Description
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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(optional) I have confirmed this bug exists on the master branch of pandas.
Code Sample, a copy-pastable example
import pandas as pd
i = pd.date_range('2020-03-28', periods=4, freq='D', tz='Europe/Berlin')
i # DatetimeIndex(['2020-03-28 00:00:00+01:00', '2020-03-29 00:00:00+01:00', '2020-03-30 00:00:00+02:00', '2020-03-31 00:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='D')
i[0] + i.freq == i[1] #True
i[2] + i.freq == i[3] #True
i[1] + i.freq == i[2] #False (!)Problem description
Variably-spaced timestamps are not handled well, if the variation is caused by DST. The result of i[1] + i.freq is
Timestamp('2020-03-30 01:00:00+0200', tz='Europe/Berlin', freq='D'), whereas
Timestamp('2020-03-30 00:00:00+0200', tz='Europe/Berlin', freq='D') was expected.
This is in contrast to timestamps where the variation is caused by e.g. months being different lengths, which are handled correctly.
Output of pd.show_versions()
INSTALLED VERSIONS
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None