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# pandas.to_datetime

pandas.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=False, format=None, exact=<no_default>, unit=None, origin='unix', cache=True)

  Convert argument to datetime.

  This function converts a scalar, array-like, `Series` or `DataFrame`/dict-like to a pandas datetime object.

  Parameters:
    arg int, 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"`. The column “year” must be specified in 4-digit format.

    errors{‘raise’, ‘coerce’}, default ‘raise’

      - If `'raise'`, then invalid parsing will raise an exception.

      - If `'coerce'`, then invalid parsing will be set as `NaT`.

    dayfirst bool, 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 parse with day first.

    yearfirst bool, 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 is preceded (same as `dateutil`).

      Warning

      `yearfirst=True` is not strict, but will prefer to parse with year first.

    utc bool, default False

      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.

      See also: pandas general documentation about timezone conversion and localization.

    format str, default None

      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:

      - “ISO8601”, to parse any ISO8601 time 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

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

    exact bool, 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.

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

    unit str, 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'` and `origin='unix'`, this would calculate the number of milliseconds to the unix epoch start.

    origin scalar, 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 number `0` is assigned to the day starting at noon on January 1, 4713 BC.

      - If Timestamp convertible (Timestamp, dt.datetime, np.datetime64 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.

    cache bool, 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.

  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` (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`)

  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-aware `datetime.datetime` is found in an array-like of mixed time offsets, and `utc=False`, or when parsing datetimes with mixed time zones unless `utc=True`. If parsing datetimes with mixed time zones, please specify `utc=True`.

  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 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 [‘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 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 [‘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[us]

  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[s]', 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("invalid for Ymd", 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[us]', 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[us, 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 raise a ValueError unless `utc=True`:

      >>> pd.to_datetime(
      ...     ["2020-10-25 02:00 +0200", "2020-10-25 04:00 +0100"]
      ... )
      ValueError: Mixed timezones detected. Pass utc=True in to_datetime
      or tz='UTC' in DatetimeIndex to convert to a common timezone.

  - To create a `Series` with mixed offsets and `object` dtype, please use `Series.apply()` and `datetime.datetime.strptime()`:

      >>> import datetime as dt
      >>> ser = pd.Series(["2020-10-25 02:00 +0200", "2020-10-25 04:00 +0100"])
      >>> ser.apply(lambda x: dt.datetime.strptime(x, "%Y-%m-%d %H:%M %z"))
      0    2020-10-25 02:00:00+02:00
      1    2020-10-25 04:00:00+01:00
      dtype: object

  - A mix of timezone-aware and timezone-naive inputs will also raise a ValueError unless `utc=True`:

      >>> from datetime import datetime
      >>> pd.to_datetime(
      ...     ["2020-01-01 01:00:00-01:00", datetime(2020, 1, 1, 3, 0)]
      ... )
      ValueError: Mixed timezones detected. Pass utc=True in to_datetime
      or tz='UTC' in DatetimeIndex to convert to a common timezone.

  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[us, 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[us, 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[us, UTC]', freq=None)