Converting Items From Pandas Series To Date Time

I have a Pandas Series ('timeSeries') that includes a time of day. Some of the items are blank, some are actual times (08:00; 13:00), some are indications of time (morning, early a

Solution 1:

Using the .dt accessor, you can set a timezone to your value, and than convert it to another one, using tz.localize and tz_convert.

import pandas as pd
import numpy as np

pd.options.display.max_columns = 5

df = pd.DataFrame({'TimeSeries': ["13:00", np.nan, "06:00", 'Morning', 'Afternoon', np.nan, np.nan, "01:30"]})

#   Convert your data to datetime, errors appears, but we do not care about them.
#   We also explicitly note that the datetime is a specific timezone.
df['TimeSeries_TZ'] = pd.to_datetime(df['TimeSeries'], errors='coerce', format='%H:%M')\
                     .dt.tz_localize('America/New_York')
print(df['TimeSeries_TZ'])
# 0   1900-01-01 13:00:00-04:56
# 1                         NaT
# 2   1900-01-01 06:00:00-04:56
# 3                         NaT
# 4                         NaT
# 5                         NaT
# 6                         NaT
# 7   1900-01-01 01:30:00-04:56

#   Then, we can use the datetime accessor to convert the timezone.
df['Converted_time'] = df['TimeSeries_TZ'].dt.tz_convert('Europe/London').dt.strftime('%H:%M')
print(df['Converted_time'])
# 0    17:55
# 1      NaT
# 2    10:55
# 3      NaT
# 4      NaT
# 5      NaT
# 6      NaT
# 7    06:25

#   If you want to convert the original result that CAN be converted, while keeping the values that
#   raised errors, you can copy the original data, and change the data that is not equal to the value
#   that means an error was raised, e.g : NaT (not a timestamp).
df['TimeSeries_result'] = df['TimeSeries'].copy()
df['TimeSeries_result'] = df['TimeSeries'].where(~df['Converted_time'].ne('NaT'), df['Converted_time'])


print(df[['TimeSeries', 'TimeSeries_result']])
#   TimeSeries TimeSeries_result
# 0      13:00             17:55
# 1        NaN               NaN
# 2      06:00             10:55
# 3    Morning           Morning
# 4  Afternoon         Afternoon
# 5        NaN               NaN
# 6        NaN               NaN
# 7      01:30             06:256          06:25             06:25