![]() 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')) ![]() 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') Time_est = published_time.replace(tzinfo=est)ĭf = time_cst.strftime('%I:%M:%S %p %Z')ĭf = time_est.strftime('%I:%M:%S %p %Z')ĭf = time_mst.strftime('%I:%M:%S %p %Z')ĭf = time_utc.strftime('%I:%M:%S %p %Z') Coordinated Universal Time is 7 hours ahead of Mountain Standard Time. Time_mst = published_time.replace(tzinfo=mst) Time_cst = published_time.replace(tzinfo=cst) UTC to MST Myanmar in 24-hour time format. UTC to MST Myanmar Time Conversion Table UTC to MST Myanmar in 12-hour (AM/PM) time format. Mountain Standard Time (MST) is UTC-7:00, and Mountain. Myanmar Standard Time is 6 hours 30 minutes ahead from the UTC universal time. Time_utc = published_time.replace(tzinfo=utc) Coordinated Universal Time (UTC) is the primary time standard now, time zones around the world are expressed using offsets from UTC, UTC offset is the difference in hours and minutes from UTC, a time zone can be determined by adding or subtracting the number of UTC offset. ![]() 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
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |