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Parameters
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DatetimeArraya�
Pandas ExtensionArray for tz-naive or tz-aware datetime data.
.. versionadded:: 0.24.0
.. warning::
DatetimeArray is currently experimental, and its API may change
without warning. In particular, :attr:`DatetimeArray.dtype` is
expected to change to always be an instance of an ``ExtensionDtype``
subclass.
Parameters
----------
values : Series, Index, DatetimeArray, ndarray
The datetime data.
For DatetimeArray `values` (or a Series or Index boxing one),
`dtype` and `freq` will be extracted from `values`.
dtype : numpy.dtype or DatetimeTZDtype
Note that the only NumPy dtype allowed is 'datetime64[ns]'.
freq : str or Offset, optional
The frequency.
copy : bool, default False
Whether to copy the underlying array of values.
Attributes
----------
None
Methods
-------
None
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|||||||�}|d k �r t|t��rN|d k �r:|jd �}|d k �rN|jd �}t|t��rjt||||�}n*t||||d�}tjdd � |D �tjd
�}|d k �r�|j n|j }| j||t|�d �}|d k �r`|j d k�r`tj|j|||d �}| |�}|d k �r|j|�j }|d k �r`|j|�j }nBtj d
|j |j |dd
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|k�r�|dd � }| �r�t |��r�|d |k�r�|d d� }t|�}| j|j||d �S )Nc s s | ]}|d kV qd S )Nr6 )�.0�xr6 r6 r7 � <genexpr>h s z0DatetimeArray._generate_range.<locals>.<genexpr>z1Must provide freq argument if no data is supplied� zVOf the four parameters: start, end, periods, and freq, exactly three must be specifiedz$Neither `start` nor `end` can be NaT)r9 r: �periods�offsetc S s g | ]
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d | _ d S )N)ry )rL r6 r6 r7 �_maybe_clear_freq� s z DatetimeArray._maybe_clear_freqc s � fdd�S )Nc s t | � j� jd�S )N)rE r5 )r rE r5 )r� )rL r6 r7 �<lambda>� s z)DatetimeArray._box_func.<locals>.<lambda>r6 )rL r6 )rL r7 � _box_func� s zDatetimeArray._box_func)�returnc C s | j S )a%
The dtype for the DatetimeArray.
.. warning::
A future version of pandas will change dtype to never be a
``numpy.dtype``. Instead, :attr:`DatetimeArray.dtype` will
always be an instance of an ``ExtensionDtype`` subclass.
Returns
-------
numpy.dtype or DatetimeTZDtype
If the values are tz-naive, then ``np.dtype('datetime64[ns]')``
is returned.
If the values are tz-aware, then the ``DatetimeTZDtype``
is returned.
)r� )rL r6 r6 r7 r� � s zDatetimeArray.dtypec C s t | jdd�S )z�
Return timezone, if any.
Returns
-------
datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None
Returns None when the array is tz-naive.
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Return an iterator over the boxed values
Yields
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tstamp : Timestamp
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datetime64r �hasattrr r r� )rL r� �other_tzr6 r6 r7 � _has_same_tzi s
zDatetimeArray._has_same_tzc C sb t |dd �}t |dd �}t|�r(|jj}|tkr2n,| jd krN|d k r^td��n|d kr^td��d S )Nr r� z;Cannot compare tz-naive and tz-aware datetime-like objects.z:Cannot compare tz-naive and tz-aware datetime-like objects)r� r r� r5 r
r� )rL r� r� Z other_dtyper6 r6 r7 r� u s
z(DatetimeArray._assert_tzawareness_compatc C s� t | �t |�krtd��t|tj�r<t|�s0t�t| �|�}| j|�sZt t| �j
� d���| j }|j }| j |j B }t
|| |d�}| js�|jr�t||< |jd�S )z3subtract DatetimeArray/Index or ndarray[datetime64]z$cannot add indices of unequal lengthz9 subtraction must have the same timezones or no timezones)�arr_maskztimedelta64[ns])r� r� r r� r� r r� r� r� r� rQ r@ �_isnanr. �_hasnansr r� )rL r� Zself_i8Zother_i8r� �
new_valuesr6 r6 r7 �_sub_datetime_arraylike� s
z%DatetimeArray._sub_datetime_arraylikec C s� | j dkr | j� j|�j| j�S t|t� s0t�y>| jd k rH| j d �}n| }|j
|�}t j |�}|j | j�}W nL t
k
r� tjdt� | jd�| }t| �s�t| �j|�j | j�S Y nX t| �j|�S )Nr| zBNon-vectorized DateOffset being applied to Series or DatetimeIndex�O)r� r� � _add_offsetr� r� r r4 r� r5 rp Z _apply_arrayrV r� �NotImplementedError�warnings�warnr r� r� r� r� )rL r� rM rN r6 r6 r7 r� � s$
zDatetimeArray._add_offsetc C sx t |ttjf�st�|tk s t�t|�}|tkr8| t S | j|�sJtd��| j }t
||j | j d�}| j
|�}|jd�S )NzBTimestamp subtraction must have the same timezones or no timezones)r� ztimedelta64[ns])r r r� r� r� r
r r� r� r@ r. r� r� rK r� )rL r� r} rN r6 r6 r7 �_sub_datetimelike_scalar� s
z&DatetimeArray._sub_datetimelike_scalarc C s t j| j| j�S )z�
Convert to an i8 (unix-like nanosecond timestamp) representation
while keeping the local timezone and not using UTC.
