Файловый менеджер - Редактировать - /home/digitalm/venv/lib/python3.7/site-packages/pandas/core/arrays/__pycache__/datetimes.cpython-37.pyc
Назад
B �5�g�E � @ s� d dl mZ d dlmZmZmZmZ d dlmZmZm Z d dl Z d dl Z d dl mZmZ d dlmZ d dlmZmZmZmZmZmZmZmZmZm Z m Z m Z m Z m Z m!Z! d dl"m#Z# d d l$m%Z% d d l&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7 d d l8m9Z9 d d l:m;Z; d d l<m=Z= d dl>m?Z? d dl@mAZAmBZC d dlDmEZE d dlFmGZG d dlHmI mJZK d dlLmMZM d dlNmOZO d dlPmQZQmRZRmSZS e�r�d dlmTZT d dlUmVZV d dl@mWZWmXZX ed d �ZYdd� ZZdIdd�Z[G d d � d eCj\eCj]�Z^e dJd d d d!�d"d#��Z_e dKd$d d%d!�d&d#��Z_dLd d d%d!�d(d#�Z_dMd'd'd'd*�d d d d*�d+d,�Z`dNd-d d d d.�d/d0�Zad d1�d2d3�Zbd4d4d4d5�d6d7�Zcd8d9� Zdd4d4d:�d;d<�Zed=d=d4d4d>�d?d@�ZfdAdAd dB�dCdD�ZgdEdF� ZhdddeQ� fdGdH�ZidS )O� )� annotations)�datetime�time� timedelta�tzinfo)� TYPE_CHECKING�cast�overloadN)�lib�tslib)� NDArrayBacked)� BaseOffset�NaT�NaTType� Resolution� Timestamp� conversion�fields�get_resolution�iNaT�ints_to_pydatetime�is_date_array_normalized�normalize_i8_timestamps� timezones� to_offset� tzconversion)�PerformanceWarning)�astype_dt64_to_dt64tz)� DT64NS_DTYPE� INT64_DTYPE� is_bool_dtype�is_categorical_dtype�is_datetime64_any_dtype�is_datetime64_dtype�is_datetime64_ns_dtype�is_datetime64tz_dtype�is_dtype_equal�is_extension_array_dtype�is_float_dtype�is_object_dtype�is_period_dtype� is_sparse�is_string_dtype�is_timedelta64_dtype� pandas_dtype)�DatetimeTZDtype)� ABCMultiIndex)�isna)�checked_add_with_arr)�ExtensionArray� datetimelike)�generate_regular_range)� IntegerArray)� extract_array)�get_period_alias)�BDay�Day�Tick)�Literal)� DataFrame)� PeriodArray�TimedeltaArrayc C s | dkr t S t| d�S dS )z� Return a datetime64[ns] dtype appropriate for the given timezone. Parameters ---------- tz : tzinfo or None Returns ------- np.dtype or Datetime64TZDType N)�tz)r r/ )r@ � rA �R/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py� tz_to_dtype_ s rC c s � fdd�}| |_ ||_t|�S )Nc s� | � � }� | jkrh� �d�rX| j}d}|rD|j}|�d|�dd��}t�|� | j|�}n t� |� �}|S � | j kr�t� |� �}| j |d d�}n t� |� �}| j |d dd�}|S )N)�start�end� Z startingMonth�month)� fill_value�float64)rH �convert) �_local_timestamps� _bool_ops�endswith�freq�kwds�getr Zget_start_end_field�freqstrZget_date_field� _object_ops�get_date_name_field�_maybe_mask_results)�self�valuesrN Zmonth_kwrO �result)�fieldrA rB �fr s& z_field_accessor.<locals>.f)�__name__�__doc__�property)�namerX Z docstringrY rA )rX rB �_field_accessorq s r^ c s� e Zd ZU dZdZeZeej fZ e Z dZ dddddd d gZd ed < d dgZd ed< ddddddddddddd d d d d d!gZd ed"< d#d$d%gZd ed&< ee e e Zd ed'< d(d)d*d+d,d-d.d/d0d1g Zd ed2< d3Zd4ed5< d6Zed6d7fd8d9�d:d;�Zed6efd<d=d d>�� fd?d@� �Zed6d7dA�d8d9�dBdC��Zed6d7d6e j d7d7dDfd8d8d8dE�dFdG��Z ed�dHdI��Z d�d8dJdK�dLdM�Z