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mZ d d!lmZmZ d d"lmZ d d#lmZmZ d d$lmZ erd d%lmZmZ d d&lmZ d d'lmZmZmZ e e8e f Zd:d(Zd;d)Z G d* d+etev« Z G d, d-e« Zd.Zd/Z d0Z¡d1Z¢ G d2 d3e« Z£ d<d4Z¤ed=d5« Z¥ed>d6« Z¥d?d7Z¥ d@d8Z¦dAd9Z§y)Bé )Ú annotations)ÚdatetimeÚ timedelta)ÚwrapsN)Ú TYPE_CHECKINGÚAnyÚCallableÚLiteralÚUnionÚcastÚfinalÚoverload)Úusing_string_dtype)ÚalgosÚlib)Ú NDArrayBacked)Ú
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Shared Base/Mixin class for DatetimeArray, TimedeltaArray, PeriodArray
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Assumes that __new__/__init__ defines:
|
_ndarray
|
|
and that inheriting subclass implements:
|
freq
|
ztuple[str, ...]Ú_infer_matcheszCallable[[DtypeObj], bool]Ú_is_recognized_dtypeztuple[type, ...]Ú_recognized_scalarsú
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np.ndarrayÚ_ndarrayúBaseOffset | NoneÚfreqc ó y)NT© ©r s rs Ú _can_hold_naz#DatetimeLikeArrayMixin._can_hold_naÚ s àru NFc ó t | « rp ©r: )r Údatar{ r Úcopys rs Ú__init__z DatetimeLikeArrayMixin.__init__Þ s ô " $Ó'Ð'ru c ó t | « )z£
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The scalar associated with this datelike
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|
* PeriodArray : Period
|
* DatetimeArray : Timestamp
|
* TimedeltaArray : Timedelta
|
r r s rs Ú _scalar_typez#DatetimeLikeArrayMixin._scalar_typeã s ô " $Ó'Ð'ru c ó t | « )ay
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Construct a scalar type from a string.
|
|
Parameters
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----------
|
value : str
|
|
Returns
|
-------
|
Period, Timestamp, or Timedelta, or NaT
|
Whatever the type of ``self._scalar_type`` is.
|
|
Notes
|
-----
|
This should call ``self._check_compatible_with`` before
|
unboxing the result.
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r ©r Úvalues rs Ú_scalar_from_stringz*DatetimeLikeArrayMixin._scalar_from_stringî ó ô$ " $Ó'Ð'ru c ó t | « )a´
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Unbox the integer value of a scalar `value`.
|
|
Parameters
|
----------
|
value : Period, Timestamp, Timedelta, or NaT
|
Depending on subclass.
|
|
Returns
|
-------
|
int
|
|
Examples
|
--------
|
>>> arr = pd.array(np.array(['1970-01-01'], 'datetime64[ns]'))
|
>>> arr._unbox_scalar(arr[0])
|
numpy.datetime64('1970-01-01T00:00:00.000000000')
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r r s rs Ú _unbox_scalarz$DatetimeLikeArrayMixin._unbox_scalar s ô* " $Ó'Ð'ru c ó t | « )a|
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Verify that `self` and `other` are compatible.
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|
* DatetimeArray verifies that the timezones (if any) match
|
* PeriodArray verifies that the freq matches
|
* Timedelta has no verification
|
|
In each case, NaT is considered compatible.
|
|
Parameters
|
----------
|
other
|
|
Raises
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------
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Exception
|
r ©r Úothers rs Ú_check_compatible_withz-DatetimeLikeArrayMixin._check_compatible_with r¡ ru c ó t | « )zI
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box function to get object from internal representation
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r )r Úxs rs r} z DatetimeLikeArrayMixin._box_func/ s ô " $Ó'Ð'ru c óF t j || j d¬« S )z1
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apply box func to passed values
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F)Úconvert)r Ú map_inferr} )r Úvaluess rs Ú _box_valuesz"DatetimeLikeArrayMixin._box_values5 s ô }}V T§^¡^¸UÔCÐCru c ó j dkD r fdt t « « D « S fd j D « S )Né c 3 ó( K | ] }| y wrp r )Ú.0Únr s rs ú <genexpr>z2DatetimeLikeArrayMixin.__iter__.<locals>.<genexpr>= s øè ø Ò6 DGÑ6ùs c 3 ó@ K | ] }j |« y wrp )r} )r² Úvr s rs r´ z2DatetimeLikeArrayMixin.__iter__.<locals>.<genexpr>? s øè ø Ò9¨!DNN 1×%Ñ9ùs )ÚndimÚrangeÚlenÚasi8r s `rs Ú__iter__z DatetimeLikeArrayMixin.__iter__; s1 ø Ø 99q=Û6¤U¬3¨t«9Ó%5Ô6Ð 6ã9¨t¯y©yÔ9Ð 9ru c ó8 | j j d« S )z
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Integer representation of the values.
|
|
Returns
|
-------
|
ndarray
|
An ndarray with int64 dtype.
|
ry )r r| r s rs rº zDatetimeLikeArrayMixin.asi8A s ð }}×!Ñ! $Ó'Ð'ru r )Úna_repÚ date_formatc ó t | « )z|
|
Helper method for astype when converting to strings.
|
|
Returns
|
-------
|
ndarray[str]
|
r )r r½ r¾ s rs Ú_format_native_typesz+DatetimeLikeArrayMixin._format_native_typesQ s ô " $Ó'Ð'ru c ó dj S )Nz'{}')Úformat)r Úboxeds rs Ú
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This conversion to NumPy requires a copy, but 'copy=False' was passed. Consider using 'np.asarray(..)' instead.©Ú
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stacklevel©r{ T)
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rE ÚwarningsÚwarnÚ FutureWarningr@ Únpr_ ÚlistÚobjectr )r r{ r s rs Ú __array__z DatetimeLikeArrayMixin.__array__d sl ô 5Ô !Øu}Ü ð0ô "Ü /Ó 1õ ô 88D J¬fÔ5Ð 5à 4<Ü88DMM°Ô7Ð 7Ø}}Ð ru c ó y rp r ©r Úitems rs Ú __getitem__z"DatetimeLikeArrayMixin.__getitem__{ ó à ru c ó y rp r rÑ s rs rÓ z"DatetimeLikeArrayMixin.__getitem__ s ð
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This getitem defers to the underlying array, which by-definition can
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Find the `freq` attribute to assign to the result of a __getitem__ lookup.