This is used to calculate time-of-day information as if the timestamps
were timezone-naive.
)r �tz_convert_from_utcr@ r5 )rL r6 r6 r7 rB � s z DatetimeArray._local_timestampsc C s8 t j|�}| jdkr td��t|�}| j| j|| jd�S )a
Convert tz-aware Datetime Array/Index from one time zone to another.
Parameters
----------
tz : str, pytz.timezone, dateutil.tz.tzfile or None
Time zone for time. Corresponding timestamps would be converted
to this time zone of the Datetime Array/Index. A `tz` of None will
convert to UTC and remove the timezone information.
Returns
-------
Array or Index
Raises
------
TypeError
If Datetime Array/Index is tz-naive.
See Also
--------
DatetimeIndex.tz : A timezone that has a variable offset from UTC.
DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a
given time zone, or remove timezone from a tz-aware DatetimeIndex.
Examples
--------
With the `tz` parameter, we can change the DatetimeIndex
to other time zones:
>>> dti = pd.date_range(start='2014-08-01 09:00',
... freq='H', periods=3, tz='Europe/Berlin')
>>> dti
DatetimeIndex(['2014-08-01 09:00:00+02:00',
'2014-08-01 10:00:00+02:00',
'2014-08-01 11:00:00+02:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='H')
>>> dti.tz_convert('US/Central')
DatetimeIndex(['2014-08-01 02:00:00-05:00',
'2014-08-01 03:00:00-05:00',
'2014-08-01 04:00:00-05:00'],
dtype='datetime64[ns, US/Central]', freq='H')
With the ``tz=None``, we can remove the timezone (after converting
to UTC if necessary):
>>> dti = pd.date_range(start='2014-08-01 09:00', freq='H',
... periods=3, tz='Europe/Berlin')
>>> dti
DatetimeIndex(['2014-08-01 09:00:00+02:00',
'2014-08-01 10:00:00+02:00',
'2014-08-01 11:00:00+02:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='H')
>>> dti.tz_convert(None)
DatetimeIndex(['2014-08-01 07:00:00',
'2014-08-01 08:00:00',
'2014-08-01 09:00:00'],
dtype='datetime64[ns]', freq='H')
Nz?Cannot convert tz-naive timestamps, use tz_localize to localize)r� rE )r � maybe_get_tzr5 r� r8 r� r@ rE )rL r5 r� r6 r6 r7 rq � s @
zDatetimeArray.tz_convertc C s� d }||kr t |t� r td��| jdk rN|dkrDtj| j| j�}qltd��n tj |�}tj
| j|||d�}|j t �}t
|�}d}tj|�s�t| �d kr�t|d
� r�| j}n|dkr�| jdkr�| j}| j|||d �S )
a�
Localize tz-naive Datetime Array/Index to tz-aware
Datetime Array/Index.
This method takes a time zone (tz) naive Datetime Array/Index object
and makes this time zone aware. It does not move the time to another
time zone.
Time zone localization helps to switch from time zone aware to time
zone unaware objects.
Parameters
----------
tz : str, pytz.timezone, dateutil.tz.tzfile or None
Time zone to convert timestamps to. Passing ``None`` will
remove the time zone information preserving local time.
ambiguous : 'infer', 'NaT', bool array, 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 an AmbiguousTimeError if there are ambiguous
times.
nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, default 'raise'
A nonexistent time does not exist in a particular timezone
where clocks moved forward due to DST.
- 'shift_forward' will shift the nonexistent time forward to the
closest existing time
- 'shift_backward' will shift the nonexistent time backward to the
closest existing time
- 'NaT' will return NaT where there are nonexistent times
- timedelta objects will shift nonexistent times by the timedelta
- 'raise' will raise an NonExistentTimeError if there are
nonexistent times.
.. versionadded:: 0.24.0
Returns
-------
Same type as self
Array/Index converted to the specified time zone.
