Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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.
[x] I have checked that this issue has not already been reported.
[x] I have confirmed this bug exists on the latest version of pandas.
[ ] (optional) I have confirmed this bug exists on the master branch of pandas.
Code Sample, a copy-pastable example
Problem description
Variably-spaced timestamps are not handled well, if the variation is caused by DST. The result of
i[1] + i.freq
isTimestamp('2020-03-30 01:00:00+0200', tz='Europe/Berlin', freq='D')
, whereasTimestamp('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()