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r° N)rz r{ rL r r· rb ÚsliceÚstepÚEllipsisÚcomÚis_bool_indexerr Úmaybe_booleans_to_slicer| rÌ Úuint8rÙ )r rÛ Ú is_periodr Únew_keys rs rÙ z(DatetimeLikeArrayMixin._get_getitem_freq s ô tzz¬;Ó7 Ù Ø99Dð& ð% YY!^ØDð" ô & d¨CÓ0CØDÜ#uÔ%Ø99Ð(¨S¯X©XÐ-AØ 88 d§i¡iÑ/Dð ð 99Dð ð ð yyð
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ValueErrorr r; r r r r§ r rD r¹ Ú_validate_listlikerE Úgetattr)r r¦ Úerrs rs Ú_validate_comparison_valuez1DatetimeLikeArrayMixin._validate_comparison_value# sM Ü eSÔ !ð /à ×0Ñ0°Ó7ô
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eT× 5Ñ 5Ô 6¸%Ä3¹,Ø×%Ñ% eÓ,Eð 8Ø×+Ñ+¨EÔ2ð, ô# eÔ$Ü# EÓ*Ð *ä Z3t9Ò $Ü Ð 1Ó2Ð 2ð <Ø ×/Ñ/°ÀDÐ/ÓIØ×+Ñ+¨EÔ2ð øô; Ô 5Ð6ò /ä'¨Ó.Ð.ð /ûô Ô 4Ð5ò 8ä'¨Ó.°CÐ7ûð 8ûô Ô 4Ð5ò <Ü"¤7¨5°'¸4Ó#@ÔAàä+¨EÓ2¸Ð;ó ð ûð <ús; C ÁC#  $D ÃC Ã#DÃ2 C>Ã>DÄEÄ#D>Ä>E)Úallow_listlikeÚunboxc ó t || j « rnt |t « r | j |« }nt || j « rt }nmt |« r | j ||« }t |« t || j « r| j |« }n | j ||« }t |« |s|S | j |« S # t $ r#}| j ||« }t |« |d}~ww xY w)a
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Validate that the input value can be cast to our scalar_type.
|
|
Parameters
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----------
|
value : object
|
allow_listlike: bool, default False
|
When raising an exception, whether the message should say
|
listlike inputs are allowed.
|
unbox : bool, default True
|
Whether to unbox the result before returning. Note: unbox=False
|
skips the setitem compatibility check.
|
|
Returns
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-------
|
self._scalar_type or NaT
|
N) rz r r r r Ú_validation_error_messager rO r{ r rP r r£ )r r r r r r s rs Ú_validate_scalarz'DatetimeLikeArrayMixin._validate_scalarG sû ô0 eT× .Ñ .Ô /Ø ä sÔ #ð .Ø ×0Ñ0°Ó7ô
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Construct an exception message on validation error.
|
|
Some methods allow only scalar inputs, while others allow either scalar
|
or listlike.
|
|
Parameters
|
----------
|
allow_listlike: bool, default False
|
|
Returns
|
-------
|
str
|
r{ r· r z arrayú'zvalue should be a 'z!', 'NaT', or array of those. Got z instead.z' or 'NaT'. Got )Úhasattrr r{ r r r )r r r Úmsg_gotr s rs r z0DatetimeLikeArrayMixin._validation_error_message s° ô 5'Ô "¤w¨u°f¸aÓ'@À1Ò'DØ VÐ,Gà$u+×.Ñ.Ð/¨qÐ1GÙ à% d×&7Ñ&7×&@Ñ&@Ð%Að B*Ø*1¨°)ð=ð ð
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ru c óî t |t | « « r9| j j dv r |s |j | j
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d¬« }|S t |t « r4t |« dk( r&t | « j g | j ¬« S t |d« rO|j t k( r<t j |« | j v r t | « j |« }t# |d¬« }t% |« }t# |d¬« }t' |« r' t | « j || j ¬« }t |j t( « r@|j* j | j k( r |j- « }t# |d¬« }|rt/ |j « rnAt | « j1 |j « s | j! |d« }t |« | j j dv r |s |j | j
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d¬« }|S # t t f$ r$ |r|cY S | j! |d« }t |« w xY w# t $ r Y .w xY w) Nrõ F©Úround_okr rÈ r{ T©Ú extract_numpy)rz r r{ r÷ Úas_unitÚunitrÍ r¹ rþ r rÎ r Ú infer_dtyper r r r ra Úpd_arrayrB rI Ú
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categoriesÚ_internal_get_valuesrE r )r r r r s rs r z)DatetimeLikeArrayMixin._validate_listlike£ s Ü eT $ZÔ (Øzz $Ñ&©|à d§i¡i¸% Ó@ØLä eTÔ "¤s¨5£z°Q¢ä:×,Ñ,¨R°t·z±zÐ,ÓBÐ Bä 5'Ô " u§{¡{´fÒ'<ô uÓ%¨×)<Ñ)<Ñ<ð)Ü J× 5Ñ 5°eÓ <Eô e°4Ô8ÜÜ e°4Ô8ä %Ô ð ô T
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×1Ñ1°%¸t¿z¹zÐ1ÓJô ekkÔ#3Ô 4à× Ñ ×%Ñ%¨¯©Ò3à ×2Ñ2Ó4Ü% e¸4Ô@á O¨E¯K©KÔ 8Ø äd×0Ñ0°·±Ô=Ø×0Ñ0°¸Ó=CÜC.Ð à ::??dÑ "©<àMM $§)¡)°eMÓ<EØ øôO #¤IÐ.ò )Ù#Ø $ Ø ×8Ñ8¸ÀÓECÜ# C.Ð(ð )ûô& ò Úð ús$ ÃH1 Ä &I' È1I$É I$É' I4É3I4c ó t |« r| j |« }n| j |d¬« S | j |« S )NT)r )rD r r Ú_unboxr s rs Ú_validate_setitem_valuez.DatetimeLikeArrayMixin._validate_setitem_valueÝ s? Ü Ô Ø×+Ñ+¨EÓ2Eà×(Ñ(¨¸tÐ(ÓDÐ Dà{{5Ó!Ð!ru c ó t j |« r| j |« }|S | j |« |j }|S )zZ
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Unbox either a scalar with _unbox_scalar or an instance of our own type.