Raises
------
TypeError
If the Datetime Array/Index is tz-aware and tz is not None.
See Also
--------
DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from
one time zone to another.
Examples
--------
>>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3)
>>> tz_naive
DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
'2018-03-03 09:00:00'],
dtype='datetime64[ns]', freq='D')
Localize DatetimeIndex in US/Eastern time zone:
>>> tz_aware = tz_naive.tz_localize(tz='US/Eastern')
>>> tz_aware
DatetimeIndex(['2018-03-01 09:00:00-05:00',
'2018-03-02 09:00:00-05:00',
'2018-03-03 09:00:00-05:00'],
dtype='datetime64[ns, US/Eastern]', freq=None)
With the ``tz=None``, we can remove the time zone information
while keeping the local time (not converted to UTC):
>>> tz_aware.tz_localize(None)
DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
'2018-03-03 09:00:00'],
dtype='datetime64[ns]', freq=None)
Be careful with DST changes. When there is sequential data, pandas can
infer the DST time:
>>> s = pd.to_datetime(pd.Series(['2018-10-28 01:30:00',
... '2018-10-28 02:00:00',
... '2018-10-28 02:30:00',
... '2018-10-28 02:00:00',
... '2018-10-28 02:30:00',
... '2018-10-28 03:00:00',
... '2018-10-28 03:30:00']))
>>> s.dt.tz_localize('CET', ambiguous='infer')
0 2018-10-28 01:30:00+02:00
1 2018-10-28 02:00:00+02:00
2 2018-10-28 02:30:00+02:00
3 2018-10-28 02:00:00+01:00
4 2018-10-28 02:30:00+01:00
5 2018-10-28 03:00:00+01:00
6 2018-10-28 03:30:00+01:00
dtype: datetime64[ns, CET]
In some cases, inferring the DST is impossible. In such cases, you can
pass an ndarray to the ambiguous parameter to set the DST explicitly
>>> s = pd.to_datetime(pd.Series(['2018-10-28 01:20:00',
... '2018-10-28 02:36:00',
... '2018-10-28 03:46:00']))
>>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False]))
0 2018-10-28 01:20:00+02:00
1 2018-10-28 02:36:00+02:00
2 2018-10-28 03:46:00+01:00
dtype: datetime64[ns, CET]
If the DST transition causes nonexistent times, you can shift these
dates forward or backwards with a timedelta object or `'shift_forward'`
or `'shift_backwards'`.
>>> s = pd.to_datetime(pd.Series(['2015-03-29 02:30:00',
... '2015-03-29 03:30:00']))
>>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
0 2015-03-29 03:00:00+02:00
1 2015-03-29 03:30:00+02:00
dtype: datetime64[ns, Europe/Warsaw]
>>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
0 2015-03-29 01:59:59.999999999+01:00
1 2015-03-29 03:30:00+02:00
dtype: datetime64[ns, Europe/Warsaw]
>>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))
0 2015-03-29 03:30:00+02:00
1 2015-03-29 03:30:00+02:00
dtype: datetime64[ns, Europe/Warsaw]
r� r
�
shift_forward�shift_backwardzoThe nonexistent argument must be one of 'raise', 'NaT', 'shift_forward', 'shift_backward' or a timedelta objectNz,Already tz-aware, use tz_convert to convert.)r� r� r{ r )r� rE )r� r
r� r� )r r r� r5 r r� r@ r� r r� r� r� r r8 rA r� r- rE r� )rL r5 r� r� Znonexistent_optionsZ new_datesr� rE r6 r6 r7 rp * s*
$zDatetimeArray.tz_localizec C s t | j| jd�S )z�
Return Datetime Array/Index as object ndarray of datetime.datetime
objects.
Returns
-------
datetimes : ndarray
)r5 )r r@ r5 )rL r6 r6 r7 �
to_pydatetime� s zDatetimeArray.to_pydatetimec C s( t | j| j�}t| �|�jd�j| j�S )a�
Convert times to midnight.
The time component of the date-time is converted to midnight i.e.
00:00:00. This is useful in cases, when the time does not matter.
Length is unaltered. The timezones are unaffected.
This method is available on Series with datetime values under
the ``.dt`` accessor, and directly on Datetime Array/Index.
Returns
-------
DatetimeArray, DatetimeIndex or Series
The same type as the original data. Series will have the same
name and index. DatetimeIndex will have the same name.
See Also
--------
floor : Floor the datetimes to the specified freq.
ceil : Ceil the datetimes to the specified freq.
round : Round the datetimes to the specified freq.
