How to convert UTC to EST with Python and take care of daylight saving automatically?

Question

If I have a bunch of data with date & time in UTC format, how can I convert them to EST.

It can determine when they will be -4(in summer) and -5(in winter) automatically every year? Thanks

Answer 1

You'll need to use the pytz module (available from PyPI):

import pytz
from datetime import datetime

est = pytz.timezone('US/Eastern')
utc = pytz.utc
fmt = '%Y-%m-%d %H:%M:%S %Z%z'

winter = datetime(2016, 1, 24, 18, 0, 0, tzinfo=utc)
summer = datetime(2016, 7, 24, 18, 0, 0, tzinfo=utc)

print(winter.strftime(fmt))
print(summer.strftime(fmt))

print(winter.astimezone(est).strftime(fmt))
print(summer.astimezone(est).strftime(fmt))

which will print:

2016-01-24 18:00:00 UTC+0000
2016-07-24 18:00:00 UTC+0000
2016-01-24 13:00:00 EST-0500
2016-07-24 14:00:00 EDT-0400

The reason why you'll need to use 'US/Eastern' and not 'EST' is exemplified in the last two lines of output.

Answer 2

If you have a pandas series with object datatype, you can first convert it into a DateTime series using pd.to_datetime()

df[col] = pd.to_datetime(your_series, format = '%Y-%m-%d %H:%M:%S', errors ='coerce')

Check if it is timezone aware or not by using series.dt.tz

df[col].dt.tz

If it's not timezone aware, we should make it timezone aware by using series.dt.tz_localize(). Also, do read about the ambiguous and nonexistent parameters of this function

df[col] = your_series[col].dt.tz_localize('UTC')

Now convert this series into the required timezone by series.dt.tz_convert()

df[col] = your_series[col].dt.tz_convert('US/Eastern')

The above method will take care of daylight savings time. If you want to check more timezones you can pip install pytz and

import pytz
pytz.common_timezones

Answer 3

In case you just want the normalized hour offset for your existing timedelta shifting:

from datetime import datetime
import pytz

def curr_est_offset():
    tz_est = pytz.timezone('US/Eastern')
    offset = tz_est.utcoffset(datetime.utcnow())
    offset_seconds = (offset.days * 86400) + offset.seconds
    offset_hours = offset_seconds // 3600

    return offset_hours # -4 or -5

Answer 4

As mentioned above, you can use pandas.DataFrame.tz_convert() like this:

import pandas as pd
from datetime import datetime

df = pd.read_csv("your_data_file_path.csv", index_col=False, engine='python')
df['Date'] = pd.to_datetime(df['Date'])
df['Date'] = df['Date'].dt.tz_localize('US/Eastern').dt.tz_convert('UTC')
df['Date'] = df['Date'].apply(lambda x: datetime.replace(x, tzinfo=None))

What the last row does is removing the timezone info from the datetime object, so you can operate with the date and time only (don't worry, that doesn't change the timezone again, it just strips it from the timestamp string).