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)r rØ r£ r§ r r¥ s rs r- z DatetimeLikeArrayMixin._unboxå sE ô
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¬« S t |« }t |t | « « sg d¢}|j t k( r¯t j |d| j ¬« }|j t k7 r| j |« S t j |d¬« }||vrN|dk( rnHd |v r t | j t « |« S t j | j t
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¬« S t | « j |« }t! j" d
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| j d t$ t' « ¬ « | j j d v r't+ d| « } |j- | j. « } | j1 |« t | j4 |j4 « S # t( $ r" t | j t « |« cY S w xY w# t2 t( f$ r( t j | j t
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¬« cY S w xY w)z÷
|
Compute boolean array of whether each value is found in the
|
passed set of values.
|
|
Parameters
|
----------
|
values : np.ndarray or ExtensionArray
|
|
Returns
|
-------
|
ndarray[bool]
|
ÚfiucrÈ )r Ú timedelta64r Ú
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datetime64ÚdateÚperiodT)Úconvert_non_numericÚdtype_if_all_natF©ÚskipnaÚstringÚmixedz"The behavior of 'isin' with dtype=z¼ and castable values (e.g. strings) is deprecated. In a future version, these will not be considered matching by isin. Explicitly cast to the appropriate dtype before calling isin instead.rÆ rõ ú DatetimeArray | TimedeltaArray)r{ r÷ rÌ Úzerosrû Úboolr` rz r rÎ r Úmaybe_convert_objectsrU r( rÿ rþ rÉ rÊ rË r@ r r r& r' r§ r rº )r r Ú inferableÚinferreds rs rU zDatetimeLikeArrayMixin.isin sà ð <<× Ñ Ñ &ä88DJJ¬dÔ3Ð 3ä/°Ó7ä&¤$ t£*Õ-òIð ||vÒ%Ü ×2Ñ2ØØ(,Ø%)§Z¡Zôð
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<<¤6Ò)Ø 99 VÓ,Ð,ä ??¨6¸%Ô@Ø 9Ñ,Ø 8Ò+Ø à HÑ,Ü # D§K¡K´Ó$7¸Ó @Ð@ä !xx¨¯
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©
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¼$Ô ?Ð?ð Ü d×2Ñ2°6Ó:ô à8¸¿¹¸ ð E,ð ,ô
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"Ü /Ó 1õ ð ::??dÑ "ÜÐ8¸$Ó?Dð ^^ D§I¡IÓ.Fð 4ð × 'Ñ '¨Ô /ô DIIv{{Ó+Ð+øô? ò 9ÜDKK¬Ó /°Ó8Ò8ð 9ûô2 :Ð&ò 4ä88DJJ¬dÔ3Ò 3ð 4ús$ Ä&G# Æ2H Ç#(HÈ HÈ4IÉIc ó | j S rp )Ú_isnanr s rs rP zDatetimeLikeArrayMixin.isnaX s Ø{{Ðru c ó( | j t k( S )z-
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return if each value is nan
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)rº r r s rs rI z DatetimeLikeArrayMixin._isnan[ s ð
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yyDÑ Ð ru c óH t | j j « « S )zJ
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return if I have any nans; enables various perf speedups
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)rD rI Úanyr s rs Ú_hasnaz DatetimeLikeArrayMixin._hasnab s ô
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DKKOOÓ%Ó&Ð&ru c óª | j rF|r|j |« }|t j }t j || j
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|« |S )az
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Parameters
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----------
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result : np.ndarray
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fill_value : object, default iNaT
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convert : str, dtype or None
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Returns
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-------
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result : ndarray with values replace by the fill_value
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mask the result if needed, convert to the provided dtype if its not
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None
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This is an internal routine.
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)rM rÿ rÌ ÚnanÚputmaskrI )r r Ú
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fill_valuer« s rs Ú_maybe_mask_resultsz*DatetimeLikeArrayMixin._maybe_mask_resultsi sD ð& ;;ÙØ wÓ/ØÐ!Ü VV
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Ü JJvt{{¨JÔ 7Ø ru c óH | j y| j j S )a{
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Return the frequency object as a string if it's set, otherwise None.
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Examples
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--------
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For DatetimeIndex:
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>>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00"], freq="D")
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>>> idx.freqstr
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'D'
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The frequency can be inferred if there are more than 2 points:
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>>> idx = pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"],
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... freq="infer")
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>>> idx.freqstr
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'2D'
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For PeriodIndex:
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>>> idx = pd.PeriodIndex(["2023-1", "2023-2", "2023-3"], freq="M")
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>>> idx.freqstr
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'M'
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N)r Úfreqstrr s rs rT z DatetimeLikeArrayMixin.freqstr s! ð4 99Ð ØØyy× Ñ Ð ru c ól | j dk7 ry t j | « S # t $ r Y yw xY w)ax
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Tries to return a string representing a frequency generated by infer_freq.
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Returns None if it can't autodetect the frequency.
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Examples
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--------
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For DatetimeIndex:
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>>> idx = pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"])
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>>> idx.inferred_freq
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'2D'
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For TimedeltaIndex:
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>>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
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>>> tdelta_idx
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TimedeltaIndex(['0 days', '10 days', '20 days'],
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dtype='timedelta64[ns]', freq=None)
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>>> tdelta_idx.inferred_freq
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'10D'
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r° N)r· rg Ú
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infer_freqr r s rs Ú inferred_freqz$DatetimeLikeArrayMixin.inferred_freq¥ s: ð0 99>Øð Ü ×)Ñ)¨$Ó/Ð /øÜò Ùð ús ' § 3²3c ój | j }|y t j |« S # t $ r Y y w xY wrp )rT r Úget_reso_from_freqstrÚKeyError)r rT s rs Ú_resolution_objz&DatetimeLikeArrayMixin._resolution_objÄ s; à,,Ø ?Øð Ü ×3Ñ3°GÓ<Ð <øÜò Ùð ús & ¦ 2±2c ó. | j j S )zO
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Returns day, hour, minute, second, millisecond or microsecond
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)r[ Úattrnamer s rs Ú
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resolutionz!DatetimeLikeArrayMixin.resolutionÎ s ð ×#Ñ#×,Ñ,Ð,ru c óJ t j | j d¬« d S )NT©Útimeliker ©r Ú is_monotonicrº r s rs Ú_is_monotonic_increasingz/DatetimeLikeArrayMixin._is_monotonic_increasingÙ ó ä×!Ñ! $§)¡)°dÔ;¸AÑ>Ð>ru c óJ t j | j d¬« d S )NTr` r° rb r s rs Ú_is_monotonic_decreasingz/DatetimeLikeArrayMixin._is_monotonic_decreasingÝ re ru c óv t t | j j d« « « | j k( S )NÚK)r¹ rW rº rù Úsizer s rs Ú
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_is_uniquez!DatetimeLikeArrayMixin._is_uniqueá s( ä8DIIOO¨CÓ 0Ó1Ó2°d·i±iÑ?Ð?ru c ó | j dkD rXt |dd « | j k( r> || j « |j « « j | j « S | j |« }t |dd « }t |« r?t j |t j | j t « « |« }|S |t u r`|t j" u r't j$ | j t& ¬« }|S t j( | j t& ¬« }|S t+ | j, t. « st1 t2 | « } | j4 |j4 k7 rWt+ |t7 | « « s |j9 | j: d¬« }n#|jD }tC | jD ||« S | jG |« } || jD jI d« |jI d« « }tK |« }| jL |z }|jO « r)|t j" u } t jP ||| « |S # t $ r t | ||« cY S w xY w# t< $ r9 t j> |j@ « }tC | jD ||« cY S w xY w)Nr° rû r{ rÈ Fr" ry ))r· r rû rù rú r r; re rE rT Úcomp_method_OBJECT_ARRAYrÌ r rÿ rÎ r ÚoperatorÚneÚonesrD rC rz r{ rL r Ú TimelikeOpsrø r r& r' r r_ Úasm8r& r r- r| rP rI rL rP )
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r r¦ rr r{ r Ú other_arrÚ
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other_valsÚo_maskÚmaskÚ
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nat_results
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rs Ú _cmp_methodz"DatetimeLikeArrayMixin._cmp_methodè s3 Ø 99q=W U¨G°TÓ :¸d¿j¹jÒ Hádjjl E§K¡K£MÓ2×:Ñ:¸4¿:¹:ÓFÐ Fð 7Ø×3Ñ3°EÓ:Eô w¨Ó-Ü 5Ô !ô ×1Ñ1ØBJJt{{¬6Ó 2Ó3°UóFð MØ C<ØX[[Ñ Ü §¡´4Ô8ð Mô $§*¡*´DÔ9ØMä$**¤kÔ2Ü TÓ*DØ{{ellÒ*Ü! %¬¨d«Ô4ðà %§ ¡ ¨d¯i©iÀ% Ó Hð !&§¡IÜ9¸$¿-¹-ÈÐTVÓWÐWà[[ Ó'
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áDMM×&Ñ& tÓ,¨j¯o©o¸dÓ.CÓDäeØ{{VÑ#Ø 88:Øx{{Ð*JÜ JJvt ZÔ 0à øô] !ò 7Ü% d¨E°2Ó6Ò 6ð 7ûô6 &ò Ü$&§H¡H¨U¯Z©ZÓ$8 Ü =Ø MM¨9°bó ò ðús$ Á)I* Æ J É*JÊJÊ?KËKÚ__pow__Ú__rpow__Ú__mul__Ú__rmul__Ú __truediv__Ú __rtruediv__Ú __floordiv__Ú __rfloordiv__Ú__mod__Ú__rmod__Ú
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__divmod__Ú __rdivmod__c óÎ t |t « r|j }d}||fS t |t t f« r|j
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}d}||fS |j }|j }||fS )zN
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Get the int64 values and b_mask to pass to add_overflowsafe.
