pandas.to_datetime

pandas.to_datetime(*args, **kwargs)

Convert argument to datetime.

Parameters:

arg : string, datetime, list, tuple, 1-d array, Series

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

dayfirst : boolean, 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).

yearfirst : boolean, 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 beahavior).

utc : boolean, default None

Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well).

box : boolean, default True

• If True returns a DatetimeIndex
• If False returns ndarray of values.

format : string, default None

strftime to parse time, eg “%d/%m/%Y”, note that “%f” will parse all the way up to nanoseconds.

exact : boolean, True by default

• If True, require an exact format match.
• If False, allow the format to match anywhere in the target string.

unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch

(e.g. a unix timestamp), which is an integer/float number.

infer_datetime_format : boolean, default False

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:

ret : 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 correspoding array/Series).

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 that does not meet timestamp limitations, passing errors=’coerce’ will force to NaT. Furthermore this will force non-dates to NaT as well.

>>> pd.to_datetime('13000101', format='%Y%m%d')
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