pandas.to_datetime

pandas.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)

Timestamps outside the interval between Timestamp.min and Timestamp.max (approximately 1677-09-22 to 2262-04-11) will be also forced to NaT.

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

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

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')
0    2000-01-01
1    2000-01-02
...
98   2000-04-08
99   2000-04-09
Length: 100, dtype: datetime64[ns]

Or from strings

>>> df = df.astype(str)
>>> pd.to_datetime(df.day + df.month + df.year, format="%d%m%Y")
0    2000-01-01
1    2000-01-02
...
98   2000-04-08
99   2000-04-09
Length: 100, dtype: datetime64[ns]

Date that does not meet timestamp limitations:

>>> pd.to_datetime('13000101', format='%Y%m%d')
datetime.datetime(1300, 1, 1, 0, 0)
>>> pd.to_datetime('13000101', format='%Y%m%d', coerce=True)
NaT