dNdO�dPdQ�Z!d�d8dR�dSdT�Z"dNdO�dUdV�Z#e$d4dO�dWdX��Z%e$dYdO�dZd[��Z&e&j'd\d[� �Z&e$dYdO�d]d^��Z(e$d8dO�d_d`��Z)e$dadO�dbdc��Z*d�d<dO�� fddde� Z+dfdg� Z,d�d8d9�didj�Z-e.j/d�d<dO�dldm��Z0d8dO�dndo�Z1dpdO�dqdr�Z2dsdt� Z3d dO�dudv�Z4dwdx� Z5d<dO�dydz�Z6d dO�d{d|�Z7e.j/d�d dO�d}d~��Z8d<dO�dd��Z9d dO�d�d��Z:e.j/d�d�dO�d�d���Z;d�dO�d�d��Z<d�d�d��Z=d�d�d��Z>e$d<dO�d�d���Z?e$d<dO�d�d���Z@e$d<dO�d�d���ZAd�dO�d�d��ZBe$d�d�� �ZCeCZDeEdd�d��ZFeEdd�d��ZGeEdd�d��ZHeEdd�d��ZIeEdd�d��ZJeEdd�d��ZKeEd d�d��ZLeEd!d�d��ZMd�ZNeEdd�eN�ZOeOZPeOZQeEdd�d��ZReRZSeEd d�d��ZTeEd d�d��ZUeUZVd�ZWeEddeWjXd�d���ZYeEddeWjXd�d���ZZeEddd��Z[eEddd��Z\eEddd��Z]eEd d d��Z^eEd d d��Z_d<dO�d�d��Z`d�d�d8d8d��d�d��Za� ZbS )�� DatetimeArraya Pandas ExtensionArray for tz-naive or tz-aware datetime data. .. 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 Z datetimearray)r � datetime64�date�is_month_start� is_month_end�is_quarter_start�is_quarter_end� is_year_start� is_year_end� is_leap_yearz list[str]rL rN r@ rR �yearrG �day�hour�minute�second� weekofyear�week�weekday� dayofweek� day_of_week� dayofyear� day_of_year�quarter� days_in_month� daysinmonth� microsecond� nanosecond� _field_opsra r �timetz� _other_ops�_datetimelike_ops� to_period� tz_localize� tz_convert� normalize�strftime�round�floor�ceil� month_name�day_name�_datetimelike_methodsi� znp.dtype | DatetimeTZDtype�_dtypeNF�bool)�copyc C s� t |dd�}t|t�r$|jdtd�}t|dd �}t|t| ��r�t|dd �}|rf|jd krft|jd�}nB|r�|jr�t � ||j�s�d|� d |j� d �}t |��n |jr�|j }|d kr�|j }|j}t|tj�s�td t|�j� d ���|jd kr�td��|j dk�r|�t�}|j tk�r$td|j � d���t|�}|dk�r>td��|�rL|�� }|�rZt|�}t|dd ��rztt �|j�d�}tj| ||d� || _ |d k�r�|d k �r�t| �� | |� d S )NT)� extract_numpy�int64)�na_value�_freqr@ )r@ z1Timezone of the array and 'dtype' do not match. 'z' != '�'zUnexpected type 'zY'. 'values' must be a DatetimeArray, ndarray, or Series or Index containing one of those.)� � z.Only 1-dimensional input arrays are supported.�i8zBThe dtype of 'values' is incorrect. Must be 'datetime64[ns]'. Got z instead.�inferzTFrequency inference not allowed in DatetimeArray.__init__. Use 'pd.array()' instead.)rV �dtype) r7 � isinstancer6 �to_numpyr �getattr�typer@ r/ r � tz_compare� TypeErrorr� rN �_ndarray�np�ndarray� ValueErrorrZ �ndim�viewr �_validate_dt64_dtyper� r Ztz_standardizer �__init__r� �_validate_frequency)rU rV r� rN r� � inferred_freq�dtz�msgrA rA rB r� � sR zDatetimeArray.__init__z np.ndarrayzBaseOffset | None)rV rN �returnc s6 t |tj�st�|jtks t�t� �||�}||_|S )N) r� r� r� �AssertionErrorr� r �super� _simple_newr� )�clsrV rN r� rW )� __class__rA rB r� C s zDatetimeArray._simple_new)r� r� c C s | j |||d�S )N)r� r� )�_from_sequence_not_strict)r� Zscalarsr� r� rA rA rB �_from_sequenceN s z DatetimeArray._from_sequence�raise)r� �dayfirst� yearfirstc C s� |d k} |t jk r|nd }t�|�\}} t|||||||d�\} }} t�|| | �\}} | r^d }t|�}| j| ||d�} | d kr�|d k r�| j| ||d� n| r�t | j �| _ | S )N)r� r� r@ r� r� � ambiguous)rN r� )r� ) r � no_default�dtlZmaybe_infer_freq�sequence_to_dt64nsZvalidate_inferred_freqrC r� r� r r� r� )r� �datar� r� r@ rN r� r� r� Z explicit_noneZ freq_inferZsubarrr� rW rA rA rB r� R s* z'DatetimeArray._from_sequence_not_strictc C s� t �|�}|d kr2tdd� |||gD ��r2td��t�||||�dkrNtd��t|�}|d k rft|�}|d k rvt|�}|tks�|tkr�td��t � | �\} } t |||�\}}} t |||�}|d k �r|d kr�d n|j } |d kr�d n|j }t || |||||�}t |||||||�}|d k �r0t|t��rN|d k �r:|�d �}|d k �rN|�d �}t|t��rjt||||�}n*t||||d�}tjdd � |D �tjd �}|d k �r�|j n|j }|�d �}| j||t|�d �}|d k �rv|j d k�rvtj|j |||d �}| |�}|d k �r|�|||�j }|d k �rv|�|||�j }nFtj d|j |j |dd �|j }t|�}|j d dd�}| j|d |d �}| �s�t!|��r�|d |k�r�t"t#|dd � �}| �s�t!|��r�|d |k�r�t"t#|d d� �}t|�}| j|j$||d �S )Nc s s | ]}|d kV qd S )NrA )�.0�xrA rA rB � <genexpr>� s z0DatetimeArray._generate_range.<locals>.<genexpr>z1Must provide freq argument if no data is supplied� zVOf the four parameters: start, end, periods, and freq, exactly three must be specifiedz$Neither `start` nor `end` can be NaT)rD rE �periods�offsetc S s g | ] }|j �qS rA )�value)r� r� rA rA rB � <listcomp>� s z1DatetimeArray._generate_range.<locals>.<listcomp>)r� zM8[ns])rN r� )r� � nonexistentr r� F)r� r� ���)%r� Zvalidate_periods�anyr� �comZcount_not_noner r r Zvalidate_endpoints�_maybe_normalize_endpoints�_infer_tz_from_endpointsr@ �_maybe_localize_pointr� r: r r; r5 �generate_ranger� �arrayr� r� r� rC r �tz_localize_to_utc�asi8�asm8Zlinspacer� �astype�lenr r_ r� )r� rD rE r� rN r@ r� r� r� �closedZ left_closedZ right_closed� _normalizedZstart_tzZend_tzrV ZxdrZ_tz�indexZarrr� rA rA rB �_generate_range sn z DatetimeArray._generate_rangez np.datetime64)�setitemr� c C s0 t || j�s |tk r td��| j||d� |jS )Nz 'value' should be a Timestamp.)r� )r� � _scalar_typer r� �_check_compatible_withr� )rU r� r� rA rA rB � _unbox_scalar� s zDatetimeArray._unbox_scalarzTimestamp | NaTType)r� c C s t || jd�S )N)r@ )r r@ )rU r� rA rA rB �_scalar_from_string� s z!DatetimeArray._scalar_from_string)r� c C sH |t kr d S | �|� |rDt�| j|j�sDtd| j� d|j� d���d S )NzTimezones don't match. 'z' != 'r� )r �_assert_tzawareness_compatr r� r@ r� )rU �otherr� rA rA rB r� � s z$DatetimeArray._check_compatible_withc C s& t || jd�}|tk r"|�| j� |S )N)r@ )r r@ r Z _set_freqrN )rU r� �tsrA rA rB � _box_func� s zDatetimeArray._box_funcc C s | j S )a% The dtype for the DatetimeArray. .. warning:: A future version of pandas will change dtype to never be a ``numpy.dtype``. Instead, :attr:`DatetimeArray.dtype` will always be an instance of an ``ExtensionDtype`` subclass. Returns ------- numpy.dtype or DatetimeTZDtype If the values are tz-naive, then ``np.dtype('datetime64[ns]')`` is returned. If the values are tz-aware, then the ``DatetimeTZDtype`` is returned. )r� )rU rA rA rB r� s zDatetimeArray.dtypez tzinfo | Nonec C s t | jdd�S )z� Return timezone, if any. Returns ------- datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None Returns None when the array is tz-naive. r@ N)r� r� )rU rA rA rB r@ " s zDatetimeArray.tzc C s t d��d S )NzNCannot directly set timezone. Use tz_localize() or tz_convert() as appropriate)�AttributeError)rU r� rA rA rB r@ / s c C s | j S )z( Alias for tz attribute )r@ )rU rA rA rB r 7 s zDatetimeArray.tzinfoc C s t | j| j�S )zN Returns True if all of the dates are at midnight ("no time") )r r� r@ )rU rA rA rB � is_normalized> s zDatetimeArray.is_normalizedr c C s t | j| j�S )N)r r� r@ )rU rA rA rB �_resolution_objE s z DatetimeArray._resolution_objc s |d kr| j rt}t� j|d�S )N)r� )r@ �objectr� � __array__)rU r� )r� rA rB r� L s zDatetimeArray.__array__c c s� | j dkr,x�tt| ��D ]}| | V qW n�| j}t| �}d}|| d }t�� �f tjdtd� xPt|�D ]D}|| }t|d | |�}t |||� | j | j dd�}|E dH qlW W dQ R X dS )zt Return an iterator over the boxed values Yields ------ tstamp : Timestamp r� i' �ignore)�category� timestamp)r@ rN �boxN) r� �ranger� r� �warnings�catch_warnings�filterwarnings� FutureWarning�minr r@ rN ) rU �ir� �length� chunksize�chunksZstart_iZend_iZ convertedrA rA rB �__iter__S s zDatetimeArray.__iter__Tc C s� t |�}t|| j�r$|r | �� S | S t|�r<t| ||dd�S | jd krdt|�rd|| jkrd| j� |�S t |�rz| j |j d�S t j� | ||�S )NF)Zvia_utc)rN )r. r& r� r� r$ r r@ r# r� r� r* r~ rN r� ZDatetimeLikeArrayMixin)rU r� r� rA rA rB r� q s zDatetimeArray.astyper c K s, ddl m} || |�}tj| j| j||d�S )Nr )�!get_format_datetime64_from_values)r@ �format�na_rep)Zpandas.io.formats.formatr� r Zformat_array_from_datetimer� r@ )rU r� Z date_format�kwargsr� �fmtrA rA rB �_format_native_types� s z"DatetimeArray._format_native_typesc C s6 t |tj�rt|�}t|d�s"dS |j}t�| j|�S )Nr F)r� r� r` r �hasattrr r r� )rU r� �other_tzrA rA rB � _has_same_tz� s zDatetimeArray._has_same_tz�Nonec 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� r@ r r� )rU r� r� Z other_dtyperA rA rB r� � s z(DatetimeArray._assert_tzawareness_compatc C s� t | �t |�krtd��t|tj�r<t|�s0t�t| �|�}| �|�sZt t| �j � d���| j }|j }| j |j B }t || |d�}| js�|jr�t�||t� |�d�S )z3subtract DatetimeArray/Index or ndarray[datetime64]z$cannot add indices of unequal lengthz9 subtraction must have the same timezones or no timezones)�arr_maskztimedelta64[ns])r� r� r� r� r� r# r� r� r� r� rZ r� �_isnanr2 �_hasnansZputmaskr r� )rU r� Zself_i8Zother_i8r� � new_valuesrA rA rB �_sub_datetime_arraylike� s z%DatetimeArray._sub_datetime_arraylikec C s� | j dkr | �� �|��| j�S t|t�r.t�yD| jd k rF| � d �}n| }|� |�� d�}t � |�}|� | j�}W nL tk r� t�dt� | �d�| }t| �s�t| ��|�� | j�S Y nX t| ��|�S )Nr� zM8[ns]zBNon-vectorized DateOffset being applied to Series or DatetimeIndex�O)r� �ravel� _add_offset�reshape�shaper� r; r� r@ r Z _apply_arrayr� r_ r� �NotImplementedErrorr� �warnr