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N)rz r Úordinalr r r~ rI rº )r r¦ Úi8valuesrv s rs Ú_get_i8_values_and_maskz.DatetimeLikeArrayMixin._get_i8_values_and_mask. sq ô eVÔ $Ø }}HØDð ~Ð ô ¤ ¬9Ð 5Ô 6Ø ||HØDð
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~Ð ð <<DØ zzHØ~Ð ru c óÈ t | j t « r | j S t j
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|« syt | j t « r | j S y)zP
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Check if we can preserve self.freq in addition or subtraction.
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N)rz r{ rL r r rØ r r¥ s rs Ú_get_arithmetic_result_freqz2DatetimeLikeArrayMixin._get_arithmetic_result_freqA sF ô djj¤+Ô .Ø99Ð Ü uÔ%ØÜ ¤4Ô (à99Ð Øru c ó t j | j d« s7t dt | « j
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dt |« j
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« t d| « } ddlm} ddl m
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} |t usJ t |« r\| j t j « j d| j d « z } |j" ||j ¬
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« S t% |« }| j' |« \ } }t d| « } | j) |« \ }}t+ | j, t/ j0 |d ¬
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« « }|j3 d| j d « } ||j4 | j ¬ « }|j3 d| j d « }| j7 |« } |j" ||| ¬ « S )Nrò ú cannot add ú and rn r ©rl )Ú tz_to_dtypezM8[ú]rÈ ry ©rï r' ©r{ r ) r Ú is_np_dtyper{ r r r r Úpandas.core.arraysrl Ú pandas.core.arrays.datetimesr r rP r Ú to_datetime64rÿ r' Ú _simple_newr Ú_ensure_matching_resosr r rº rÌ r r| rï r )
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r r¦ rl r r Úother_i8ru Ú
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res_valuesr{ Únew_freqs
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rs Ú_add_datetimelike_scalarz/DatetimeLikeArrayMixin._add_datetimelike_scalarQ s ätzz¨3Ô/ÜØ d 4j× 1Ñ 1Ð 2°%¼¸U» ×8LÑ8LÐ7MÐNóð ô Ð$ dÓ+å4Ý<àCÑ Ð Ð Ü ;ð ]]¤S×%6Ñ%6Ó%8×%?Ñ%?À#ÀdÇiÁiÀ[ÐPQÐ@RÓ%SÑSFà,=×,Ñ,¨V¸6¿<¹<ÔHÐ Hä%Ó Ø×1Ñ1°%Ó8 eÜÐ$ dÓ+à ×7Ñ7¸Ó>Ñ&Ü! $§)¡)¬R¯Z©Z¸ÈÔ-MÓNØ[[ 3 t§y¡y k°Ð!3Ó4
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áuxx¨d¯i©iÔ8Ø[[ 3 t§y¡y k°Ð!3Ó4
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Ø×3Ñ3°EÓ:Ø(}×(Ñ(¨¸5ÀxÔPÐPru c óº t j | j d« s7t dt | « j
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dt |« j
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« || z S )Nrò r r )r r r{ r r r r¥ s rs Ú_add_datetime_arraylikez.DatetimeLikeArrayMixin._add_datetime_arraylikes sS ätzz¨3Ô/ÜØ d 4j× 1Ñ 1Ð 2°%¼¸U» ×8LÑ8LÐ7MÐNóð ð
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t|Ðru c ó | j j dk7 r!t dt | « j « t d| « } t |« r | t z
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S t |« }| j |« \ } }| j |« S )Nrí ú"cannot subtract a datelike from a rl ) r{ r÷ r r r r rP r r r Ú_sub_datetimelike)r r¦ Útss rs Ú_sub_datetimelike_scalarz/DatetimeLikeArrayMixin._sub_datetimelike_scalar} s ð ::??cÒ !ÜÐ @ÄÀdÃ×ATÑATÐ@UÐ VÓWÐ WäO TÓ*ô ;à#:Ð ä uÓ à×.Ñ.¨rÓ2bØ×%Ñ% bÓ)Ð)ru c ó | j j dk7 r!t dt | « j « t | « t |« k7 r t d« t d| « } | j |« \ } }| j |« S )Nrí r ú$cannot add indices of unequal lengthrl )
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r{ r÷ r r r r¹ r r r r¡ r¥ s rs Ú_sub_datetime_arraylikez.DatetimeLikeArrayMixin._sub_datetime_arraylike s} à ::??cÒ !ÜÐ @ÄÀdÃ×ATÑATÐ@UÐ VÓWÐ Wä t9E
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Ò "Ü Ð CÓDÐ DäO TÓ*à×1Ñ1°%Ó8 eØ×%Ñ% eÓ,Ð,ru c óü t d| « } ddlm} | j |« | j |« \ }}t | j t j | d¬« « }|j d| j d « }| j |« } t d
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| « } |j ||j" | ¬ « S # t $ r2}t |« j dd« } t |« |« |d }~ww xY w) Nrl r ©rn ÚcompareÚsubtractry rÈ ú timedelta64[r z Tick | Noner )r r rn Ú_assert_tzawareness_compatr r Úreplacer r r rº rÌ r r| r' r r r{ )
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r r¦ rn r Ú new_messager ru r Úres_m8r s
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rs r¡ z(DatetimeLikeArrayMixin._sub_datetimelike så äO TÓ*å5ð 2Ø × +Ñ +¨EÔ 2ð
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×7Ñ7¸Ó>Ñ&Ü% d§i¡i´·±¸X¸IÈTÔ1RÓS
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Ø <°· ± ¨{¸!Ð!<Ó=à×3Ñ3°EÓ:Ü xÓ0Ø)~×)Ñ)¨&¸¿ ¹ È8ÔTÐTøô ò 2Ü c(×*Ñ*¨9°jÓAKØ$s)KÓ(¨cÐ 1ûð 2ús C à C;à -C6Ã6C;c ó, t j | j d« s!t dt | « j
