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Convert Strings to DateTime?


Convert Strings to DateTime?

By : user2950661
Date : November 17 2020, 11:55 AM
will help you The datatype I need is DataTime? The sample string values I have are like 11/28/2014 1:40:42 AM What format should I use to convert them to DateTime? I found the DateTime.ParseExact method but still not sure what to enter as the format based on the shape of the input that I have. , You can use the invariant culture to parse this format:
code :
DateTime? result = DateTime.Parse("11/28/2014 1:40:42 AM", CultureInfo.InvariantCulture);
DateTime result;
if (!DateTime.TryParse("11/28/2014 1:40:42 AM",
                        CultureInfo.InvariantCulture,
                        DateTimeStyles.None,
                        out result))
{
    // Handle error
}


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C# Convert strings to DateTime

C# Convert strings to DateTime


By : Colby
Date : March 29 2020, 07:55 AM
it should still fix some issue DateTime.Parse(d + " " + t) should do it, the problem you were probably having is the lack of space inbetween the two variables, you were trying to parse:
"09/06/2413:35:01"
convert strings to datetime

convert strings to datetime


By : mca.mazhom kashmiri
Date : March 29 2020, 07:55 AM
this one helps. Your code is comparing both day and time (hours, minutes etc will have to match). Try comparing just the day part like this:
code :
buDataEntities.StudentAttendances.Where(s => s.Date.Subtract(date1).Days == 0)
DateTime date1 = DateTime.Parse(date, new CultureInfo("en-US"));
How to convert strings to DateTime C#

How to convert strings to DateTime C#


By : Carl Pearson
Date : March 29 2020, 07:55 AM
I hope this helps you . You can combine both your time parts with date part respectively and use ParseExact method with MM-dd-yyyyH:mmtt format like;
code :
var date = "01-29-2016";
var ts1 = "1:00am";
var ts2 = "2:30am";

var dt1 = DateTime.ParseExact(date + ts1, "MM-dd-yyyyH:mmtt", CultureInfo.InvariantCulture);
// 29.01.2016 01:00:00
var dt2 = DateTime.ParseExact(date + ts2, "MM-dd-yyyyH:mmtt", CultureInfo.InvariantCulture);
// 29.01.2016 02:30:00
Convert incomplete 12h datetime-like strings into appropriate datetime type

Convert incomplete 12h datetime-like strings into appropriate datetime type


By : laman
Date : March 29 2020, 07:55 AM
I wish did fix the issue. You have data in 5 second intervals throughout multiple days. The desired end format is like this (with AM/PM column we need to add, because Pandas cannot possibly guess, since it looks at one value at a time):
code :
31/12/2016 11:59:55 PM
01/01/2017 12:00:00 AM
01/01/2017 12:00:05 AM
01/01/2017 11:59:55 AM
01/01/2017 12:00:00 PM
01/01/2017 12:59:55 PM
01/01/2017 01:00:00 PM
01/01/2017 01:00:05 PM
01/01/2017 11:59:55 PM
02/01/2017 12:00:00 AM
ts = pd.to_datetime(df.TS, format = '%d/%m/%Y %I:%M:%S')
ts[ts.dt.hour == 12] -= pd.Timedelta(12, 'h')
twelve = ts.dt.time == datetime.time(0,0,0)
newdate = ts.dt.date.diff() > pd.Timedelta(0)
midnight = twelve & newdate
noon = twelve & ~newdate
offset = pd.Series(np.nan, ts.index, dtype='timedelta64[ns]')
offset[midnight] = pd.Timedelta(0)
offset[noon] = pd.Timedelta(12, 'h')
offset.fillna(method='ffill', inplace=True)
ts += offset
How to convert unaware datetime strings into aware datetime objects with timezone?

How to convert unaware datetime strings into aware datetime objects with timezone?


By : Paul
Date : March 29 2020, 07:55 AM
wish help you to fix your issue To add awareness I think dt.localize is what you are looking for:
code :
import pandas as pd

aware = pd.to_datetime(df['date']).dt.tz_localize('Europe/London')
>>> aware
0   2019-05-01 17:05:00+01:00
1   2019-05-01 17:10:00+01:00
2   2019-05-01 17:10:00+01:00
3   2019-05-01 17:15:00+01:00
Name: date, dtype: datetime64[ns, Europe/London]

utc = aware.dt.tz_convert('utc')
>>> utc
0   2019-05-01 16:05:00+00:00
1   2019-05-01 16:10:00+00:00
2   2019-05-01 16:10:00+00:00
3   2019-05-01 16:15:00+00:00
Name: date, dtype: datetime64[ns, UTC]
aware = pd.to_datetime(df['date']).dt.tz_localize('Europe/London', nonexistent="shift_forward")
>>> print(aware)
0   2019-05-01 17:05:00+01:00
1   2019-05-01 17:10:00+01:00
2   2019-05-01 17:10:00+01:00
3   2019-05-01 17:15:00+01:00
4   2019-03-31 03:00:00+01:00
Name: date, dtype: datetime64[ns, Europe/London]


aware = pd.to_datetime(df['date']).dt.tz_localize('Europe/London', nonexistent="NaT")
>>> print(aware)
0   2019-05-01 17:05:00+01:00
1   2019-05-01 17:10:00+01:00
2   2019-05-01 17:10:00+01:00
3   2019-05-01 17:15:00+01:00
4                         NaT
Name: date, dtype: datetime64[ns, Europe/London]

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