pandas.tseries.tools.to_datetime

pandas.tseries.tools.to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True, format=None, exact=True, coerce=False, unit='ns', infer_datetime_format=False)

Convert argument to datetime.

Parameters :

arg : string, datetime, array of strings (with possible NAs)

errors : {‘ignore’, ‘raise’}, default ‘ignore’

Errors are ignored by default (values left untouched)

dayfirst : boolean, default False

If True parses dates with the day first, eg 20/01/2005 Warning: dayfirst=True is not strict, but will prefer to parse with day first (this is a known bug).

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.

coerce : force errors to NaT (False by default)

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 no format is given, try to infer the format based on the first datetime string. Provides a large speed-up in many cases.

Returns :

ret : datetime if parsing succeeded. Return type depends on input:

• list-like: DatetimeIndex
• Series: Series of datetime64 dtype
• scalar: Timestamp

Examples

Take separate series and convert to datetime

>>> import pandas as pd
>>> i = pd.date_range('20000101',periods=100)
>>> df = pd.DataFrame(dict(year = i.year, month = i.month, day = i.day))
>>> pd.to_datetime(df.year*10000 + df.month*100 + df.day, format='%Y%m%d')

Or from strings

>>> df = df.astype(str)
>>> pd.to_datetime(df.day + df.month + df.year, format="%d%m%Y")