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« ddlm} t j |j | j « }t |j « } |||¬« }|| z S )Nrò zcannot add Period to a r )rm rÈ )r r r{ r r r Úpandas.core.arrays.periodrm rÌ Ú broadcast_tor rû rL r )r r¦ rm Úi8valsr{ Úparrs rs Ú _add_periodz"DatetimeLikeArrayMixin._add_period± ss ätzz¨3Ô/ÜÐ 5´d¸4³j×6IÑ6IÐ5JÐ KÓLÐ Lõ :ä § ¡ °·
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±
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Ó;ÜEJJÓ'Ù6¨Ô/Ød{Ðru c ó t | « rp r )r Úoffsets rs Ú _add_offsetz"DatetimeLikeArrayMixin._add_offset¾ s Ü! $Ó'Ð'ru c ó t |« rt j | j d¬« j | j
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j « }|j t « t | « j || j ¬« S t d| « } t |« }| j |« \ } }| j |« S )zk
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Add a delta of a timedeltalike
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Returns
|
-------
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Same type as self
|
ry rÈ rB )rP rÌ Úemptyrû r| r r{ Úfillr r r r r r Ú_add_timedeltalike)r r¦ Ú
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new_valuess rs Ú_add_timedeltalike_scalarz0DatetimeLikeArrayMixin._add_timedeltalike_scalarÁ s ô ;ä $§*¡*°DÔ9×>Ñ>¸t¿}¹}×?RÑ?RÓSJØ OODÔ !Ü:×)Ñ)¨*¸D¿J¹JÐ)ÓGÐ Gô Ð4°dÓ;Ü%Ó Ø×1Ñ1°%Ó8 eØ×&Ñ& uÓ-Ð-ru c ó¨ t | « t |« k7 r t d« t d| « } | j |« \ } }| j |« S )zl
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Add a delta of a TimedeltaIndex
|
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Returns
|
-------
|
Same type as self
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r¥ rB )r¹ r r r r¼ r¥ s rs Ú_add_timedelta_arraylikez/DatetimeLikeArrayMixin._add_timedelta_arraylikeÕ sR ô t9E
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Ò "Ü Ð CÓDÐ DäÐ4°dÓ;à×1Ñ1°%Ó8 eØ×&Ñ& uÓ-Ð-ru c óR t d| « } | j |« \ }}t | j t j
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|d¬« « }|j | j j « }| j |« }t | « j || j |¬« S )NrB ry rÈ r ) r r r rº rÌ r r| r r{ r r r )r r¦ r ru r½ r r s rs r¼ z)DatetimeLikeArrayMixin._add_timedeltalikeç s äÐ4°dÓ;à ×7Ñ7¸Ó>Ñ&Ü% d§i¡i´·±¸HÈDÔ1QÓR
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Ø __ T§]¡]×%8Ñ%8Ó9
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à×3Ñ3°EÓ:ô
|
Dz×%Ñ%Ø djj¨xð &ó
|
ð
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ru c óä t | j t « r;t dt | « j
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dt t « j
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« t d| « } t j | j t j ¬« }|j t « |j | j j « }t | « j! || j d¬« S )z$
|
Add pd.NaT to self
|
z Cannot add r z TimedeltaArray | DatetimeArrayrÈ Nr )rz r{ rL r r r r r rÌ rº rû r r» r r| r r ©r r s rs Ú_add_natz DatetimeLikeArrayMixin._add_natø s» ô
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djj¤+Ô .ÜØ d 4j× 1Ñ 1Ð 2°%¼¼S» ×8JÑ8JÐ7KÐLóð ô Ð4°dÓ;ô $**¬B¯H©HÔ5Ø DÔØT]]× 0Ñ 0Ó1ô Dz×%Ñ%Ø $**¨4ð &ó
|
ð
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ru c ó2 t j | j t j ¬« }|j t
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« | j j dv r+t d| « } |j d| j d« S |j d« S )z+
|
Subtract pd.NaT from self
|
rÈ rõ z DatetimeArray| TimedeltaArrayr« r ztimedelta64[ns]) rÌ rº rû r r» r r{ r÷ r r| r' rà s rs Ú_sub_natz DatetimeLikeArrayMixin._sub_nat sq ô $**¬B¯H©HÔ5Ø DÔØ ::??dÑ "äÐ7¸Ó>DØ;; ¨d¯i©i¨[¸Ð :Ó;Ð ;à;;Ð 0Ó1Ð 1ru c ó. t | j t « s7t dt |« j
|
dt | « j
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« t d| « } | j |« | j |« \ }}t | j t j | d¬« « }t j |D cg c] }| j j |z c}« }| | j }n| j |z }t" ||< |S c c}w )Núcannot subtract ú from rm ry rÈ )rz r{ rL r r r r r§ r r rº rÌ r r_ r ÚbaserI r )r r¦ r ru Ú new_i8_datar© Únew_datarv s rs Ú_sub_periodlikez&DatetimeLikeArrayMixin._sub_periodlike# së ô $**¤kÔ2ÜØ"¤4¨£;×#7Ñ#7Ð"8¸¼tÀD»z×?RÑ?RÐ>SÐTóð ô M 4Ó(Ø ×#Ñ# EÔ*à ×7Ñ7¸Ó>Ñ&Ü& t§y¡y´"·*±*¸h¸YÈdÔ2SÓT Ü88¸Ö E°ATYY^^¨aÓ /Ò EÓFà >à;;Dð ;; Ñ'DÜ Øùò Fs à Dc óØ |t j t j fv sJ t |« dk( r| j dk( r || |d « S t j dt | « j dt t « ¬« | j |j k( s J | j |j f« || j d« t j |« « }|S )aZ
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Add or subtract array-like of DateOffset objects
|
|
Parameters
|
----------
|
other : np.ndarray[object]
|
op : {operator.add, operator.sub}
|
|
Returns
|
-------
|
np.ndarray[object]
|
Except in fastpath case with length 1 where we operate on the
|
contained scalar.
