reset_index loses type information on time indices with time zone information

[ghost]

import pandas as pd

ts1 = pd.Timestamp('2015-07-01 10:00:00')
ts2 = pd.Timestamp('2015-07-01 11:00:00')

df1 = pd.DataFrame([[0.1],[0.2]], index=[ts1, ts2], columns=['a'])
print(df1.index.dtype)
df1 = df1.reset_index(drop=False)
print(df1['index'].dtype)

df2 = pd.DataFrame([[0.1],[0.2]], index=[ts1, ts2], columns=['a'])
df2.index = df2.index.tz_localize('Europe/London')
print(df2.index.dtype)
df2 = df2.reset_index(drop=False)
print(df2['index'].dtype)

gives

datetime64[ns]
datetime64[ns]
datetime64[ns]
object

While one would expect that the last two lines are identical.

[jorisvandenbossche]

This is because, at this moment, series cannot hold datetimes with timezone information in a proper numpy dtype. Therefore the object dtype is used and the values are stored as pd.Timestamp objects (which can hold timezones).
But this is being worked on (#10477), and the ability to store timezones info in series will be in pandas 0.17

[jorisvandenbossche]

You can also see what I described by looking at the difference of the .values for both:

In [16]: df1['index'].values
Out[16]:
array(['2015-07-01T12:00:00.000000000+0200',
       '2015-07-01T13:00:00.000000000+0200'], dtype='datetime64[ns]')

In [17]: df2['index'].values
Out[17]:
array([Timestamp('2015-07-01 10:00:00+0100', tz='Europe/London'),
       Timestamp('2015-07-01 11:00:00+0100', tz='Europe/London')], dtype=object)

[ghost]

Thanks. See related issue: #10763