Python - pandas datetime column with multiple timezones

Question

I have a data frame with multiple users and timezones, like such:

cols = [\'user\', \'zone_name\', \'utc_datetime\']
data = [
    [1, \'Europe/Amsterdam\', pd.to         


        

Answer 1

I think you need Series.dt.tz_convert in lambda function:

df['local_datetime'] = (pd.to_datetime(df.groupby('zone_name')['utc_datetime']
    .transform(lambda x: x.dt.tz_localize('UTC').dt.tz_convert(x.name))
    .astype(str).str[:-6]))

print(df)
   user         zone_name        utc_datetime      local_datetime
0     1  Europe/Amsterdam 2019-11-13 11:14:15 2019-11-13 12:14:15
1     2     Europe/London 2019-11-13 11:14:15 2019-11-13 11:14:15

Answer 2

Relatively shorter answer using DataFrame.apply:

df['local_datetime'] = df.apply(lambda x: x.utc_datetime.tz_localize(tz = "UTC").tz_convert(x.zone_name), axis = 1)
print(df)
   user         zone_name        utc_datetime             local_datetime
0     1  Europe/Amsterdam 2019-11-13 11:14:15  2019-11-13 12:14:15+01:00
1     2     Europe/London 2019-11-13 11:14:15  2019-11-13 11:14:15+00:00

If you want to remove the time zone information, you can localize times by passing None

df['local_datetime'] = df.apply(lambda x: x.utc_datetime.tz_localize(tz = "UTC").tz_convert(x.zone_name).tz_localize(None), axis = 1)
print(df)
   user         zone_name        utc_datetime      local_datetime
0     1  Europe/Amsterdam 2019-11-13 11:14:15 2019-11-13 12:14:15
1     2     Europe/London 2019-11-13 11:14:15 2019-11-13 11:14:15