|
r° r z)Adding/subtracting object-dtype array to z not vectorized.rÆ ÚO)rn ÚaddÚsubr¹ r· rÉ rÊ r r r< r@ rû rÿ rÌ r )r r¦ rr r s rs Ú_addsub_object_arrayz+DatetimeLikeArrayMixin._addsub_object_array< sÅ ð hll¤H§L¡LÐ1Ñ1Ð1Ð1Ü u:?tyy¨A~ñ dE !HÓ%Ð %ä Ø 7ÜDz×"Ñ"Ð#Ð#3ð 5ä Ü'Ó)õ
|
ð zzU[[Ò(ÐC¨4¯:©:°u·{±{Ð*CÓCÐ(á CÓ(¬"¯*©*°UÓ*;Ó<
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ØÐru r> c óÜ |dvrt d| dt | « « t t |« } || j « fd|i|¤}t | « j || j ¬« S )N> ÚcummaxÚcumminz Accumulation z not supported for r? rÈ )r r r rX r r r{ )r Únamer? r rr r s rs Ú _accumulatez"DatetimeLikeArrayMixin._accumulate` sm Ø Ð+Ñ +Üm¨D¨6Ð1DÄTÈ$ÃZÀLÐ QÓRÐ Rä Ô/°Ó 6ÙDIIKÑ9¨Ð9°&Ñ9äDz×%Ñ% f°D·J±JÐ%Ó?Ð?ru Ú__add__c ó¸ t |dd « }t |« }|t u r| j « }nUt |t
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t t j f« r| j |« }n t |t « r| j |« }nút |t t j f« r| j |« }nÇt |t « r3t! j" | j$ d« r| j' |« }nt! j( |« rjt | j$ t* « s t- | « t/ d| « }|j1 ||j$ j2 z t4 j6 « }nt! j" |d« r| j9 |« }nÝt; |« r!| j= |t4 j6 « }n±t! j" |d« st |t> « r| jA |« S tC |« rit | j$ t* « s t- | « t/ d| « }|j1 ||j$ j2 z t4 j6 « }ntD S t |t jF « r8t! j" |j$ d« rddl$m%} |jL |« S |S )Nr{ rò rm rí r r¨ )'r r` r rÄ rz r r rÌ r8 r¾ r r¸ r r9 r r r r r{ rµ Ú
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is_integerrL r( r Ú_addsub_int_array_or_scalarÚ_nrn rÐ rÀ rE rÒ rJ r rC ÚNotImplementedÚndarrayr rn rþ ©r r¦ Ú other_dtyper Úobjrn s rs rØ z DatetimeLikeArrayMixin.__add__i s ä e W¨dÓ3 Ü.¨uÓ5ð C<Ø]]_FÜ ¤¤i´· ± Ð @Ô AØ×3Ñ3°EÓ:FÜ zÔ *à×%Ñ% eÓ,FÜ ¤¬"¯-©-Ð 8Ô 9Ø×2Ñ2°5Ó9FÜ vÔ &¬3¯?©?¸4¿:¹:ÀsÔ+KØ×%Ñ% eÓ,FÜ ^^EÔ "ô djj¬+Ô6Ü.¨tÓ4Ð4Ü} dÓ+CØ×4Ñ4°U¸S¿Y¹Y¿\¹\Ñ5IÌ8Ï<É<ÓXFô __[¨#Ô .à×2Ñ2°5Ó9FÜ [Ô )à×.Ñ.¨u´h·l±lÓCFÜ __[¨#Ô .´*Ø ô3
|
ð ×/Ñ/°Ó6Ð 6Ü kÔ *Ü djj¬+Ô6Ü.¨tÓ4Ð4Ü} dÓ+CØ×4Ñ4°U¸S¿Y¹Y¿\¹\Ñ5IÌ8Ï<É<ÓXFô "Ð !ä fbjjÔ )¬c¯o©o¸f¿l¹lÈCÔ.PÝ 9à0>×0Ñ0°Ó8Ð 8Ø ru c ó$ | j |« S rp )rØ r¥ s rs Ú__radd__z DatetimeLikeArrayMixin.__radd__¡ s à||EÓ"Ð"ru Ú__sub__c óÆ t |dd « }t |« }|t u r| j « }n\t |t
|
t t j f« r| j | « }n#t |t « r| j | « }nÿt |t t j f« r| j |« }nÌt j |« rjt | j" t$ « s t' | « t) d| « }|j+ ||j" j, z t. j0 « }nMt |t2 « r| j5 |« }n*t j6 |d« r| j9 | « }n t; |« r!| j= |t. j0 « }nÔt j6 |d« st |t> « r| jA |« }nt |t$ « r| j5 |« }nztC |« rit | j" t$ « s t' | « t) d| « }|j+ ||j" j, z t. j0 « }ntD S t |t jF « r8t j6 |j" d« rddl$m%} |jL |« S |S )Nr{ rm rò rí r r¨ )'r r` r rÆ rz r r rÌ r8 r¾ r r¸ r r9 r£ r rÚ r{ rL r( r rÛ rÜ rn rÑ r rÍ r rÀ rE rÒ rJ r¦ rC rÝ rÞ r rn rþ rß s rs rä z DatetimeLikeArrayMixin.__sub__¥ s ä e W¨dÓ3 Ü.¨uÓ5ð C<Ø]]_FÜ ¤¤i´· ± Ð @Ô AØ×3Ñ3°U°FÓ;FÜ zÔ *à×%Ñ% u fÓ-FÜ ¤¬"¯-©-Ð 8Ô 9Ø×2Ñ2°5Ó9FÜ ^^EÔ "ô djj¬+Ô6Ü.¨tÓ4Ð4Ü} dÓ+CØ×4Ñ4°U¸S¿Y¹Y¿\¹\Ñ5IÌ8Ï<É<ÓXFä vÔ &Ø×)Ñ)¨%Ó0Fô __[¨#Ô .à×2Ñ2°E°6Ó:FÜ [Ô )à×.Ñ.¨u´h·l±lÓCFÜ __[¨#Ô .´*Ø ô3
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S ) Nr{ rí rò r r rÈ rÉ rn )r r r rz rJ r{ rØ r r r rl rþ r÷ r r r r rL r )r r¦ rà Ú other_is_dt64rl s rs Ú__rsub__z DatetimeLikeArrayMixin.__rsub__Ý s^ Ü e W¨dÓ3 ܨ °SÓ9ò
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¤KÔ 0´S·_±_À[ÐRUÔ5VäÐ .¬t°D«z×/BÑ/BÐ.CÀ6È%Ï+É+È Ð WÓXÐ XÜ __TZZ¨Ô -ÜÐ(¨$Ó/DØEU?Ð "ð Ð ru c óv | |z }|d d | d d t | j t « s|j | _ | S rp ©rz r{ rL r rÚ ©r r¦ r s rs Ú__iadd__z DatetimeLikeArrayMixin.__iadd__ÿ ó6 Ø Ø)Qä$**¤kÔ2à DJØ ru c óv | |z
|
}|d d | d d t | j t « s|j | _ | S rp rê rë s rs Ú__isub__z DatetimeLikeArrayMixin.__isub__ rí ru c ó& t | ||¬« S )N)ÚqsÚ interpolation)r× Ú _quantile)r rñ rò rÜ s rs ró z DatetimeLikeArrayMixin._quantile s ø ô wÑ B°mÐ ÓDÐDru ©Úaxisr? c óÖ t j d|« t j || j « t j
|
| j ||¬« }| j ||« S )a
|
Return the minimum value of the Array or minimum along
|
an axis.
