cudf.to_datetime#

cudf.to_datetime(arg, errors: Literal['raise', 'coerce', 'warn', 'ignore'] = 'raise', dayfirst: bool = False, yearfirst: bool = False, utc: bool = False, format: str | None = None, exact: bool = True, unit: str = 'ns', infer_datetime_format: bool = True, origin='unix', cache: bool = True)#

Convert argument to datetime.

Parameters:

argint, float, str, datetime, list, tuple, 1-d array,

Series DataFrame/dict-like The object to convert to a datetime.

errors{‘ignore’, ‘raise’, ‘coerce’, ‘warn’}, default ‘raise’

If ‘raise’, then invalid parsing will raise an exception.
If ‘coerce’, then invalid parsing will be set as NaT.
If ‘warn’prints last exceptions as warnings and
return the input.
If ‘ignore’, then invalid parsing will return the input.

dayfirstbool, default False

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. Warning: dayfirst=True is not strict, but will prefer to parse with day first (this is a known bug, based on dateutil behavior).

utcbool, default False

Whether the result should be have a UTC timezone.

formatstr, default None

The strftime to parse time, eg “%d/%m/%Y”, note that “%f” will parse all the way up to nanoseconds. See strftime documentation for more information on choices: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.

unitstr, default ‘ns’

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(unix epoch start). Example, with unit=’ms’ and origin=’unix’ (the default), this would calculate the number of milliseconds to the unix epoch start.

infer_datetime_formatbool, default True

If True and no format is given, attempt to infer the format of the datetime strings, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x.

Returns:

datetime: If parsing succeeded. Return type depends on input: - list-like: DatetimeIndex - Series: Series of datetime64 dtype - scalar: Timestamp

Examples

Assembling a datetime from multiple columns of a DataFrame. The keys can be common abbreviations like [‘year’, ‘month’, ‘day’, ‘minute’, ‘second’, ‘ms’, ‘us’, ‘ns’]) or plurals of the same

>>> import cudf
>>> df = cudf.DataFrame({'year': [2015, 2016],
...                    'month': [2, 3],
...                    'day': [4, 5]})
>>> cudf.to_datetime(df)
0   2015-02-04
1   2016-03-05
dtype: datetime64[ns]
>>> cudf.to_datetime(1490195805, unit='s')
numpy.datetime64('2017-03-22T15:16:45.000000000')
>>> cudf.to_datetime(1490195805433502912, unit='ns')
numpy.datetime64('1780-11-20T01:02:30.494253056')