In [1]:

    

import pandas as pd


    

In [2]:

    

s = '2018-01-01T12:00+09:00'
print(s)


    

    




2018-01-01T12:00+09:00

In [3]:

    

print(type(s))


    

    




<class 'str'>

In [4]:

    

ts = pd.to_datetime(s)
print(ts)


    

    




2018-01-01 12:00:00+09:00

In [5]:

    

print(type(ts))


    

    




<class 'pandas._libs.tslibs.timestamps.Timestamp'>

In [6]:

    

print(ts.tz)


    

    




pytz.FixedOffset(540)

In [7]:

    

ts_utc = pd.to_datetime(s, utc=True)
print(ts_utc)


    

    




2018-01-01 03:00:00+00:00

In [8]:

    

print(ts_utc.tz)


    

    




UTC

In [9]:

    

s_without_tz = '2018-01-01T12:00'


    

In [10]:

    

ts_naive = pd.to_datetime(s_without_tz)
print(ts_naive)


    

    




2018-01-01 12:00:00

In [11]:

    

print(ts_naive.tz)


    

    




None

In [12]:

    

ts_set_utc = pd.to_datetime(s_without_tz, utc=True)
print(ts_set_utc)


    

    




2018-01-01 12:00:00+00:00

In [13]:

    

print(ts_set_utc.tz)


    

    




UTC

In [14]:

    

print(ts_utc)


    

    




2018-01-01 03:00:00+00:00

In [15]:

    

print(ts_utc.tz)


    

    




UTC

In [16]:

    

ts_jst = ts_utc.tz_convert('Asia/Tokyo')
print(ts_jst)


    

    




2018-01-01 12:00:00+09:00

In [17]:

    

print(ts_jst.tz)


    

    




Asia/Tokyo

In [18]:

    

print(ts_utc.value)


    

    




1514775600000000000

In [19]:

    

print(ts_jst.value)


    

    




1514775600000000000

In [20]:

    

print(ts_utc == ts_jst)


    

    




True

In [21]:

    

ts_pst = ts_utc.tz_convert('US/Pacific')
print(ts_pst)


    

    




2017-12-31 19:00:00-08:00

In [22]:

    

print(ts_pst.tz)


    

    




US/Pacific

In [23]:

    

print(ts_utc.tz_convert('America/Los_Angeles'))


    

    




2017-12-31 19:00:00-08:00

In [24]:

    

print(ts_utc.tz_convert('America/Vancouver'))


    

    




2017-12-31 19:00:00-08:00

In [25]:

    

print(ts_naive)


    

    




2018-01-01 12:00:00

In [26]:

    

print(ts_naive.tz)


    

    




None

In [27]:

    

# print(ts_naive.tz_convert('Asia/Tokyo'))
# TypeError: Cannot convert tz-naive Timestamp, use tz_localize to localize


    

In [28]:

    

ts_jst_localize = ts_naive.tz_localize('Asia/Tokyo')
print(ts_jst_localize)


    

    




2018-01-01 12:00:00+09:00

In [29]:

    

print(ts_jst_localize.tz)


    

    




Asia/Tokyo

In [30]:

    

print(ts_naive.tz_localize('US/Pacific'))


    

    




2018-01-01 12:00:00-08:00

In [31]:

    

print(ts_naive.tz_localize('Asia/Tokyo') == ts_naive.tz_localize('US/Pacific'))


    

    




False

In [32]:

    

print(ts_jst)


    

    




2018-01-01 12:00:00+09:00

In [33]:

    

print(ts_jst.tz)


    

    




Asia/Tokyo

In [34]:

    

# print(ts_jst.tz_localize('US/Pacific'))
# TypeError: Cannot localize tz-aware Timestamp, use tz_convert for conversions


    

In [35]:

    

print(ts_jst)


    

    




2018-01-01 12:00:00+09:00

In [36]:

    

print(ts_jst.tz)


    

    




Asia/Tokyo

In [37]:

    

print(ts_jst.tz_convert(None))


    

    




2018-01-01 03:00:00

In [38]:

    

print(ts_jst.tz_localize(None))


    

    




2018-01-01 12:00:00

In [39]:

    

df = pd.DataFrame({'date': ['2018-01-01T12:00',
                            '2018-01-02T00:00',
                            '2018-01-03T10:00',
                            '2018-01-03T19:00'],
                   'value': ['A', 'B', 'C', 'D']})


    

In [40]:

    

print(df)


    

    




               date value
0  2018-01-01T12:00     A
1  2018-01-02T00:00     B
2  2018-01-03T10:00     C
3  2018-01-03T19:00     D

In [41]:

    

s_naive = pd.to_datetime(df['date'])
print(s_naive)


    

    




0   2018-01-01 12:00:00
1   2018-01-02 00:00:00
2   2018-01-03 10:00:00
3   2018-01-03 19:00:00
Name: date, dtype: datetime64[ns]

In [42]:

    

print(s_naive[0])


