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.
This function converts a scalar, array-like,
Series
orDataFrame
/dict-like to a pandas datetime object.- Parameters
- argint, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
The object to convert to a datetime. If a
DataFrame
is provided, the method expects minimally the following columns:"year"
,"month"
,"day"
.- errors{‘ignore’, ‘raise’, ‘coerce’}, default ‘raise’
If
'raise'
, then invalid parsing will raise an exception.If
'coerce'
, then invalid parsing will be set asNaT
.If
'ignore'
, then invalid parsing will return the input.
- dayfirstbool, default False
Specify a date parse order if arg is str or is list-like. If
True
, parses dates with the day first, e.g."10/11/12"
is parsed as2012-11-10
.Warning
dayfirst=True
is not strict, but will prefer to parse with day first. If a delimited date string cannot be parsed in accordance with the given dayfirst option, e.g.to_datetime(['31-12-2021'])
, then a warning will be shown.- yearfirstbool, default False
Specify a date parse order if arg is str or is list-like.
If
True
parses dates with the year first, e.g."10/11/12"
is parsed as2010-11-12
.If both dayfirst and yearfirst are
True
, yearfirst is preceded (same asdateutil
).
Warning
yearfirst=True
is not strict, but will prefer to parse with year first.- utcbool, default None
Control timezone-related parsing, localization and conversion.
If
True
, the function always returns a timezone-aware UTC-localizedTimestamp
,Series
orDatetimeIndex
. To do this, timezone-naive inputs are localized as UTC, while timezone-aware inputs are converted to UTC.If
False
(default), inputs will not be coerced to UTC. Timezone-naive inputs will remain naive, while timezone-aware ones will keep their time offsets. Limitations exist for mixed offsets (typically, daylight savings), see Examples section for details.
See also: pandas general documentation about timezone conversion and localization.
- formatstr, default None
The strftime to parse time, e.g.
"%d/%m/%Y"
. Note that"%f"
will parse all the way up to nanoseconds. See strftime documentation for more information on choices.- exactbool, default True
Control how format is used:
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'
andorigin='unix'
, 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 number0
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
toTrue
.
- Returns
- datetime
If parsing succeeded. Return type depends on input (types in parenthesis correspond to fallback in case of unsuccessful timezone or out-of-range timestamp parsing):
scalar:
Timestamp
(ordatetime.datetime
)array-like:
DatetimeIndex
(orSeries
withobject
dtype containingdatetime.datetime
)Series:
Series
ofdatetime64
dtype (orSeries
ofobject
dtype containingdatetime.datetime
)DataFrame:
Series
ofdatetime64
dtype (orSeries
ofobject
dtype containingdatetime.datetime
)
- Raises
- ParserError
When parsing a date from string fails.
- ValueError
When another datetime conversion error happens. For example when one of ‘year’, ‘month’, day’ columns is missing in a
DataFrame
, or when a Timezone-awaredatetime.datetime
is found in an array-like of mixed time offsets, andutc=False
.
See also
DataFrame.astype
Cast argument to a specified dtype.
to_timedelta
Convert argument to timedelta.
convert_dtypes
Convert dtypes.
Notes
Many input types are supported, and lead to different output types:
scalars can be int, float, str, datetime object (from stdlib
datetime
module ornumpy
). They are converted toTimestamp
when possible, otherwise they are converted todatetime.datetime
. None/NaN/null scalars are converted toNaT
.array-like can contain int, float, str, datetime objects. They are converted to
DatetimeIndex
when possible, otherwise they are converted toIndex
withobject
dtype, containingdatetime.datetime
. None/NaN/null entries are converted toNaT
in both cases.Series are converted to
Series
withdatetime64
dtype when possible, otherwise they are converted toSeries
withobject
dtype, containingdatetime.datetime
. None/NaN/null entries are converted toNaT
in both cases.DataFrame/dict-like are converted to
Series
withdatetime64
dtype. For each row a datetime is created from assembling the various dataframe columns. Column keys can be common abbreviations like [‘year’, ‘month’, ‘day’, ‘minute’, ‘second’, ‘ms’, ‘us’, ‘ns’]) or plurals of the same.
The following causes are responsible for
datetime.datetime
objects being returned (possibly inside anIndex
or aSeries
withobject
dtype) instead of a proper pandas designated type (Timestamp
,DatetimeIndex
orSeries
withdatetime64
dtype):when any input element is before
Timestamp.min
or afterTimestamp.max
, see timestamp limitations.when
utc=False
(default) and the input is an array-like orSeries
containing mixed naive/aware datetime, or aware with mixed time offsets. Note that this happens in the (quite frequent) situation when the timezone has a daylight savings policy. In that case you may wish to useutc=True
.
Examples
Handling various input formats
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]
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)
Non-convertible date/times
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 toNaT
, in addition to forcing non-dates (or non-parseable dates) toNaT
.>>> 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
Timezones and time offsets
The default behaviour (
utc=False
) is as follows:Timezone-naive inputs are converted to timezone-naive
DatetimeIndex
:
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00:15']) DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'], dtype='datetime64[ns]', freq=None)
Timezone-aware inputs with constant time offset are converted to timezone-aware
DatetimeIndex
:
>>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500']) DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'], dtype='datetime64[ns, pytz.FixedOffset(-300)]', freq=None)
However, timezone-aware inputs with mixed time offsets (for example issued from a timezone with daylight savings, such as Europe/Paris) are not successfully converted to a
DatetimeIndex
. Instead a simpleIndex
containingdatetime.datetime
objects is returned:
>>> pd.to_datetime(['2020-10-25 02:00 +0200', '2020-10-25 04:00 +0100']) Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00], dtype='object')
A mix of timezone-aware and timezone-naive inputs is converted to a timezone-aware
DatetimeIndex
if the offsets of the timezone-aware are constant:
>>> from datetime import datetime >>> pd.to_datetime(["2020-01-01 01:00 -01:00", datetime(2020, 1, 1, 3, 0)]) DatetimeIndex(['2020-01-01 01:00:00-01:00', '2020-01-01 02:00:00-01:00'], dtype='datetime64[ns, pytz.FixedOffset(-60)]', freq=None)
Setting
utc=True
solves most of the above issues:Timezone-naive inputs are localized as UTC
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True) DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)
Timezone-aware inputs are converted to UTC (the output represents the exact same datetime, but viewed from the UTC time offset +00:00).
>>> 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)
Inputs can contain both naive and aware, string or datetime, the above rules still apply
>>> from datetime import timezone, timedelta >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 12:00 -0530', ... datetime(2020, 1, 1, 18), ... datetime(2020, 1, 1, 18, ... tzinfo=timezone(-timedelta(hours=1)))], ... utc=True) DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 17:30:00+00:00', '2020-01-01 18:00:00+00:00', '2020-01-01 19:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)