Examples
--------
>>> idx = pd.date_range(start='2014-08-01 10:00', freq='H',
... periods=3, tz='Asia/Calcutta')
>>> idx
DatetimeIndex(['2014-08-01 10:00:00+05:30',
'2014-08-01 11:00:00+05:30',
'2014-08-01 12:00:00+05:30'],
dtype='datetime64[ns, Asia/Calcutta]', freq='H')
>>> idx.normalize()
DatetimeIndex(['2014-08-01 00:00:00+05:30',
'2014-08-01 00:00:00+05:30',
'2014-08-01 00:00:00+05:30'],
dtype='datetime64[ns, Asia/Calcutta]', freq=None)
r~ )r r@ r5 r� Z
_with_freqrp )rL r� r6 r6 r7 rr � s &zDatetimeArray.normalizec C sr ddl m} | jdk r"tjdt� |dkr^| jp4| j}|dkrFtd��t |�}|dkrZ|}|}|j
| j || jd�S )a�
Cast to PeriodArray/Index at a particular frequency.
Converts DatetimeArray/Index to PeriodArray/Index.
Parameters
----------
freq : str or Offset, optional
One of pandas' :ref:`offset strings <timeseries.offset_aliases>`
or an Offset object. Will be inferred by default.
Returns
-------
PeriodArray/Index
Raises
------
ValueError
When converting a DatetimeArray/Index with non-regular values,
so that a frequency cannot be inferred.
See Also
--------
PeriodIndex: Immutable ndarray holding ordinal values.
DatetimeIndex.to_pydatetime: Return DatetimeIndex as object.
Examples
--------
>>> df = pd.DataFrame({"y": [1, 2, 3]},
... index=pd.to_datetime(["2000-03-31 00:00:00",
... "2000-05-31 00:00:00",
... "2000-08-31 00:00:00"]))
>>> df.index.to_period("M")
PeriodIndex(['2000-03', '2000-05', '2000-08'],
dtype='period[M]', freq='M')
Infer the daily frequency
>>> idx = pd.date_range("2017-01-01", periods=2)
>>> idx.to_period()
PeriodIndex(['2017-01-01', '2017-01-02'],
dtype='period[D]', freq='D')
r )� PeriodArrayNzNConverting to PeriodArray/Index representation will drop timezone information.z8You must pass a freq argument as current index has none.)r5 ) �pandas.core.arraysr� r5 r� r� � UserWarningrH r� r� r1 Z_from_datetime64r� )rL rE r� �resr6 r6 r7 ro s ,
zDatetimeArray.to_periodc C sD t jdtdd� ddlm} | j| j|�j� j }|jd�}||�S )a5
Calculate TimedeltaArray of difference between index
values and index converted to PeriodArray at specified
freq. Used for vectorized offsets.
Parameters
----------
freq : Period frequency
Returns
-------
TimedeltaArray/Index
z�to_perioddelta is deprecated and will be removed in a future version. Use `dtindex - dtindex.to_period(freq).to_timestamp()` insteadr� )�
stacklevelr )�TimedeltaArrayzm8[ns]) r� r� �
FutureWarningZ pandas.core.arrays.timedeltasr� r@ ro Z to_timestampr� )rL rE r� Zi8deltaZm8deltar6 r6 r7 �to_perioddeltaY s
z DatetimeArray.to_perioddeltac C sJ | j dk r"tj| j � r"| j� }n| j}tj|d|d�}| j|dd�}|S )a�
Return the month names of the DateTimeIndex with specified locale.
.. versionadded:: 0.23.0
Parameters
----------
locale : str, optional
Locale determining the language in which to return the month name.
Default is English locale.
Returns
-------
Index
Index of month names.
Examples
--------
>>> idx = pd.date_range(start='2018-01', freq='M', periods=3)
>>> idx
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
dtype='datetime64[ns]', freq='M')
>>> idx.month_name()
Index(['January', 'February', 'March'], dtype='object')
Nrw )�locale)r= )r5 r rA rB r@ r rJ rK )rL r� rM rN r6 r6 r7 rw x s
zDatetimeArray.month_namec C sJ | j dk r"tj| j � r"| j� }n| j}tj|d|d�}| j|dd�}|S )a�
Return the day names of the DateTimeIndex with specified locale.
.. versionadded:: 0.23.0
Parameters
----------
locale : str, optional
Locale determining the language in which to return the day name.
Default is English locale.
Returns
-------
Index
Index of day names.
Examples
--------
>>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3)
>>> idx
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],
dtype='datetime64[ns]', freq='D')
>>> idx.day_name()
Index(['Monday', 'Tuesday', 'Wednesday'], dtype='object')
Nrx )r� )r= )r5 r rA rB r@ r rJ rK )rL r� rM rN r6 r6 r7 rx � s
zDatetimeArray.day_namec C s4 | j dk r"tj| j � r"| j� }n| j}t|dd�S )zX
Returns numpy array of datetime.time. The time part of the Timestamps.