    

    




2018-01-01 12:00:00

In [43]:

    

print(type(s_naive[0]))


    

    




<class 'pandas._libs.tslibs.timestamps.Timestamp'>

In [44]:

    

print(s_naive[0].tz)


    

    




None

In [45]:

    

s_utc = pd.to_datetime(df['date'], utc=True)
print(s_utc)


    

    




0   2018-01-01 12:00:00+00:00
1   2018-01-02 00:00:00+00:00
2   2018-01-03 10:00:00+00:00
3   2018-01-03 19:00:00+00:00
Name: date, dtype: datetime64[ns, UTC]

In [46]:

    

print(s_utc[0].tz)


    

    




UTC

In [47]:

    

# print(s_naive.tz_localize('Asia/Tokyo'))
# TypeError: index is not a valid DatetimeIndex or PeriodIndex


    

In [48]:

    

# print(s_utc.tz_convert('Asia/Tokyo'))
# TypeError: index is not a valid DatetimeIndex or PeriodIndex


    

In [49]:

    

print(s_naive.dt.tz_localize('Asia/Tokyo'))


    

    




0   2018-01-01 12:00:00+09:00
1   2018-01-02 00:00:00+09:00
2   2018-01-03 10:00:00+09:00
3   2018-01-03 19:00:00+09:00
Name: date, dtype: datetime64[ns, Asia/Tokyo]

In [50]:

    

print(s_utc.dt.tz_convert('Asia/Tokyo'))


    

    




0   2018-01-01 21:00:00+09:00
1   2018-01-02 09:00:00+09:00
2   2018-01-03 19:00:00+09:00
3   2018-01-04 04:00:00+09:00
Name: date, dtype: datetime64[ns, Asia/Tokyo]

In [51]:

    

# print(s_naive.dt.tz_convert('Asia/Tokyo'))
# TypeError: Cannot convert tz-naive timestamps, use tz_localize to localize


    

In [52]:

    

# print(s_utc.dt.tz_localize('Asia/Tokyo'))
# TypeError: Already tz-aware, use tz_convert to convert.


    

In [53]:

    

# print(df['date'].dt.tz_localize('Asia/Tokyo'))
# AttributeError: Can only use .dt accessor with datetimelike values


    

In [54]:

    

df['date'] = pd.to_datetime(df['date'])
df_ts = df.set_index('date')
print(df_ts)


    

    




                    value
date                     
2018-01-01 12:00:00     A
2018-01-02 00:00:00     B
2018-01-03 10:00:00     C
2018-01-03 19:00:00     D

In [55]:

    

print(df_ts.index)


    

    




DatetimeIndex(['2018-01-01 12:00:00', '2018-01-02 00:00:00',
               '2018-01-03 10:00:00', '2018-01-03 19:00:00'],
              dtype='datetime64[ns]', name='date', freq=None)

In [56]:

    

print(type(df_ts.index))


    

    




<class 'pandas.core.indexes.datetimes.DatetimeIndex'>

In [57]:

    

print(df_ts['2018-01-03'])


    

    




                    value
date                     
2018-01-03 10:00:00     C
2018-01-03 19:00:00     D

In [58]:

    

print(df_ts.index.tz_localize('Asia/Tokyo'))


    

    




DatetimeIndex(['2018-01-01 12:00:00+09:00', '2018-01-02 00:00:00+09:00',
               '2018-01-03 10:00:00+09:00', '2018-01-03 19:00:00+09:00'],
              dtype='datetime64[ns, Asia/Tokyo]', name='date', freq=None)

In [59]:

    

print(df_ts.tz_localize('Asia/Tokyo'))


    

    




                          value
date                           
2018-01-01 12:00:00+09:00     A
2018-01-02 00:00:00+09:00     B
2018-01-03 10:00:00+09:00     C
2018-01-03 19:00:00+09:00     D

In [60]:

    

s_ts = df_ts['value']
print(s_ts)


    

    




date
2018-01-01 12:00:00    A
2018-01-02 00:00:00    B
2018-01-03 10:00:00    C
2018-01-03 19:00:00    D
Name: value, dtype: object

In [61]:

    

print(s_ts.tz_localize('Asia/Tokyo'))


    

    




date
2018-01-01 12:00:00+09:00    A
2018-01-02 00:00:00+09:00    B
2018-01-03 10:00:00+09:00    C
2018-01-03 19:00:00+09:00    D
Name: value, dtype: object

In [62]:

    

df = pd.DataFrame({'date': ['2018-01-01T12:00+09:00',
                            '2018-01-02T00:00+09:00',
                            '2018-01-03T10:00+09:00',
                            '2018-01-03T19:00+09:00'],
                   'value': ['A', 'B', 'C', 'D']})


    

In [63]:

    

print(df)


    

    




                     date value
0  2018-01-01T12:00+09:00     A
1  2018-01-02T00:00+09:00     B
2  2018-01-03T10:00+09:00     C
3  2018-01-03T19:00+09:00     D

