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# pandas.DatetimeIndex

class pandas.DatetimeIndex(data=None, freq=<no_default>, tz=<no_default>, ambiguous='raise', dayfirst=False, yearfirst=False, dtype=None, copy=None, name=None)

  Immutable ndarray-like of datetime64 data.

  Represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata.

  Changed in version 2.0.0: The various numeric date/time attributes (`day`, `month`, `year` etc.) now have dtype `int32`. Previously they had dtype `int64`.

  Parameters:
    data array-like (1-dimensional)

      Datetime-like data to construct index with.

    freq str or pandas offset object, optional

      One of pandas date offset strings or corresponding objects. The string ‘infer’ can be passed in order to set the frequency of the index as the inferred frequency upon creation.

    tz zoneinfo.ZoneInfo, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or str

      Set the Timezone of the data.

    ambiguous‘infer’, bool-ndarray, ‘NaT’, default ‘raise’

      When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.

      - ‘infer’ will attempt to infer fall dst-transition hours based on order

      - bool-ndarray where True signifies a DST time, False signifies a non-DST time (note that this flag is only applicable for ambiguous times)

      - ‘NaT’ will return NaT where there are ambiguous times

      - ‘raise’ will raise a ValueError if there are ambiguous times.

    dayfirst bool, default False

      If True, parse dates in data with the day first order.

    yearfirst bool, default False

      If True parse dates in data with the year first order.

    dtype numpy.dtype or DatetimeTZDtype or str, default None

      Note that the only NumPy dtypes allowed are ‘datetime64[ns]’, ‘datetime64[us]’, ‘datetime64[ms]’, ‘datetime64[s]’.

    copy bool, default None

      Whether to copy input data, only relevant for array, Series, and Index inputs (for other input, e.g. a list, a new array is created anyway). Defaults to True for array input and False for Index/Series. Set to False to avoid copying array input at your own risk (if you know the input data won’t be modified elsewhere). Set to True to force copying Series/Index up front.

    name label, default None

      Name to be stored in the index.

  Attributes

  `year` | The year of the datetime.
  `month` | The month as January=1, December=12.
  `day` | The day of the datetime.
  `hour` | The hours of the datetime.
  `minute` | The minutes of the datetime.
  `second` | The seconds of the datetime.
  `microsecond` | The microseconds of the datetime.
  `nanosecond` | The nanoseconds of the datetime.
  `date` | Returns numpy array of python `datetime.date` objects.
  `time` | Returns numpy array of `datetime.time` objects.
  `timetz` | Returns numpy array of `datetime.time` objects with timezones.
  `dayofyear` | The ordinal day of the year.
  `day_of_year` | The ordinal day of the year.
  `dayofweek` | The day of the week with Monday=0, Sunday=6.
  `day_of_week` | The day of the week with Monday=0, Sunday=6.
  `weekday` | The day of the week with Monday=0, Sunday=6.
  `quarter` | The quarter of the date.
  `tz` | Return the timezone.
  `freq` | Return the frequency object if it is set, otherwise None.
  `freqstr` | Return the frequency object as a string if it's set, otherwise None.
  `is_month_start` | Indicates whether the date is the first day of the month.
  `is_month_end` | Indicates whether the date is the last day of the month.
  `is_quarter_start` | Indicator for whether the date is the first day of a quarter.
  `is_quarter_end` | Indicator for whether the date is the last day of a quarter.
  `is_year_start` | Indicate whether the date is the first day of a year.
  `is_year_end` | Indicate whether the date is the last day of the year.
  `is_leap_year` | Boolean indicator if the date belongs to a leap year.
  `inferred_freq` | Return the inferred frequency of the index.

  Methods

  `normalize`() | Convert times to midnight.
  `strftime`(date_format) | Convert to Index using specified date_format.
  `snap`([freq]) | Snap time stamps to nearest occurring frequency.
  `tz_convert`(tz) | Convert tz-aware Datetime Array/Index from one time zone to another.
  `tz_localize`(tz[, ambiguous, nonexistent]) | Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.
  `round`(freq[, ambiguous, nonexistent]) | Perform round operation on the data to the specified freq.
  `floor`(freq[, ambiguous, nonexistent]) | Perform floor operation on the data to the specified freq.
  `ceil`(freq[, ambiguous, nonexistent]) | Perform ceil operation on the data to the specified freq.
  `to_period`([freq]) | Cast to PeriodArray/PeriodIndex at a particular frequency.
  `to_pydatetime`() | Return an ndarray of `datetime.datetime` objects.
  `to_series`([index, name]) | Create a Series with both index and values equal to the index keys.
  `to_frame`([index, name]) | Create a DataFrame with a column containing the Index.
  `to_julian_date`() | Convert Timestamp to a Julian Date.
  `month_name`([locale]) | Return the month names with specified locale.
  `day_name`([locale]) | Return the day names with specified locale.
  `mean`(*[, skipna, axis]) | Return the mean value of the Array.
  `std`([axis, dtype, out, ddof, keepdims, skipna]) | Return sample standard deviation over requested axis.

  See also

  `Index`

    The base pandas Index type.

  `TimedeltaIndex`

    Index of timedelta64 data.

  `PeriodIndex`

    Index of Period data.

  `to_datetime`

    Convert argument to datetime.

  `date_range`

    Create a fixed-frequency DatetimeIndex.

  Notes

  To learn more about the frequency strings, please see this link.

  Examples

      >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
      >>> idx
      DatetimeIndex(['2020-01-01 10:00:00+00:00', '2020-02-01 11:00:00+00:00'],
      dtype='datetime64[us, UTC]', freq=None)