|
|
See Also
|
--------
|
numpy.ndarray.min
|
Index.min : Return the minimum value in an Index.
|
Series.min : Return the minimum value in a Series.
|
r rô )ÚnvÚ validate_minÚvalidate_minmax_axisr· rS Únanminr Ú_wrap_reduction_result©r rõ r? r r s rs ÚminzDatetimeLikeArrayMixin.min óO ô FÔ#Ü
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× Ñ d§i¡iÔ0ä t}}°4ÀÔGØ×*Ñ*¨4°Ó8Ð8ru c óÖ t j d|« t j || j « t j
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|
Return the maximum value of the Array or maximum along
|
an axis.
|
|
See Also
|
--------
|
numpy.ndarray.max
|
Index.max : Return the maximum value in an Index.
|
Series.max : Return the maximum value in a Series.
|
r rô )r÷ Ú validate_maxrù r· rS Únanmaxr rû rü s rs ÚmaxzDatetimeLikeArrayMixin.max. rþ ru r )r? rõ c ó t | j t « r"t dt | « j
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d« t j | j ||| j « ¬« }| j ||« S )aÝ
|
Return the mean value of the Array.
|
|
Parameters
|
----------
|
skipna : bool, default True
|
Whether to ignore any NaT elements.
|
axis : int, optional, default 0
|
|
Returns
|
-------
|
scalar
|
Timestamp or Timedelta.
|
|
See Also
|
--------
|
numpy.ndarray.mean : Returns the average of array elements along a given axis.
|
Series.mean : Return the mean value in a Series.
|
|
Notes
|
-----
|
mean is only defined for Datetime and Timedelta dtypes, not for Period.
|
|
Examples
|
--------
|
For :class:`pandas.DatetimeIndex`:
|
|
>>> idx = pd.date_range('2001-01-01 00:00', periods=3)
|
>>> idx
|
DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
|
dtype='datetime64[ns]', freq='D')
|
>>> idx.mean()
|
Timestamp('2001-01-02 00:00:00')
|
|
For :class:`pandas.TimedeltaIndex`:
|
|
>>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D')
|
>>> tdelta_idx
|
TimedeltaIndex(['1 days', '2 days', '3 days'],
|
dtype='timedelta64[ns]', freq=None)
|
>>> tdelta_idx.mean()
|
Timedelta('2 days 00:00:00')
|
z mean is not implemented for zX since the meaning is ambiguous. An alternative is obj.to_timestamp(how='start').mean()©rõ r? rv ) rz r{ rL r r r rS Únanmeanr rP rû )r r? rõ r s rs ÚmeanzDatetimeLikeArrayMixin.mean@ su ôX djj¤+Ô .äØ.¬t°D«z×/BÑ/BÐ.Cð D7ð 7óð ô Ø MM ¨V¸$¿)¹)»+ô
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ð ×*Ñ*¨4°Ó8Ð8ru c óà t j d|« |#t |« | j k\ r t d« t j | j ||¬« }| j ||« S )Nr z abs(axis) must be less than ndimrô ) r÷ Úvalidate_medianÚabsr· r rS Ú nanmedianr rû rü s rs Úmedianz DatetimeLikeArrayMixin.mediany s^ ä
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×Ñ2vÔ&à Ð ¤ D£ ¨T¯Y©YÒ 6Ü Ð ?Ó@Ð @ä×!Ñ! $§-¡-°dÀ6ÔJØ×*Ñ*¨4°Ó8Ð8ru c ó d }|r| j « }t j | j d« |¬« }|j | j j
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« }t t j |« }| j |« S )Nry )rv )
|
rP rQ Úmoder| r r{ r rÌ rÞ r )r Údropnarv Úi8modesÚnpmodess rs Ú_modez DatetimeLikeArrayMixin._mode sf ØÙ Ø99;Dä // $§)¡)¨D£/¸Ô=Ø,,t}}× 2Ñ 2Ó3Ürzz 7Ó+Ø×&Ñ& wÓ/Ð/ru c óê | j }|j dk( rC|dv rt d| d« |dv rt j d| d| dt
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|¬« } | j |f|||d d|¤} | j | j v r| S | j dk( sJ |dv rvddlm} t | j t « r t d« t' d| « } d| j( d}| j |« } | j* | | j ¬« S | j | j j « } | j- | « S )Nrí )ÚsumÚprodÚcumsumÚcumprodÚvarÚskewz!datetime64 type does not support z operations)rL Úallr zh' with datetime64 dtypes is deprecated and will raise in a future version. Use (obj != pd.Timestamp(0)).z () instead.rÆ z Period type does not support ze' with PeriodDtype is deprecated and will raise in a future version. Use (obj != pd.Period(0, freq)).)r r r r z"timedelta64 type does not support rx r )ÚWrappedCythonOp)Úhowr÷ Úhas_dropped_na)Ú min_countÚngroupsÚcomp_idsrv )ÚstdÚsemr¨ z-'std' and 'sem' are not valid for PeriodDtyperB zm8[r rÈ )r{ r÷ r rÉ rÊ rË r@ rz rL r r| Úpandas.core.groupby.opsr Úget_kind_from_howÚ_cython_op_ndim_compatr Úcast_blocklistr rn r r' r r )r r r r r Úidsr r{ Únpvaluesr r÷ rr r rn Ú new_dtypes rs Ú _groupby_opz"DatetimeLikeArrayMixin._groupby_op s ð
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Ø ::Ò àÐIÑIÜ Ð"CÀCÀ5È Ð TÓUÐUØnÑ$ä Øuð DØDGÀ5È ðUä!Ü /Ó 1ö ô {Ô +àÐIÑIÜ Ð"?À¸uÀKÐ PÓQÐQØnÑ$ä Øuð GØGJÀeÈ;ðXä!Ü /Ó 1ö ð Ð8Ñ8Ü Ð"DÀSÀEÈÐ UÓVÐVð ==×%Ñ% hÓ/å;à ×0Ñ0°Ó5Ù ¨4ÀÔ Oà.R×.Ñ.Ø ð
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ð 66R×&Ñ&Ñ &ð Ð ð × Ñ 8Ò+Ð+Ð+Ø .Ñ Ý 9ä$**¤kÔ2Ü Ð OÓPÐPÜÐ8¸$Ó?DØ dii[¨Ð*IØ# ¨Ó3JØ->×-Ñ-¨jÀ
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Ø×&Ñ& zÓ2Ð2ru ©ÚreturnrD )NNF)r{ ú Dtype | Noner rD r+ ÚNone)r+ ztype[DatetimeLikeScalar])r r r+ Ú DTScalarOrNaT)r r. r+ z)np.int64 | np.datetime64 | np.timedelta64)r¦ r. r+ r- ©r+ r )r+ rh )r+ znpt.NDArray[np.int64])r½ z str | floatr+ únpt.NDArray[np.object_])F)rà rD )NN)r{ zNpDtype | Noner z bool | Noner+ r )rÒ r3 r+ r. )rÒ z(SequenceIndexer | PositionalIndexerTupler+ r4 )rÛ r1 r+ zSelf | DTScalarOrNaT)r+ r )rÛ z,int | Sequence[int] | Sequence[bool] | slicer z NaTType | Any | Sequence[Any]r+ r- ©r+ r- ©T)r rD ©r+ r4 )r{ zLiteral['M8[ns]']r+ rl )r{ zLiteral['m8[ns]']r+ rn ).)r{ r, r+ r) rp )r rD r rD )r rD r+ r )r rD )r+ z6np.int64 | np.datetime64 | np.timedelta64 | np.ndarray)r r) r+ únpt.NDArray[np.bool_])r+ r4 )r r r+ r )r+ z
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ð M3ð ð M3ð "÷M3ru r c ó0 e Zd ZdZ ed¬« dd« Zy)Ú DatelikeOpszK
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Common ops for DatetimeIndex/PeriodIndex, but not TimedeltaIndex.