r� r� r� r� )rU r� rV rW rA rA rB r � s$ zDatetimeArray._add_offsetc C sx t |ttjf�st�|tk s t�t|�}|tkr8| t S | �|�sJtd��| j }t ||j | j d�}| � |�}|�d�S )NzBTimestamp subtraction must have the same timezones or no timezones)r� ztimedelta64[ns])r� r r� r` r� r r r� r� r� r2 r� r� rT r� )rU r� r� rW rA rA rB �_sub_datetimelike_scalar� s z&DatetimeArray._sub_datetimelike_scalarc C s, | j dkst�| j �r | jS t�| 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. N)r@ r �is_utcr� r �tz_convert_from_utc)rU rA rA rB rK s z DatetimeArray._local_timestampsc C s8 t �|�}| 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� rN )r � maybe_get_tzr@ r� rC r� r� rN )rU r@ r� rA rA rB r� s @ zDatetimeArray.tz_convertc C s� d}||kr t |t�s td��| jdk rL|dkrBt�| j| j�}qjtd��n t� |�}tj | j|||d�}|� t �}t |�}d}t�|�s�t| �dkr�t|d �s�| 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. This method can also be used to do the inverse -- to create a time zone unaware object from an aware object. To that end, pass `tz=None`. 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. 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 Z shift_forwardZshift_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� rN )r� r r� r@ r r r� r� r r r� r� r rC r r� r1 rN r� )rU r@ r� r� Znonexistent_optionsZ new_datesr� rN rA rA rB r _ 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[object] )r@ )r r� r@ )rU rA rA rB � to_pydatetime s zDatetimeArray.to_pydatetimec C s( t | j| j�}t| �|��d��| 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� r@ r� Z _with_freqr )rU r� rA rA rB r� s &zDatetimeArray.normalizer> c C sr ddl m} | jdk r"t�dt� |dkr^| jp4| j}|dkrFtd��t |�}|dkrZ|}|}|j | j || jd�S )ax 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]') 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]') r )r> NzNConverting to PeriodArray/Index representation will drop timezone information.z8You must pass a freq argument as current index has none.)r@ ) �pandas.core.arraysr> r@ r� r � UserWarningrQ r� r� r8 Z_from_datetime64r� )rU rN r> �resrA rA rB r~ G s - zDatetimeArray.to_periodr? c C sD t jdtdd� ddlm} | j| �|��� j }|�d�}||�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 )r? zm8[ns]) r� r r� Z pandas.core.arrays.timedeltasr? r� r~ Z to_timestampr� )rU rN r? Zi8deltaZm8deltarA rA rB �to_perioddelta� s z DatetimeArray.to_perioddeltac C s* | � � }tj|d|d�}| j|dd�}|S )a� Return the month names of the DateTimeIndex with specified locale. 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') r� )�localeN)rH )rK r rS rT )rU r rV rW rA rA rB r� � s zDatetimeArray.month_namec C s* | � � }tj|d|d�}| j|dd�}|S )a� Return the day names of the DateTimeIndex with specified locale. 