pandas.to_datetime — pandas 2.2.2 documentation (2024)

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, themethod expects minimally the following columns: "year","month", "day". The column “year”must be specified in 4-digit format.

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.

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 as 2012-11-10.

Warning

dayfirst=True is not strict, but will prefer to parsewith day first.

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 as 2010-11-12.

• If both dayfirst and yearfirst are True, yearfirst ispreceded (same as dateutil).

Warning

yearfirst=True is not strict, but will prefer to parsewith year first.

utcbool, default False

Control timezone-related parsing, localization and conversion.

• If True, the function always returns a timezone-awareUTC-localized Timestamp, Series orDatetimeIndex. To do this, timezone-naive inputs arelocalized 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 oneswill keep their time offsets. Limitations exist for mixedoffsets (typically, daylight savings), see Examples section for details.

Warning

In a future version of pandas, parsing datetimes with mixed timezones will raise an error unless utc=True.Please specify utc=True to opt in to the new behaviourand silence this warning. To create a Series with mixed offsets andobject dtype, please use apply and datetime.datetime.strptime.

See also: pandas general documentation about timezone conversion andlocalization.

formatstr, default None

The strftime to parse time, e.g. "%d/%m/%Y". Seestrftime documentation for more information on choices, thoughnote that "%f" will parse all the way up to nanoseconds.You can also pass:

• “ISO8601”, to parse any ISO8601time string (not necessarily in exactly the same format);

• “mixed”, to infer the format for each element individually. This is risky,and you should probably use it along with dayfirst.

Note

See Also

How to Fix AttributeError: 'timestamp' object has no attribute 'dt'Timestamp Object Has No Attribute dt: What It Means and How to Fix ItAttributeError: Can only use .dt accessor with datetimelike values | bobbyhadzTimestamp Object Has No Attribute Dt - Debug Lab

If a DataFrame is passed, then format has no effect.

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 targetstring.

Cannot be used alongside format='ISO8601' or format='mixed'.

unitstr, default ‘ns’

The unit of the arg (D,s,ms,us,ns) denote the unit, which is aninteger or float number. This will be based off the origin.Example, with unit='ms' and origin='unix', this would calculatethe number of milliseconds to the unix epoch start.

infer_datetime_formatbool, default False

If True and no format is given, attempt to infer the formatof 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.

Deprecated since version 2.0.0: A strict version of this argument is now the default, passing it hasno effect.

originscalar, default ‘unix’

Define the reference date. The numeric values would be parsed as numberof 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 tobeginning of Julian Calendar. Julian day number 0 is assignedto the day starting at noon on January 1, 4713 BC.

• If Timestamp convertible (Timestamp, dt.datetime, np.datetimt64 or datestring), origin is set to Timestamp identified by origin.

• If a float or integer, origin is the difference(in units determined by the unit argument) relative to 1970-01-01.

cachebool, default True

If True, use a cache of unique, converted dates to apply thedatetime conversion. May produce significant speed-up when parsingduplicate date strings, especially ones with timezone offsets. The cacheis only used when there are at least 50 values. The presence ofout-of-bounds values will render the cache unusable and may slow downparsing.

Returns:

datetime

If parsing succeeded.Return type depends on input (types in parenthesis correspond tofallback in case of unsuccessful timezone or out-of-range timestampparsing):

• scalar: Timestamp (or datetime.datetime)

• array-like: DatetimeIndex (or Series withobject dtype containing datetime.datetime)

• Series: Series of datetime64 dtype (orSeries of object dtype containingdatetime.datetime)

• DataFrame: Series of datetime64 dtype (orSeries of object dtype containingdatetime.datetime)

Raises:

ParserError

When parsing a date from string fails.

ValueError

When another datetime conversion error happens. For example when oneof ‘year’, ‘month’, day’ columns is missing in a DataFrame, orwhen a Timezone-aware datetime.datetime is found in an array-likeof mixed time offsets, and utc=False.

Notes

Many input types are supported, and lead to different output types:

• scalars can be int, float, str, datetime object (from stdlib datetimemodule or numpy). They are converted to Timestamp whenpossible, otherwise they are converted to datetime.datetime.None/NaN/null scalars are converted to NaT.

• array-like can contain int, float, str, datetime objects. They areconverted to DatetimeIndex when possible, otherwise they areconverted to Index with object dtype, containingdatetime.datetime. None/NaN/null entries are converted toNaT in both cases.

• Series are converted to Series with datetime64dtype when possible, otherwise they are converted to Series withobject dtype, containing datetime.datetime. None/NaN/nullentries are converted to NaT in both cases.

• DataFrame/dict-like are converted to Series withdatetime64 dtype. For each row a datetime is created from assemblingthe various dataframe columns. Column keys can be common abbreviationslike [‘year’, ‘month’, ‘day’, ‘minute’, ‘second’, ‘ms’, ‘us’, ‘ns’]) orplurals of the same.

The following causes are responsible for datetime.datetime objectsbeing returned (possibly inside an Index or a Series withobject dtype) instead of a proper pandas designated type(Timestamp, DatetimeIndex or Serieswith datetime64 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 mixedtime offsets. Note that this happens in the (quite frequent) situation whenthe timezone has a daylight savings policy. In that case you may wish touse utc=True.

Examples

Handling various input formats

Assembling a datetime from multiple columns of a DataFrame. The keyscan 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 2015-02-041 2016-03-05dtype: datetime64[ns]

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')

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)

Differences with strptime behavior

"%f" will parse all the way up to nanoseconds.

>>> pd.to_datetime('2018-10-26 12:00:00.0000000011',...  format='%Y-%m-%d %H:%M:%S.%f')Timestamp('2018-10-26 12:00:00.000000001')

Non-convertible date/times

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='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 12:00:00', '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 totimezone-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, UTC-05:00]', freq=None)

• However, timezone-aware inputs with mixed time offsets (for exampleissued from a timezone with daylight savings, such as Europe/Paris)are not successfully converted to a DatetimeIndex.Parsing datetimes with mixed time zones will show a warning unlessutc=True. If you specify utc=False the warning below will be shownand a simple Index containing datetime.datetimeobjects will be returned:

>>> pd.to_datetime(['2020-10-25 02:00 +0200',...  '2020-10-25 04:00 +0100']) FutureWarning: In a future version of pandas, parsing datetimes with mixedtime zones will raise an error unless `utc=True`. Please specify `utc=True`to opt in to the new behaviour and silence this warning. To create a `Series`with mixed offsets and `object` dtype, please use `apply` and`datetime.datetime.strptime`.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 also converted toa simple Index containing datetime.datetime objects:

>>> from datetime import datetime>>> pd.to_datetime(["2020-01-01 01:00:00-01:00",...  datetime(2020, 1, 1, 3, 0)]) FutureWarning: In a future version of pandas, parsing datetimes with mixedtime zones will raise an error unless `utc=True`. Please specify `utc=True`to opt in to the new behaviour and silence this warning. To create a `Series`with mixed offsets and `object` dtype, please use `apply` and`datetime.datetime.strptime`.Index([2020-01-01 01:00:00-01:00, 2020-01-01 03:00:00], dtype='object')

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 theexact 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 string or datetime, the aboverules still apply

>>> pd.to_datetime(['2018-10-26 12:00', datetime(2020, 1, 1, 18)], utc=True)DatetimeIndex(['2018-10-26 12:00:00+00:00', '2020-01-01 18:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)