Nr )r� )r5 r rA rB r@ r )rL �
timestampsr6 r6 r7 r � s
zDatetimeArray.timec C s t | j| jdd�S )z�
Returns numpy array of datetime.time also containing timezone
information. The time part of the Timestamps.
r )r� )r r@ r5 )rL r6 r6 r7 rn � s zDatetimeArray.timetzc C s4 | j dk r"tj| j � r"| j� }n| j}t|dd�S )z�
Returns numpy array of python datetime.date objects (namely, the date
part of Timestamps without timezone information).
Nrm )r� )r5 r rA rB r@ r )rL r� r6 r6 r7 rm � s
zDatetimeArray.datec C sh ddl m} | jdk r.tj| j� r.| j� }n| j}tj|�}||dddgdd�}| j rdd|j
| j < |S ) aL
Returns a DataFrame with the year, week, and day calculated according to
the ISO 8601 standard.
.. versionadded:: 1.1.0
Returns
-------
DataFrame
with columns year, week and day
See Also
--------
Timestamp.isocalendar
datetime.date.isocalendar
Examples
--------
>>> idx = pd.date_range(start='2019-12-29', freq='D', periods=4)
>>> idx.isocalendar()
year week day
2019-12-29 2019 52 7
2019-12-30 2020 1 1
2019-12-31 2020 1 2
2020-01-01 2020 1 3
>>> idx.isocalendar().week
2019-12-29 52
2019-12-30 1
2019-12-31 1
2020-01-01 1
Freq: D, Name: week, dtype: UInt32
r )� DataFrameNr^ rd r_ ZUInt32)�columnsr� ) Zpandasr� r5 r rA rB r@ r Zbuild_isocalendar_sarrayr� Zilocr� )rL r� rM ZsarrayZiso_calendar_dfr6 r6 r7 � isocalendar� s !
zDatetimeArray.isocalendarc C s< t jdtdd� | j� j}|jr0|jdtjd�S |jdd�S )z�
The week ordinal of the year.
.. deprecated:: 1.1.0
weekofyear and week have been deprecated.
Please use DatetimeIndex.isocalendar().week instead.
z�weekofyear and week have been deprecated, please use DatetimeIndex.isocalendar().week instead, which returns a Series. To exactly reproduce the behavior of week and weekofyear and return an Index, you may call pd.Int64Index(idx.isocalendar().week)r� )r� r> )r� Zna_valuer� )r� ) r� r� r� r rd Zhasnans�to_numpyr� �nan)rL Z week_seriesr6 r6 r7 rc s
zDatetimeArray.weekofyear�Ya�
The year of the datetime.
Examples
--------
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="Y")
... )
>>> datetime_series
0 2000-12-31
1 2001-12-31
2 2002-12-31
dtype: datetime64[ns]
>>> datetime_series.dt.year
0 2000
1 2001
2 2002
dtype: int64
�Ma�
The month as January=1, December=12.
Examples
--------
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="M")
... )
>>> datetime_series
0 2000-01-31
1 2000-02-29
2 2000-03-31
dtype: datetime64[ns]
>>> datetime_series.dt.month
0 1
1 2
2 3
dtype: int64
�Da�
The day of the datetime.
Examples
--------
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="D")
... )
>>> datetime_series
0 2000-01-01
1 2000-01-02
2 2000-01-03
dtype: datetime64[ns]
>>> datetime_series.dt.day
0 1
1 2
2 3
dtype: int64
�ha�
The hours of the datetime.
Examples
--------
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="h")
... )
>>> datetime_series
0 2000-01-01 00:00:00
1 2000-01-01 01:00:00
2 2000-01-01 02:00:00
dtype: datetime64[ns]
>>> datetime_series.dt.hour
0 0
1 1
2 2
dtype: int64
�ma�
The minutes of the datetime.
Examples
--------
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="T")
... )
>>> datetime_series
0 2000-01-01 00:00:00
1 2000-01-01 00:01:00
2 2000-01-01 00:02:00
dtype: datetime64[ns]
>>> datetime_series.dt.minute
0 0
1 1
2 2
dtype: int64
�sa�
The seconds of the datetime.
Examples
--------
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="s")
... )
>>> datetime_series
0 2000-01-01 00:00:00
1 2000-01-01 00:00:01
2 2000-01-01 00:00:02
dtype: datetime64[ns]
>>> datetime_series.dt.second
0 0
1 1
2 2
dtype: int64
�usa�
The microseconds of the datetime.
