pandas.to_datetime¶
- pandas.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix', cache=True)[source]¶
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’}, default ‘raise’
If ‘raise’, then invalid parsing will raise an exception.
If ‘coerce’, then invalid parsing will be set as NaT.
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).
- yearfirstbool, default False
Specify a date parse order if arg is str or its list-likes.
If True parses dates with the year first, eg 10/11/12 is parsed as 2010-11-12.
If both dayfirst and yearfirst are True, yearfirst is preceded (same as dateutil).
Warning: yearfirst=True is not strict, but will prefer to parse with year first (this is a known bug, based on dateutil behavior).
- utcbool, default None
Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well).
- 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.
- exactbool, True by default
Behaves as: - If True, require an exact format match. - If False, allow the format to match anywhere in the target string.
- 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. 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 False
If True and no format is given, attempt to infer the format of the datetime strings based on the first non-NaN element, 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.
- originscalar, default ‘unix’
Define the reference date. The numeric values would be parsed as number of units (defined by unit) since this reference date.
If ‘unix’ (or POSIX) time; origin is set to 1970-01-01.
If ‘julian’, unit must be ‘D’, and origin is set to beginning of Julian Calendar. Julian day number 0 is assigned to the day starting at noon on January 1, 4713 BC.
If Timestamp convertible, origin is set to Timestamp identified by origin.
- cachebool, default True
If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets. The cache is only used when there are at least 50 values. The presence of out-of-bounds values will render the cache unusable and may slow down parsing.
Changed in version 0.25.0: - changed default value from False to True.
- Returns
- datetime
If parsing succeeded. Return type depends on input:
list-like: DatetimeIndex
Series: Series of datetime64 dtype
scalar: Timestamp
In case when it is not possible to return designated types (e.g. when any element of input is before Timestamp.min or after Timestamp.max) return will have datetime.datetime type (or corresponding array/Series).
See also
DataFrame.astypeCast argument to a specified dtype.
to_timedeltaConvert argument to timedelta.
convert_dtypesConvert dtypes.
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
>>> 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]
If a date does not meet the timestamp limitations, passing errors=’ignore’ will return the original input instead of raising any exception.
Passing errors=’coerce’ will force an out-of-bounds date to NaT, in addition to forcing non-dates (or non-parseable dates) to NaT.
>>> 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
Passing infer_datetime_format=True can often-times speedup a parsing if its not an ISO8601 format exactly, but in a regular format.
>>> 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)
In case input is list-like and the elements of input are of mixed timezones, return will have object type Index if utc=False.
>>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500']) Index([2018-10-26 12:00:00-05:30, 2018-10-26 12:00:00-05:00], dtype='object')
>>> 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)