Pandas `period_range` gives strange results

Question

I want a pandas period range with 25 hours offset, and I saw there are two ways to do this (see here):

The first way is to use freq=25H, which I tried, and gave me the right answer:

import pandas as pd
pd.period_range(start='2016-01-01 10:00', freq = '25H', periods = 10)

and the result is

PeriodIndex(['2016-01-01 10:00', '2016-01-02 11:00', '2016-01-03 12:00',
             '2016-01-04 13:00', '2016-01-05 14:00', '2016-01-06 15:00',
             '2016-01-07 16:00', '2016-01-08 17:00', '2016-01-09 18:00',
             '2016-01-10 19:00'],
            dtype='int64', freq='25H')   

The second way, using freq=1D1H, however, gave me a rather strange result:

pd.period_range(start='2016-01-01 10:00', freq = '1D1H', periods = 10)

and I got

 PeriodIndex(['1971-12-02 01:00', '1971-12-02 02:00', '1971-12-02 03:00',
              '1971-12-02 04:00', '1971-12-02 05:00', '1971-12-02 06:00',
              '1971-12-02 07:00', '1971-12-02 08:00', '1971-12-02 09:00',
              '1971-12-02 10:00'],
            dtype='int64', freq='25H')

So maybe 1D1H is not a valid way to specify frequency? how did 1971 come up? (I also tried to use use 1D1H as frequency for the date_range() method, which did yield the right answer.)

pd.date_range('2016-01-01 10:00', freq = '1D1H', periods = 10)
DatetimeIndex(['2016-01-01 10:00:00', '2016-01-02 11:00:00',
               '2016-01-03 12:00:00', '2016-01-04 13:00:00',
               '2016-01-05 14:00:00', '2016-01-06 15:00:00',
               '2016-01-07 16:00:00', '2016-01-08 17:00:00',
               '2016-01-09 18:00:00', '2016-01-10 19:00:00'],
              dtype='datetime64[ns]', freq='25H')

EDIT: it appears that with period_range(), though freq=1D1H doesn't work, freq=1H1D does. The reason is still unknown.

EDIT2: this has been identified as a bug, see the answer below.

Answer 1

The bug has already been identified and reported on GitHub.

EDIT: A fix has been merged and will be included in v0.19.