![]() Then again I tried the following but got an error that said, 'time_stamp' does not match format '%a, %d %b %Y %H:%M:%S %Z' published_time = df.apply(lambda x: dt. So, I changed it using lambda but got an error saying, " NameError: name 'time_stamp' is not defined" published_time = time_stamp.apply(lambda x: dt.strptime(df.time_stamp, '%a, %d %b %Y %H:%M:%S %Z')) Specifically, MDT is 6 hours behind the Coordinated Universal Time (UTC). Initially I received an error for the following that said, "TypeError: strptime() argument 1 must be str, not Series": published_time = datetime.strptime(time_stamp, '%a, %d %b %Y %H:%M:%S %Z') The main difference between these two time zones is that they differ in one hour. Time_mst = published_time.replace(tzinfo=mst) If you are in London, the most convenient time to accommodate all parties is between 5:00 pm and 6:00 pm for a conference call or meeting. Another way is to use the datetimeoffset to add the offset to UTC and store it in a datetimeoffset column. Next way is to convert the given local time to UTC time and then store it in a date time column. Time zone Currently Mountain Daylight Time (MDT), UTC -6 Standard time (Mountain Standard Time (MST), UTC -7) starts Nov. Time_cst = published_time.replace(tzinfo=cst) One way is to get the UTC time directly and store it in a date-time datatype column. Mountain Standard Time is seven hours behind the Coordinated Universal Time standard, written as an offset of UTC - 7:00. Time_utc = published_time.replace(tzinfo=utc) ![]() Published_time = time_stamp.apply(lambda x: dt.strptime(df.time_stamp, '%a, %d %b %Y %H:%M:%S %Z')) My default time is UTC, but I would like to create multiple columns based of the start_time_UTC to create cst, mst, and est. ![]() I have a data set like the following: start_time_UTC
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