Examples
--------
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="us")
... )
>>> datetime_series
0 2000-01-01 00:00:00.000000
1 2000-01-01 00:00:00.000001
2 2000-01-01 00:00:00.000002
dtype: datetime64[ns]
>>> datetime_series.dt.microsecond
0 0
1 1
2 2
dtype: int64
�nsa�
The nanoseconds of the datetime.
Examples
--------
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="ns")
... )
>>> datetime_series
0 2000-01-01 00:00:00.000000000
1 2000-01-01 00:00:00.000000001
2 2000-01-01 00:00:00.000000002
dtype: datetime64[ns]
>>> datetime_series.dt.nanosecond
0 0
1 1
2 2
dtype: int64
a�
The day of the week with Monday=0, Sunday=6.
Return the day of the week. It is assumed the week starts on
Monday, which is denoted by 0 and ends on Sunday which is denoted
by 6. This method is available on both Series with datetime
values (using the `dt` accessor) or DatetimeIndex.
Returns
-------
Series or Index
Containing integers indicating the day number.
See Also
--------
Series.dt.dayofweek : Alias.
Series.dt.weekday : Alias.
Series.dt.day_name : Returns the name of the day of the week.
Examples
--------
>>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()
>>> s.dt.dayofweek
2016-12-31 5
2017-01-01 6
2017-01-02 0
2017-01-03 1
2017-01-04 2
2017-01-05 3
2017-01-06 4
2017-01-07 5
2017-01-08 6
Freq: D, dtype: int64
ZdowZdoyz.
The ordinal day of the year.
�qz*
The quarter of the date.
Zdimz2
The number of days in the month.
a�
Indicates whether the date is the {first_or_last} day of the month.
Returns
-------
Series or array
For Series, returns a Series with boolean values.
For DatetimeIndex, returns a boolean array.
See Also
--------
is_month_start : Return a boolean indicating whether the date
is the first day of the month.
is_month_end : Return a boolean indicating whether the date
is the last day of the month.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor, and directly on DatetimeIndex.
>>> s = pd.Series(pd.date_range("2018-02-27", periods=3))
>>> s
0 2018-02-27
1 2018-02-28
2 2018-03-01
dtype: datetime64[ns]
>>> s.dt.is_month_start
0 False
1 False
2 True
dtype: bool
>>> s.dt.is_month_end
0 False
1 True
2 False
dtype: bool
>>> idx = pd.date_range("2018-02-27", periods=3)
>>> idx.is_month_start
array([False, False, True])
>>> idx.is_month_end
array([False, True, False])
�first)Z
first_or_last�lasta�
Indicator for whether the date is the first day of a quarter.
Returns
-------
is_quarter_start : Series or DatetimeIndex
The same type as the original data with boolean values. Series will
have the same name and index. DatetimeIndex will have the same
name.
See Also
--------
quarter : Return the quarter of the date.
is_quarter_end : Similar property for indicating the quarter start.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor, and directly on DatetimeIndex.
>>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30",
... periods=4)})
>>> df.assign(quarter=df.dates.dt.quarter,
... is_quarter_start=df.dates.dt.is_quarter_start)
dates quarter is_quarter_start
0 2017-03-30 1 False
1 2017-03-31 1 False
2 2017-04-01 2 True
3 2017-04-02 2 False
>>> idx = pd.date_range('2017-03-30', periods=4)
>>> idx
DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],
dtype='datetime64[ns]', freq='D')
>>> idx.is_quarter_start
array([False, False, True, False])
a�
Indicator for whether the date is the last day of a quarter.
Returns
-------
is_quarter_end : Series or DatetimeIndex
The same type as the original data with boolean values. Series will
have the same name and index. DatetimeIndex will have the same
name.
See Also
--------
quarter : Return the quarter of the date.
is_quarter_start : Similar property indicating the quarter start.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor, and directly on DatetimeIndex.
>>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30",
... periods=4)})
>>> df.assign(quarter=df.dates.dt.quarter,
... is_quarter_end=df.dates.dt.is_quarter_end)
dates quarter is_quarter_end
0 2017-03-30 1 False
1 2017-03-31 1 True
2 2017-04-01 2 False
3 2017-04-02 2 False
>>> idx = pd.date_range('2017-03-30', periods=4)
>>> idx
DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],
dtype='datetime64[ns]', freq='D')
>>> idx.is_quarter_end
array([False, True, False, False])
a~
Indicate whether the date is the first day of a year.
Returns
-------
Series or DatetimeIndex
The same type as the original data with boolean values. Series will
have the same name and index. DatetimeIndex will have the same
name.
See Also
--------
is_year_end : Similar property indicating the last day of the year.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor, and directly on DatetimeIndex.
