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
: string, default ‘ns’
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 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')
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
