12:00:00+01:00 8.0
2020-10-25 13:00:00+01:00 6.0
2020-10-25 14:00:00+01:00 0.0
2020-10-25 15:00:00+01:00 6.0
2020-10-25 16:00:00+01:00 0.0
2020-10-25 17:00:00+01:00 0.0
2020-10-25 18:00:00+01:00 9.0
2020-10-25 19:00:00+01:00 7.0
2020-10-25 20:00:00+01:00 9.0
2020-10-25 21:00:00+01:00 3.0
2020-10-25 22:00:00+01:00 4.0
2020-10-25 23:00:00+01:00 0.0
2020-10-26 00:00:00+01:00 6.0
2020-10-26 01:00:00+01:00 5.0
2020-10-26 02:00:00+01:00 9.0
2020-10-26 03:00:00+01:00 1.0
2020-10-26 04:00:00+01:00 4.0
2020-10-26 05:00:00+01:00 4.0
2020-10-26 06:00:00+01:00 3.0
2020-10-26 07:00:00+01:00 1.0
2020-10-26 08:00:00+01:00 8.0
2020-10-26 09:00:00+01:00 1.0
2020-10-26 10:00:00+01:00 6.0
2020-10-26 11:00:00+01:00 5.0
2020-10-26 12:00:00+01:00 6.0
Freq: H, dtype: float64