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)[source]#

Convert argument to datetime.

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:

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.


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


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')
>>> cudf.to_datetime(1490195805433502912, unit='ns')