Python convert series to datetime
I have a Pandas DataFrame as below Show
TimeReviewed is a series type
I've tried below, but it still doesn't change the Series type
How can I change the df.TimeReviewed to DateTime type and pull out year, month, day, hour, min, sec separately? I'm kinda new to python, thanks for your help. Convert argument to datetime. This function converts a
scalar, array-like, The object to convert to a datetime. If a
Specify a date parse order if arg is str or is list-like. If Warning
Specify a date parse order if arg is str or is list-like.
Warning
Control timezone-related parsing, localization and conversion.
See also: pandas general documentation about timezone conversion and localization. formatstr, default NoneThe strftime to parse time, e.g. Control how format is used:
The unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number. This will be based off the
origin. Example, with If Define the reference date. The numeric values would be parsed as number of units (defined by unit) since this reference date.
If Changed in version 0.25.0: changed default value from If parsing succeeded. Return type depends on input (types in parenthesis correspond to fallback in case of unsuccessful timezone or out-of-range timestamp parsing):
When parsing a date from string fails. ValueErrorWhen another datetime conversion error happens. For example when one of ‘year’,
‘month’, day’ columns is missing in a Notes Many input types are supported, and lead to different output types:
The following causes are responsible for
Examples Handling various input formats Assembling a datetime from multiple columns of a
>>> df = pd.DataFrame({'year': [2015, 2016], ... 'month': [2, 3], ... 'day': [4, 5]}) >>> pd.to_datetime(df) 0 2015-02-04 1 2016-03-05 dtype: datetime64[ns] Passing >>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000) >>> s.head() 0 3/11/2000 1 3/12/2000 2 3/13/2000 3 3/11/2000 4 3/12/2000 dtype: object >>> %timeit pd.to_datetime(s, infer_datetime_format=True) 100 loops, best of 3: 10.4 ms per loop >>> %timeit pd.to_datetime(s, infer_datetime_format=False) 1 loop, best of 3: 471 ms per loop Using a unix epoch time >>> pd.to_datetime(1490195805, unit='s') Timestamp('2017-03-22 15:16:45') >>> pd.to_datetime(1490195805433502912, unit='ns') Timestamp('2017-03-22 15:16:45.433502912') Warning For float arg, precision rounding might happen. To prevent unexpected behavior use a fixed-width exact type. Using a non-unix epoch origin >>> pd.to_datetime([1, 2, 3], unit='D', ... origin=pd.Timestamp('1960-01-01')) DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None) Non-convertible date/times If a date does not meet the timestamp limitations, passing Passing >>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore') datetime.datetime(1300, 1, 1, 0, 0) >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT Timezones and time offsets The default behaviour (
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00:15']) DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'], dtype='datetime64[ns]', freq=None)
>>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500']) DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'], dtype='datetime64[ns, pytz.FixedOffset(-300)]', freq=None)
>>> pd.to_datetime(['2020-10-25 02:00 +0200', '2020-10-25 04:00 +0100']) Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00], dtype='object')
>>> from datetime import datetime >>> pd.to_datetime(["2020-01-01 01:00 -01:00", datetime(2020, 1, 1, 3, 0)]) DatetimeIndex(['2020-01-01 01:00:00-01:00', '2020-01-01 02:00:00-01:00'], dtype='datetime64[ns, pytz.FixedOffset(-60)]', freq=None)
>>> from datetime import datetime, timezone, timedelta >>> d = datetime(2020, 1, 1, 18, tzinfo=timezone(-timedelta(hours=1))) >>> pd.to_datetime(["2020-01-01 17:00 -0100", d]) Traceback (most recent call last): ... ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True Setting
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True) DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)
>>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'], ... utc=True) DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 12:00 -0530', ... datetime(2020, 1, 1, 18), ... datetime(2020, 1, 1, 18, ... tzinfo=timezone(-timedelta(hours=1)))], ... utc=True) DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 17:30:00+00:00', '2020-01-01 18:00:00+00:00', '2020-01-01 19:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None) How do you convert series to time in Python?to_datetime() Function helps in converting a date string to a python date object. So, it can be utilized for converting a series of date strings to a time series.
How do you convert a series to a DataFrame in Python?to_frame() function is used to convert the given series object to a dataframe. Parameter : name : The passed name should substitute for the series name (if it has one).
How do I convert a string to a date in pandas?Use pandas.
pandas. to_datetime() method is used to change String/Object time to date type (datetime64[ns]). This method is smart enough to change different formats of the String date column to date.
How does PD To_datetime work?The to_datetime() function is used to convert argument to datetime. Specify a date parse order if arg is str or its list-likes. If True, parses dates with the day first, eg 10/11/12 is parsed as 2012-11-10.
|