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') r� )r N)rH )rK r rS rT )rU r rV rW rA rA rB r� � s zDatetimeArray.day_namec C s | � � }t|dd�S )zX Returns numpy array of datetime.time. The time part of the Timestamps. r )r� )rK r )rU � timestampsrA rA rB 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� r@ )rU rA rA rB r{ � s zDatetimeArray.timetzc C s | � � }t|dd�S )z� Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information). ra )r� )rK r )rU r rA rA rB ra � s zDatetimeArray.dater= c C sH ddl m} | �� }t�|�}||dddgdd�}| jrDd|j| j< |S ) a 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 : Function return a 3-tuple containing ISO year, week number, and weekday for the given Timestamp object. datetime.date.isocalendar : Return a named tuple object with three components: year, week and weekday. 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 )r= ri ro rj ZUInt32)�columnsr� N)�pandasr= rK r Zbuild_isocalendar_sarrayr� Zilocr� )rU r= rV ZsarrayZiso_calendar_dfrA rA rB � isocalendar s # zDatetimeArray.isocalendarc C s< t jdtdd� | �� 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 rI )r� r� r� )r� ) r� r r� r ro Zhasnansr� r� �nan)rU Z week_seriesrA rA rB rn : 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. �dimz2 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�}t �| j�}t �| j�}|dk }|| d8 < || d7 < |t �d| d d � d| t �|d � t �|d � t �|d � d | j| jd | j d | j d d | j d d d S )z� 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� rF � i� � im � �d i� g � C:A�< i i@B i ʚ;� ) r� �asarrayri rG rj Zfixr� rk rl rm rx ry )rU ri rG rj ZtestarrrA rA rB �to_julian_dateS s N4z DatetimeArray.to_julian_dater� �int)�ddof�keepdims�skipnac C s2 ddl m} || j�d��}|j||||||d�S )Nr )r? r� )�axisr� �outr* r+ r, )r r? r� r� �std) rU r- r� r. r* r+ r, r? ZtdarA rA rB r/ v s zDatetimeArray.std)NFr� r� N)F)F)N)T)r N)r� r� )N)N)N)NNNr� FT)crZ � __module__� __qualname__r[ Z_typr r� r r� r` Z_recognized_scalarsr" Z_is_recognized_dtypeZ_infer_matchesrL �__annotations__rR rz r| r} r� Z__array_priority__r� r r� � classmethodr� r� r r� r� r� r� r� r� r� r\ r� r@ �setterr r� r� r� r� r� r� Z ravel_compatr� r� r� r� r r rK r� r r r� r~ r r� r� r r{ ra r rn ro r^ ri rG rj rk rl rm rx ry Z_dayofweek_docrr rq rp rt rs ru rv rw Z _is_month_docr� rb rc rd re rf rg rh r( r/ � __classcell__rA rA )r� rB r_ � st ! I # ` L 4 )G . # ,&&((%% r_ .zLiteral[False]r� )� allow_object�require_iso8601r� c C s d S )NrA )r� r6 r7 rA rA rB �sequence_to_datetimes� s r8 z Literal[True]znp.ndarray | DatetimeArrayc C s d S )NrA )r� r6 r7 rA rA rB r8 � s Fc C s@ t | |d|d�\}}}|jtkr$|S t|�}tj|||d�}|S )zV Parse/convert the passed data to either DatetimeArray or np.ndarray[object]. T)r6 � allow_mixedr7 )rN r� )r� r� r� rC r_ r� )r� r6 r7 rW r@ rN r� ZdtarA rA rB r8 � s r� )r6 r9 r7 c C s� d} t |�}t�|�}t||�}t| d�sPt�| �dkr@t| �} t�| �} d}n t | t �rdt d��n t | dd�} t | t �r�| jdtd �} nt | tjtf�s�t�| �} t | t�r�| j} t| |�\} }t| dd�} t| �s�t| �s�t| ��r�d}tj| dd �d k�r| �tj �} n�t | ||||| d �\} } |�rl| �rl| j d k�rN| � t �|dfS t!