How to Get Datetime Value from Timestamp Type Column

Is there a way to get DateTime value from timestamp type column?

The Transact-SQL timestamp data type is a binary data type with no time-related values.

So to answer your question: Is there a way to get DateTime value from timestamp type column?

The answer is: No

How do I get date/time information from a TIMESTAMP column?

TIMESTAMP is an unfortunate name the SQL Server team gave the data type. It is for concurrency, and has nothing to do with date or time - they've recommended using its alias, ROWVERSION to prevent confusion. From this Books Online article, "In DDL statements, use rowversion instead of timestamp wherever possible."

Unfortunately you won't be able to derive any date/time details from the ROWVERSION column you already have, but if this information is important, you should add CreatedDate / ModifiedDate columns, for example:

ALTER TABLE dbo.foo ADD CreatedDate DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP;
ALTER TABLE dbo.foo ADD ModifiedDate DATETIME NULL;

Then create a TRIGGER that fires on UPDATE to keep the ModifiedDate value current. You may need to decide whether you want the ModifiedDate to be NULL or equal to CreatedDate on initialization.

Should I use the datetime or timestamp data type in MySQL?

Timestamps in MySQL are generally used to track changes to records, and are often updated every time the record is changed. If you want to store a specific value you should use a datetime field.

If you meant that you want to decide between using a UNIX timestamp or a native MySQL datetime field, go with the native DATETIME format. You can do calculations within MySQL that way
("SELECT DATE_ADD(my_datetime, INTERVAL 1 DAY)") and it is simple to change the format of the value to a UNIX timestamp ("SELECT UNIX_TIMESTAMP(my_datetime)") when you query the record if you want to operate on it with PHP.

How to convert SQL Server's timestamp column to datetime format

SQL Server's TIMESTAMP datatype has nothing to do with a date and time!

It's just a hexadecimal representation of a consecutive 8 byte integer - it's only good for making sure a row hasn't change since it's been read.

You can read off the hexadecimal integer or if you want a BIGINT. As an example:

SELECT CAST (0x0000000017E30D64 AS BIGINT)

The result is

400756068

In newer versions of SQL Server, it's being called RowVersion - since that's really what it is. See the MSDN docs on ROWVERSION:

Is a data type that exposes automatically generated, unique binary numbers within a database. rowversion is generally used as a mechanism
for version-stamping table rows. The
rowversion data type is just an incrementing number and does not
preserve a date or a time. To record a date or time, use a datetime2
data type.

So you cannot convert a SQL Server TIMESTAMP to a date/time - it's just not a date/time.

But if you're saying timestamp but really you mean a DATETIME column - then you can use any of those valid date formats described in the CAST and CONVERT topic in the MSDN help. Those are defined and supported "out of the box" by SQL Server. Anything else is not supported, e.g. you have to do a lot of manual casting and concatenating (not recommended).

The format you're looking for looks a bit like the ODBC canonical (style = 121):

DECLARE @today DATETIME = SYSDATETIME()

SELECT CONVERT(VARCHAR(50), @today, 121)

gives:

2011-11-14 10:29:00.470

SQL Server 2012 will finally have a FORMAT function to do custom formatting......

How to get Time from DateTime format in SQL?

SQL Server 2008:

SELECT cast(AttDate as time) [time]
FROM yourtable

Earlier versions:

SELECT convert(char(5), AttDate, 108) [time]
FROM yourtable

Splitting timestamp column into separate date and time columns

I'm not sure why you would want to do this in the first place, but if you really must...

df = pd.DataFrame({'my_timestamp': pd.date_range('2016-1-1 15:00', periods=5)})

>>> df
         my_timestamp
0 2016-01-01 15:00:00
1 2016-01-02 15:00:00
2 2016-01-03 15:00:00
3 2016-01-04 15:00:00
4 2016-01-05 15:00:00

df['new_date'] = [d.date() for d in df['my_timestamp']]
df['new_time'] = [d.time() for d in df['my_timestamp']]

>>> df
         my_timestamp    new_date  new_time
0 2016-01-01 15:00:00  2016-01-01  15:00:00
1 2016-01-02 15:00:00  2016-01-02  15:00:00
2 2016-01-03 15:00:00  2016-01-03  15:00:00
3 2016-01-04 15:00:00  2016-01-04  15:00:00
4 2016-01-05 15:00:00  2016-01-05  15:00:00

The conversion to CST is more tricky. I assume that the current timestamps are 'unaware', i.e. they do not have a timezone attached? If not, how would you expect to convert them?

For more details:

https://docs.python.org/2/library/datetime.html

How to make an unaware datetime timezone aware in python

EDIT

An alternative method that only loops once across the timestamps instead of twice:

new_dates, new_times = zip(*[(d.date(), d.time()) for d in df['my_timestamp']])
df = df.assign(new_date=new_dates, new_time=new_times)

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