>>> dates = pd.Series(pd.date_range("2017-12-30", periods=3))
>>> dates
0 2017-12-30
1 2017-12-31
2 2018-01-01
dtype: datetime64[ns]
>>> dates.dt.is_year_start
0 False
1 False
2 True
dtype: bool
>>> idx = pd.date_range("2017-12-30", periods=3)
>>> idx
DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],
dtype='datetime64[ns]', freq='D')
>>> idx.is_year_start
array([False, False, True])
a{
Indicate whether the date is the last day of the year.
Returns
-------
Series or DatetimeIndex
The same type as the original data with boolean values. Series will
have the same name and index. DatetimeIndex will have the same
name.
See Also
--------
is_year_start : Similar property indicating the start of the year.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor, and directly on DatetimeIndex.
>>> dates = pd.Series(pd.date_range("2017-12-30", periods=3))
>>> dates
0 2017-12-30
1 2017-12-31
2 2018-01-01
dtype: datetime64[ns]
>>> dates.dt.is_year_end
0 False
1 True
2 False
dtype: bool
>>> idx = pd.date_range("2017-12-30", periods=3)
>>> idx
DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],
dtype='datetime64[ns]', freq='D')
>>> idx.is_year_end
array([False, True, False])
a�
Boolean indicator if the date belongs to a leap year.
A leap year is a year, which has 366 days (instead of 365) including
29th of February as an intercalary day.
Leap years are years which are multiples of four with the exception
of years divisible by 100 but not by 400.
Returns
-------
Series or ndarray
Booleans indicating if dates belong to a leap year.
Examples
--------
This method is available on Series with datetime values under
the ``.dt`` accessor, and directly on DatetimeIndex.
>>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="Y")
>>> idx
DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'],
dtype='datetime64[ns]', freq='A-DEC')
>>> idx.is_leap_year
array([ True, False, False])
>>> dates_series = pd.Series(idx)
>>> dates_series
0 2012-12-31
1 2013-12-31
2 2014-12-31
dtype: datetime64[ns]
>>> dates_series.dt.is_leap_year
0 True
1 False
2 False
dtype: bool
c C s� t j| j�}t j| j�}t j| j�}|dk }|| d8 < || d7 < |t jd| d d � d| t j|d � t j|d � t j|d
� d | j| jd | j d
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Convert Datetime Array to float64 ndarray of Julian Dates.
0 Julian date is noon January 1, 4713 BC.
https://en.wikipedia.org/wiki/Julian_day
r� r{ r; � i� � im � �d i� g � C:Ag N@g �@g ��.Ag e��Ag 8@) r� �asarrayr^ r< r_ Zfixru r` ra rb rk rl )rL r^ r< r_ Ztestarrr6 r6 r7 �to_julian_date+ s N4z DatetimeArray.to_julian_date)NFr� r� N)F)N)T)r
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_other_opsZ_datetimelike_opsZ_datetimelike_methodsZ__array_priority__r r� r) r� ry r r� � classmethodr� r r� r� r� r� r� �boolr� r� rS r� r5 �setterr r� r r� r� r� r� r� r� r� r� r� r� r� rB rq rp r� rr ro r� rw rx r rn rm r rc rd rU r^ r< r_ r` ra rb rk rl Z_dayofweek_docrf re rg rh ri rj Z
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Parameters
----------
data : list-like
dtype : dtype, str, or None, default None
copy : bool, default False
tz : tzinfo, str, or None, default None
dayfirst : bool, default False
yearfirst : bool, default False
ambiguous : str, bool, or arraylike, default 'raise'
See pandas._libs.tslibs.tzconversion.tz_localize_to_utc.
Returns
-------
result : numpy.ndarray
The sequence converted to a numpy array with dtype ``datetime64[ns]``.
tz : tzinfo or None
Either the user-provided tzinfo or one inferred from the data.
inferred_freq : Tick or None
The inferred frequency of the sequence.
Raises
------
TypeError : PeriodDType data is passed
Nr� r FrE r{ z0Cannot create a DatetimeArray from a MultiIndex.)Zskipna�integer)r� r� r� r} )r� )r� zM8[ns])-r� r r� r� r� r� �listr r r, r� r+ r rE �validate_tz_from_dtyper* Znlevelsr� r� �maybe_convert_dtyper� r# r& r% r Z infer_dtyper� r� �objects_to_datetime64ns�_maybe_infer_tzr� r r5 r r r
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Convert data to array of timestamps.
Parameters
----------
data : np.ndarray[object]
dayfirst : bool
yearfirst : bool
utc : bool, default False
Whether to convert timezone-aware timestamps to UTC.
errors : {'raise', 'ignore', 'coerce'}
allow_object : bool
Whether to return an object-dtype ndarray instead of raising if the
data contains more than one timezone.
