Creating pandas DatetimeIndex in Dataframe from DST aware datetime objects

From an online API I gather a series of data points, each with a value and an ISO timestamp. Unfortunately I need to loop over them, so I store them in a temporary dict and then create a pandas dataframe from that and set the index to the timestamp column (simplified example):

from datetime import datetime
import pandas


input_data = [
    '2019-09-16T06:44:01+02:00',
    '2019-11-11T09:13:01+01:00',
]

data = []
for timestamp in input_data:
    _date = datetime.fromisoformat(timestamp)

    data.append({'time': _date})

pd_data = pandas.DataFrame(data).set_index('time')

As long as all timestamps are in the same timezone and DST/non-DST everything works fine, and, I get a Dataframe with a DatetimeIndex which I can work on later. However, once two different time-offsets appear in one dataset (above example), I only get an Index, in my dataframe, which does not support any time-based methods.

Is there any way to make pandas accept timezone-aware, differing date as index?

• A pandas datetime column also requires the offset to be the same. A column with different offsets, will not be converted to a datetime dtype.
• I suggest, do not convert the data to a datetime until it's in pandas.
• Separate the time offset, and treat it as a timedelta
• to_timedelta requires a format of 'hh:mm:ss' so add ':00' to the end of the offset
• See Pandas: Time deltas for all the available timedelta operations
• pandas.Series.dt.tz_convert
• pandas.Series.tz_localize
• Convert to a specific TZ with:
  • If a datetime is not datetime64[ns, UTC] dtype, then first use .dt.tz_localize('UTC') before .dt.tz_convert('US/Pacific')
  • Otherwise df.datetime_utc.dt.tz_convert('US/Pacific')

import pandas as pd

# sample data
input_data = ['2019-09-16T06:44:01+02:00', '2019-11-11T09:13:01+01:00']

# dataframe
df = pd.DataFrame(input_data, columns=['datetime'])

# separate the offset from the datetime and convert it to a timedelta
df['offset'] = pd.to_timedelta(df.datetime.str[-6:] + ':00')

# if desired, create a str with the separated datetime
# converting this to a datetime will lead to AmbiguousTimeError because of overlapping datetimes at 2AM, per the OP
df['datetime_str'] = df.datetime.str[:-6]

# convert the datetime column to a datetime format without the offset
df['datetime_utc'] = pd.to_datetime(df.datetime, utc=True)

# display(df)
                    datetime          offset        datetime_str              datetime_utc
0  2019-09-16T06:44:01+02:00 0 days 02:00:00 2019-09-16 06:44:01 2019-09-16 04:44:01+00:00
1  2019-11-11T09:13:01+01:00 0 days 01:00:00 2019-11-11 09:13:01 2019-11-11 08:13:01+00:00

print(df.info())
[out]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype              
---  ------        --------------  -----              
 0   datetime      2 non-null      object             
 1   offset        2 non-null      timedelta64[ns]    
 2   datetime_str  2 non-null      object             
 3   datetime_utc  2 non-null      datetime64[ns, UTC]
dtypes: datetime64[ns, UTC](1), object(2), timedelta64[ns](1)
memory usage: 192.0+ bytes

# convert to local timezone
df.datetime_utc.dt.tz_convert('US/Pacific')

[out]:
0   2019-09-15 21:44:01-07:00
1   2019-11-11 00:13:01-08:00
Name: datetime_utc, dtype: datetime64[ns, US/Pacific]

Other Resources

• Calculate Pandas DataFrame Time Difference Between Two Columns in Hours and Minutes.
• Talk Python to Me: Episode #271: Unlock the mysteries of time, Python's datetime that is!
• Real Python: Using Python datetime to Work With Dates and Times
• The dateutil module provides powerful extensions to the standard datetime module.