Convert argument to datetime.
This function converts a scalar, array-like, Series or DataFrame/dict-like to a pandas datetime object.
The object to convert to a datetime. If a DataFrame is provided, the method expects minimally the following columns: "year", "month", "day". The column âyearâ must be specified in 4-digit format.
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.
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 parse with day first.
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 is preceded (same as dateutil).
Warning
yearfirst=True is not strict, but will prefer to parse with year first.
Control timezone-related parsing, localization and conversion.
If True, the function always returns a timezone-aware UTC-localized Timestamp, Series or DatetimeIndex. 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.
Warning
In a future version of pandas, parsing datetimes with mixed time 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.
See also: pandas general documentation about timezone conversion and localization.
The strftime to parse time, e.g. "%d/%m/%Y". See strftime documentation for more information on choices, though note that "%f" will parse all the way up to nanoseconds. You can also pass:
âISO8601â, to parse any ISO8601 time string (not necessarily in exactly the same format);
âmixedâ, to infer the format for each element individually. This is risky, and you should probably use it along with dayfirst.
Note
If a DataFrame is passed, then format has no effect.
Control how format is used:
If True, require an exact format match.
If False, allow the format to match anywhere in the target string.
Cannot be used alongside format='ISO8601' or format='mixed'.
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' and origin='unix', this would calculate the number of milliseconds to the unix epoch start.
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.
Deprecated since version 2.0.0: A strict version of this argument is now the default, passing it has no effect.
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 number 0 is assigned to the day starting at noon on January 1, 4713 BC.
If Timestamp convertible (Timestamp, dt.datetime, np.datetimt64 or date string), 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.
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.
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 (or datetime.datetime)
array-like: DatetimeIndex (or Series with object dtype containing datetime.datetime)
Series: Series of datetime64 dtype (or Series of object dtype containing datetime.datetime)
DataFrame: Series of datetime64 dtype (or Series of object dtype containing datetime.datetime)
When parsing a date from string fails.
When another datetime conversion error happens. For example when one of âyearâ, âmonthâ, dayâ columns is missing in a DataFrame, or when a Timezone-aware datetime.datetime is found in an array-like of 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 datetime module or numpy). They are converted to Timestamp when possible, 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 are converted to DatetimeIndex when possible, otherwise they are converted to Index with object dtype, containing datetime.datetime. None/NaN/null entries are converted to NaT in both cases.
Series are converted to Series with datetime64 dtype when possible, otherwise they are converted to Series with object dtype, containing datetime.datetime. None/NaN/null entries are converted to NaT in both cases.
DataFrame/dict-like are converted to Series with datetime64 dtype. For each row a datetime is created from assembling the various dataframe columns. Column keys can be common abbreviations like [âyearâ, âmonthâ, âdayâ, âminuteâ, âsecondâ, âmsâ, âusâ, ânsâ]) or plurals of the same.
The following causes are responsible for datetime.datetime objects being returned (possibly inside an Index or a Series with object dtype) instead of a proper pandas designated type (Timestamp, DatetimeIndex or Series with datetime64 dtype):
when any input element is before Timestamp.min or after Timestamp.max, see timestamp limitations.
when utc=False (default) and the input is an array-like or Series 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 use utc=True.
Examples
Handling various input formats
Assembling a datetime from multiple columns of a DataFrame. The keys can be common abbreviations like [âyearâ, âmonthâ, âdayâ, âminuteâ, âsecondâ, âmsâ, âusâ, ânsâ]) 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]
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)
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 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, UTC-05:00]', 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. Parsing datetimes with mixed time zones will show a warning unless utc=True. If you specify utc=False the warning below will be shown and a simple Index containing datetime.datetime objects 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 mixed
time 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 to a 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 mixed time 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 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 string or datetime, the above rules 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)
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