�"| � d �|�} | � t �} n(| �rx| }n |�r�| j t#k�r�| ddfS | j } t$| ��r�t%|| j&�}| j'}n�t(| ��r8t| d| �} | j t k�r�t)�*| �} d}|dk �r t�|�}t!j+| � d �||d�} | � t �} | j t k�s2t,| j ��| }n6|�rHt�|�}| j t-k�rd| jtj dd�} | � t �}|�r||�.� }t |tj��s�t,t/|���|j dk�s�t,|j ��t||� ||| fS )af 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. allow_object : bool, default False Whether to return an object-dtype ndarray instead of raising if the data contains more than one timezone. allow_mixed : bool, default False Interpret integers as timestamps when datetime objects are also present. require_iso8601 : bool, default False Only consider ISO-8601 formats when parsing strings. 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 Fz0Cannot create a DatetimeArray from a MultiIndex.T)r� r� )r� )r, �integer)r� r� r6 r9 r7 r� r� )r� )r� zM8[ns])0r� r r �validate_tz_from_dtyper� r� r� �listr' r� r0 r� r7 r6 r� r r� r3 r_ rN �maybe_convert_dtyper� r) r, r+ r Z infer_dtyper� r� �objects_to_datetime64nsr� r� r r r r� r% �_maybe_infer_tzr@ r� r# r Zensure_datetime64nsr� r� r r� r� )r� r� r� r@ r� r� r� r6 r9 r7 r� Z data_dtype� inferred_tzZutc_valsrW rA rA rB r� � s� . r� z np.ndarray)r� r7 r6 r9 c C s2 |dks t �tj| dtjd�} | j}|jr.dnd} y6tj| �d�||||||d�\} } | j | j | d�} W nr t k r� } zTy2t � | �d��\} } | j | j | d�} | �d �| fS t tfk r� | �Y nX W d d } ~ X Y nX | d k r�| �d �| fS t| ��r| | fS t| ��r&|�r | | fS t| ��nt| ��d S ) a� 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'} require_iso8601 : bool, default False allow_object : bool Whether to return an object-dtype ndarray instead of raising if the data contains more than one timezone. allow_mixed : bool, default False Interpret integers as timestamps when datetime objects are also present. 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 )r� r� ZcoerceF)r� r� �F�C�K)�errors�utcr� r� r7 r9 )�orderr� N)r� r� r� �object_�flags� f_contiguousr Zarray_to_datetimer� r r r� r Zdatetime_to_datetime64r� r� r# r) )r� r� r� rE rD r7 r6 r9 rH rF rW Z tz_parsed�errrV rA rA rB r> T s< ( r> )r� c C s� t | d�s| |fS t| j�r,| �t�} d}n�t| j�s@t| j�rTtd| j� d���n`t| j�rhtd��nLt | j�r�| j j | j t d�j} d}n(t| j�r�t| j�s�tj| tjd�} d}| |fS )aN Convert data based on dtype conventions, issuing deprecation warnings or errors where appropriate. Parameters ---------- data : np.ndarray or pd.Index copy : bool Returns ------- data : np.ndarray or pd.Index copy : bool Raises ------ TypeError : PeriodDType data is passed r� Fzdtype z& cannot be converted to datetime64[ns]zFPassing PeriodDtype data is invalid. Use `data.to_timestamp()` instead)rH )r� )r� r( r� r� r r- r r� r* r! � categoriesZtake�codesr Z_valuesr' r% r� r� rG )r� r� rA rA rB r= � s" r= z tzinfo | None)r@ r@ r� c C s< | dkr|} n*|dkrn t �| |�s8td|� d| � ���| S )aV 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� )r@ r@ rA rA rB r? � s r? c C sd | dk r`t | �} t| t�d��r,d}t|��t| tj�r@| tksPt| tjtf�s`td| � d���| S )a� Check that a dtype, if passed, represents either a numpy datetime64[ns] dtype or a pandas DatetimeTZDtype. Parameters ---------- dtype : object Returns ------- dtype : None, numpy.dtype, or DatetimeTZDtype Raises ------ ValueError : invalid dtype Notes ----- 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� rA rA rB r� s r� )r@ r� c C s� | dk r�t | t�r6yt�| �} W n tk r4 Y nX t| dd�}|dk rj|dk rft�||�sftd��|}|dk r�t | �r�|dk r�t�||�s�td��|S )aU 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 ---------- dtype : dtype, str tz : None, tzinfo Returns ------- tz : consensus tzinfo Raises ------ ValueError : on tzinfo mismatch Nr@ z-cannot supply both a tz and a dtype with a tzzHcannot supply both a tz and a timezone-naive dtype (i.e. datetime64[ns])) r� �strr/ Zconstruct_from_stringr� r� r r� r� r# )r� r@ r� rA rA rB r; 5 s r; r )rD rE r@ r� c C s� yt �| |�}W n, tk r< } ztd�|�W dd}~X Y nX t �|�}t �|�}|dk rx|dk rxt �||�s�td��n |dk r�|}|S )a� 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� )rD rE r@ r@ rJ rA rA rB r� d s r� zTimestamp | None)rD rE r� c C sb d}| d k r.|r | � � } d}n|o,| �� tk}|d k rX|rH|� � }d}n|oV|�� tk}| ||fS )NT)r� r � _midnight)rD rE r� r� rA rA rB 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� r@ r@ )r� r; r )r� Zis_noneZ is_not_nonerN r@ r� r� Z localize_argsrA rA rB r� � s r� c c sf t |�}t| �} | tk r | nd} t|�}|tk r4|nd}| rR|�| �sR|�| �} n|rj|�|�sj|�|�}|dkr�|| k r�|jdkr�d}d}|dkr�| |d | }| dkr�||d | } | }|jdk�rx�||k�r|V ||kr�P |�|�}||k�r td|� d���|}q�W nLxJ||k�r`|V ||k�r4P |�|�}||k�rXtd|� d���|}�qW 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 Nr r� zOffset z did not increment datez did not decrement date) r r r Z is_on_offsetZ rollforward�rollback�n�applyr� )rD rE r� r� �curZ next_daterA rA rB r� � sD r� )N)..)..)FF)NFNFFr� )Fr� FFF)j� __future__r r r r r �typingr r r r� �numpyr� Z pandas._libsr r Zpandas._libs.arraysr Zpandas._libs.tslibsr r r r r r r r r r r r r r r Z pandas.errorsr Zpandas.core.dtypes.castr Zpandas.core.dtypes.commonr r r r! r" r# r$ r% r& r' r( r) r* r+ r, r- r. Zpandas.core.dtypes.dtypesr/ Zpandas.core.dtypes.genericr0 Zpandas.core.dtypes.missingr1 Zpandas.core.algorithmsr2 r r3 r4 r� Zpandas.core.arrays._rangesr5 Zpandas.core.arrays.integerr6 Zpandas.core.common�core�commonr� Zpandas.core.constructionr7 Zpandas.tseries.frequenciesr8 Zpandas.tseries.offsetsr9 r: r; r< r r= r> r? rN rC r^ Z TimelikeOpsZ DatelikeOpsr_ r8 r� r> r= r? r� r; r� r� r� r� rA rA rA rB �<module> s� D L & T= */+!
| ver. 1.4 |
Github
|
.
| PHP 8.3.23 | Генерация страницы: 0.05 |
proxy
|
phpinfo
|
Настройка