In [64]:

    

print(pd.to_datetime(df['date']))


    

    




0   2018-01-01 12:00:00+09:00
1   2018-01-02 00:00:00+09:00
2   2018-01-03 10:00:00+09:00
3   2018-01-03 19:00:00+09:00
Name: date, dtype: datetime64[ns, pytz.FixedOffset(540)]

In [65]:

    

print(pd.to_datetime(df['date'], utc=True))


    

    




0   2018-01-01 03:00:00+00:00
1   2018-01-01 15:00:00+00:00
2   2018-01-03 01:00:00+00:00
3   2018-01-03 10:00:00+00:00
Name: date, dtype: datetime64[ns, UTC]

In [66]:

    

print(pd.to_datetime(df['date']).dt.tz_convert('US/Pacific'))


    

    




0   2017-12-31 19:00:00-08:00
1   2018-01-01 07:00:00-08:00
2   2018-01-02 17:00:00-08:00
3   2018-01-03 02:00:00-08:00
Name: date, dtype: datetime64[ns, US/Pacific]

In [67]:

    

df['date'] = pd.to_datetime(df['date'])
df_ts = df.set_index('date')
print(df_ts)


    

    




                          value
date                           
2018-01-01 12:00:00+09:00     A
2018-01-02 00:00:00+09:00     B
2018-01-03 10:00:00+09:00     C
2018-01-03 19:00:00+09:00     D

In [68]:

    

print(df_ts.index)


    

    




DatetimeIndex(['2018-01-01 12:00:00+09:00', '2018-01-02 00:00:00+09:00',
               '2018-01-03 10:00:00+09:00', '2018-01-03 19:00:00+09:00'],
              dtype='datetime64[ns, pytz.FixedOffset(540)]', name='date', freq=None)

In [69]:

    

print(df_ts.tz_convert('US/Pacific'))


    

    




                          value
date                           
2017-12-31 19:00:00-08:00     A
2018-01-01 07:00:00-08:00     B
2018-01-02 17:00:00-08:00     C
2018-01-03 02:00:00-08:00     D

In [70]:

    

df = pd.DataFrame({'date': ['2018-01-01T12:00+09:00',
                            '2018-01-02T00:00+09:00',
                            '2018-01-03T10:00-05:00',
                            '2018-01-03T19:00-08:00'],
                   'value': ['A', 'B', 'C', 'D']})


    

In [71]:

    

print(df)


    

    




                     date value
0  2018-01-01T12:00+09:00     A
1  2018-01-02T00:00+09:00     B
2  2018-01-03T10:00-05:00     C
3  2018-01-03T19:00-08:00     D

In [72]:

    

print(pd.to_datetime(df['date']))


    

    




0    2018-01-01 12:00:00+09:00
1    2018-01-02 00:00:00+09:00
2    2018-01-03 10:00:00-05:00
3    2018-01-03 19:00:00-08:00
Name: date, dtype: object

In [73]:

    

print(type(pd.to_datetime(df['date'])[0]))


    

    




<class 'datetime.datetime'>

In [74]:

    

print(pd.to_datetime(df['date'])[0].tzinfo)


    

    




tzoffset(None, 32400)

In [75]:

    

print(pd.to_datetime(df['date'])[2].tzinfo)


    

    




tzoffset(None, -18000)

In [76]:

    

print(pd.to_datetime(df['date'], utc=True))


    

    




0   2018-01-01 03:00:00+00:00
1   2018-01-01 15:00:00+00:00
2   2018-01-03 15:00:00+00:00
3   2018-01-04 03:00:00+00:00
Name: date, dtype: datetime64[ns, UTC]

In [77]:

    

print(type(pd.to_datetime(df['date'], utc=True)[0]))


    

    




<class 'pandas._libs.tslibs.timestamps.Timestamp'>

In [78]:

    

# print(pd.to_datetime(df['date']).dt.tz_convert('Asia/Tokyo'))
# ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True


    

In [79]:

    

df['date'] = pd.to_datetime(df['date'])
df_dt = df.set_index('date')
print(df_dt)


    

    




                          value
date                           
2018-01-01 12:00:00+09:00     A
2018-01-02 00:00:00+09:00     B
2018-01-03 10:00:00-05:00     C
2018-01-03 19:00:00-08:00     D

In [80]:

    

print(df_dt.index)


    

    




Index([2018-01-01 12:00:00+09:00, 2018-01-02 00:00:00+09:00,
       2018-01-03 10:00:00-05:00, 2018-01-03 19:00:00-08:00],
      dtype='object', name='date')

In [81]:

    

# print(df_dt.tz_convert('Asia/Tokyo'))
# TypeError: index is not a valid DatetimeIndex or PeriodIndex


    

In [82]:

    

# print(df_dt.tz_localize('Asia/Tokyo'))
# TypeError: index is not a valid DatetimeIndex or PeriodIndex