python-datetime

Given the string in this format "HH:MM" , for example "03:55" , that represents 3 hours and 55 minutes . I want to convert it to datetime.time object for easier manipulation. What would be the easiest way to do that? Use datetime.datetime.strptime() and call .time() on the result: >>> datetime.datetime.strptime('03:55', '%H:%M').time() datetime.time(3, 55) The first argument to .strptime() is the string to parse, the second is the expected format. >>> datetime.time(*map(int, '03:55'.split(':'))) datetime.time(3, 55) 来源： https://stackoverflow.com/questions/14295673/convert-string-into-datetime

I am trying to convert string date to timestamp in Python as described in the post here . When I run the code examples in the post, I encounter an error. For e.g.: >>> import time >>> import datetime >>> s = "01/12/2011" >>> time.mktime(datetime.datetime.strptime(s, "%d/%m/%Y").timetuple()) Traceback (most recent call last): File "<pyshell#31>", line 1, in <module> time.mktime(datetime.datetime.strptime(s, "%d/%m/%Y").timetuple()) File "C:\Python34\lib\_strptime.py", line 15, in <module> import calendar File "C:\Python34\calendar.py", line 9, in <module> calendar = wckCalendar.Calendar(root,

I have a dataframe. A column contains timestamps. I would like to remove dates and convert the time to seconds. First I converted them to datetime: In: df_time = pd.to_datetime(df["Timestamp"]) Out: 0 2017-11-07 13:09:00 1 2017-11-07 13:11:00 2 2017-11-07 13:13:00 3 2017-11-07 13:15:00 dtype: datetime64[ns] Then I removed dates: In: df_time = pd.Series([val.time() for val in df_time]) Out: 0 13:09:00 1 13:11:00 2 13:13:00 3 13:15:00 4 13:17:00 dtype: object But they became objects and I did not managed to convert them to datetime-like objects to convert them into seconds. I know there are some

What would be the best way to see if the current time lies between say 10:30 AM and 4:30 PM . I could think of the following, not sure how correct: from datetime import datetime nw = datetime.now() hrs = nw.hour;mins = nw.minute;secs = nw.second; zero = timedelta(seconds = secs+mins*60+hrs*3600) st = nw - zero # this take me to 0 hours. time1 = st + timedelta(seconds=10*3600+30*60) # this gives 10:30 AM time2 = st + timedelta(seconds=16*3600+30*60) # this gives 4:30 PM if nw>= time1 or nw <= time2: print "yes, within the interval" Please let me know if this the correct approach, can something

See the following code: import datetime import pytz fmt = '%Y-%m-%d %H:%M:%S %Z' d = datetime.datetime.now(pytz.timezone("America/New_York")) d_string = d.strftime(fmt) d2 = datetime.datetime.strptime(d_string, fmt) print d_string print d2.strftime(fmt) the output is 2013-02-07 17:42:31 EST 2013-02-07 17:42:31 The timezone information simply got lost in the translation. If I switch '%Z' to '%z', I get ValueError: 'z' is a bad directive in format '%Y-%m-%d %H:%M:%S %z' I know I can use python-dateutil , but I just found it bizzare that I can't achieve this simple feature in datetime and have to

I have a .csv file in such format timestmp, p 2014/12/31 00:31:01:9200, 0.7 2014/12/31 00:31:12:1700, 1.9 ... and when read via pd.read_csv and convert the time str to datetime using pd.to_datetime , the performance drops dramatically. Here is a minimal example. import re import pandas as pd d = '2014-12-12 01:02:03.0030' c = re.sub('-', '/', d) %timeit pd.to_datetime(d) %timeit pd.to_datetime(c) %timeit pd.to_datetime(c, format="%Y/%m/%d %H:%M:%S.%f") and the performances are: 10000 loops, best of 3: 62.4 µs per loop 10000 loops, best of 3: 181 µs per loop 10000 loops, best of 3: 82.9 µs per