Returns
-------
result : ndarray
np.int64 dtype if returned values represent UTC timestamps
np.datetime64[ns] if returned values represent wall times
object if mixed timezones
inferred_tz : tzinfo or None
Raises
------
ValueError : if data cannot be converted to datetimes
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Convert data based on dtype conventions, issuing deprecation warnings
or errors where appropriate.
Parameters
----------
data : np.ndarray or pd.Index
copy : bool
Returns
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data : np.ndarray or pd.Index
copy : bool
Raises
------
TypeError : PeriodDType data is passed
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If a timezone is inferred from data, check that it is compatible with
the user-provided timezone, if any.
Parameters
----------
tz : tzinfo or None
inferred_tz : tzinfo or None
Returns
-------
tz : tzinfo or None
Raises
------
TypeError : if both timezones are present but do not match
Nzdata is already tz-aware z , unable to set specified tz: )r r� r� )r5 r! r6 r6 r7 r g s r c C sf | dk rbt | �} t| tjd��r,d}t|��t| tj�r@| tksRt| tjtf� rbtd| � d���| S )a�
Check that a dtype, if passed, represents either a numpy datetime64[ns]
dtype or a pandas DatetimeTZDtype.
Parameters
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dtype : object
Returns
-------
dtype : None, numpy.dtype, or DatetimeTZDtype
Raises
------
ValueError : invalid dtype
Notes
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Unlike validate_tz_from_dtype, this does _not_ allow non-existent
tz errors to go through
NZM8zhPassing in 'datetime64' dtype with no precision is not allowed. Please pass in 'datetime64[ns]' instead.z Unexpected value for 'dtype': 'z0'. Must be 'datetime64[ns]' or DatetimeTZDtype'.)r( r r� r� r� r r r) )r� r� r6 r6 r7 r� � s r� )r5 r� c C s� | dk r�t | t�r6ytj| �} W n tk
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If the given dtype is a DatetimeTZDtype, extract the implied
tzinfo object from it and check that it does not conflict with the given
tz.
Parameters
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dtype : dtype, str
tz : None, tzinfo
Returns
-------
tz : consensus tzinfo
Raises
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ValueError : on tzinfo mismatch
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If a timezone is not explicitly given via `tz`, see if one can
be inferred from the `start` and `end` endpoints. If more than one
of these inputs provides a timezone, require that they all agree.
Parameters
----------
start : Timestamp
end : Timestamp
tz : tzinfo or None
Returns
-------
tz : tzinfo or None
Raises
------
TypeError : if start and end timezones do not agree
z>Start and end cannot both be tz-aware with different timezonesNz0Inferred time zone not equal to passed time zone)r Z infer_tzinfor� r� r� r� )r9 r: r5 r! �errr6 r6 r7 r� � s
r� )r9 r: rr c C sb d}| d k r.|r | j � } d}n|o,| j� tk}|d k rX|rH|j � }d}n|oV|j� tk}| ||fS )NT)rr r � _midnight)r9 r: rr r� r6 r6 r7 r� s r� c C sV |dkrR|dk rR|dkr |nd}||dd�}t |t�s>|dkrF||d< | jf |�} | S )a
Localize a start or end Timestamp to the timezone of the corresponding
start or end Timestamp
Parameters
----------
ts : start or end Timestamp to potentially localize
is_none : argument that should be None
is_not_none : argument that should not be None
freq : Tick, DateOffset, or None
tz : str, timezone object or None
ambiguous: str, localization behavior for ambiguous times
nonexistent: str, localization behavior for nonexistent times
Returns
-------
ts : Timestamp
Nr~ F)r� r� r5 r5 )r r4 rp )�tsZis_noneZ is_not_nonerE r5 r� r� Z
localize_argsr6 r6 r7 r� ! s r� c c sj t |�}t| �} | tk r | nd} t|�}|tk r4|nd}| rT|j| � rT|j| �} n|rn|j|� rn|j|�}|dkr�|| k r�|jdkr�d}d}|dkr�| |d | }| dkr�||d | } | }|jdk�rx�||k�r|V ||kr�P |j|�}||k�rtd|� d���|}q�W nLxJ||k�rd|V ||k�r8P |j|�}||k�r\td|� d���|}�q W dS )a�
Generates a sequence of dates corresponding to the specified time
offset. Similar to dateutil.rrule except uses pandas DateOffset
objects to represent time increments.
Parameters
----------
start : datetime, (default None)
end : datetime, (default None)
periods : int, (default None)
offset : DateOffset, (default BDay())
Notes
-----
* This method is faster for generating weekdays than dateutil.rrule
* At least two of (start, end, periods) must be specified.
* If both start and end are specified, the returned dates will
satisfy start <= date <= end.
Returns
-------
dates : generator object
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