ENH: Allow to reset tz from DatetimeIndex holding time representation

There looks no easy way to remove tz from tz-aware DatetimeIndex holding the timestamp represented. What required is inverse operation of tz_localize.

idx = pd.date_range('2014-01-01 09:00', '2014-01-01 20:00', freq='H', tz='Asia/Tokyo')
idx
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01 09:00:00+09:00, ..., 2014-01-01 20:00:00+09:00]
# Length: 12, Freq: H, Timezone: Asia/Tokyo

# What I want to do is:
idx.tz_reset()
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01 09:00:00, ..., 2014-01-01 20:00:00]
# Length: 12, Freq: H, Timezone: None

# This raises TypeError
idx.tz_localize(None)
# TypeError: Already tz-aware, use tz_convert to convert.

# This change the timestamp to UTC
idx.tz_convert(None)
# <class 'pandas.tseries.index.DatetimeIndex'>
# [2014-01-01 00:00:00, ..., 2014-01-01 11:00:00]
# Length: 12, Freq: H, Timezone: None

In my case, there are some globally distributed data sources which holds local timezones. And want to merge them and analyze based on local subjective times (business hours, etc).

[sinhrks]

Hmm, both tz_localize and tz_convert “converts” something, the actual behavior should be considered.

• tz_localize “converts” timezone holding time representation (holding subjective time and change absolute time).
• tz_convert “converts” timezone holding UTC time (holding absolute time and change subjective time).

The required operation is “holding subjective time and reset timezone”, thus I feel tz_localize(None) is more natural. If we use tz_convert(None) for this operation, tz_convert has inconsistency which sometimes convert subjective time, otherwise convert absolute time.

[sinhrks]

Thanks, looks more clear. Docs in #7852 uses US timezone and different hours from UTC offset, there should be no problem.