|
zNhttps://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior)ÚURLc óÖ | j |t j ¬« }t « r(ddlm} t | |t j ¬« ¬« S |j t d¬« S )a°
|
Convert to Index using specified date_format.
|
|
Return an Index of formatted strings specified by date_format, which
|
supports the same string format as the python standard library. Details
|
of the string format can be found in `python string format
|
doc <%(URL)s>`__.
|
|
Formats supported by the C `strftime` API but not by the python string format
|
doc (such as `"%%R"`, `"%%r"`) are not officially supported and should be
|
preferably replaced with their supported equivalents (such as `"%%H:%%M"`,
|
`"%%I:%%M:%%S %%p"`).
|
|
Note that `PeriodIndex` support additional directives, detailed in
|
`Period.strftime`.
|
|
Parameters
|
----------
|
date_format : str
|
Date format string (e.g. "%%Y-%%m-%%d").
|
|
Returns
|
-------
|
ndarray[object]
|
NumPy ndarray of formatted strings.
|
|
See Also
|
--------
|
to_datetime : Convert the given argument to datetime.
|
DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.
|
DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
|
DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
|
Timestamp.strftime : Format a single Timestamp.
|
Period.strftime : Format a single Period.
|
|
Examples
|
--------
|
>>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"),
|
... periods=3, freq='s')
|
>>> rng.strftime('%%B %%d, %%Y, %%r')
|
Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
|
'March 10, 2018, 09:00:02 AM'],
|
dtype='object')
|
)r¾ r½ r )Ú StringDtype©rü rÈ Fró ) rÀ rÌ rO r r2 rC r) rÿ rÎ )r r¾ r rC s rs ÚstrftimezDatelikeOps.strftimeå sP ðb ×*Ñ*°{Ì2Ï6É6Ð*ÓRÜ Ô Ý *äF©+¼r¿v¹vÔ*FÔGÐ GØ}}V¨%}Ó0Ð0ru N)r¾ r r+ r0 )r r8 r9 r: r> rE r ru rs r@ r@ à s% ññ ð *ôò21ó ñ21ru r@ aO
|
Perform {op} operation on the data to the specified `freq`.
|
|
Parameters
|
----------
|
freq : str or Offset
|
The frequency level to {op} the index to. Must be a fixed
|
frequency like 'S' (second) not 'ME' (month end). See
|
:ref:`frequency aliases <timeseries.offset_aliases>` for
|
a list of possible `freq` values.
|
ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
|
Only relevant for DatetimeIndex:
|
|
- 'infer' will attempt to infer fall dst-transition hours based on
|
order
|
- bool-ndarray where True signifies a DST time, False designates
|
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
|
-------
|
DatetimeIndex, TimedeltaIndex, or Series
|
Index of the same type for a DatetimeIndex or TimedeltaIndex,
|
or a Series with the same index for a Series.
|
|
Raises
|
------
|
ValueError if the `freq` cannot be converted.
|
|
Notes
|
-----
|
If the timestamps have a timezone, {op}ing will take place relative to the
|
local ("wall") time and re-localized to the same timezone. When {op}ing
|
near daylight savings time, use ``nonexistent`` and ``ambiguous`` to
|
control the re-localization behavior.
|
|
Examples
|
--------
|
**DatetimeIndex**
|
|
>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')
|
>>> rng
|
DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',
|
'2018-01-01 12:01:00'],
|
dtype='datetime64[ns]', freq='min')
|
a >>> rng.round('h')
|
DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
|
'2018-01-01 12:00:00'],
|
dtype='datetime64[ns]', freq=None)
|
|
**Series**
|
|
>>> pd.Series(rng).dt.round("h")
|
0 2018-01-01 12:00:00
|
1 2018-01-01 12:00:00
|
2 2018-01-01 12:00:00
|
dtype: datetime64[ns]
|
|
When rounding near a daylight savings time transition, use ``ambiguous`` or
|
``nonexistent`` to control how the timestamp should be re-localized.
|
|
>>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam")
|
|
>>> rng_tz.floor("2h", ambiguous=False)
|
DatetimeIndex(['2021-10-31 02:00:00+01:00'],
|
dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
|
|
>>> rng_tz.floor("2h", ambiguous=True)
|
DatetimeIndex(['2021-10-31 02:00:00+02:00'],
|
dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
|
a >>> rng.floor('h')
|
DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',
|
'2018-01-01 12:00:00'],
|
dtype='datetime64[ns]', freq=None)
|
|
**Series**
|
|
>>> pd.Series(rng).dt.floor("h")
|
0 2018-01-01 11:00:00
|
1 2018-01-01 12:00:00
|
2 2018-01-01 12:00:00
|
dtype: datetime64[ns]
|
|
When rounding near a daylight savings time transition, use ``ambiguous`` or
|
``nonexistent`` to control how the timestamp should be re-localized.
|
|
>>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam")
|
|
>>> rng_tz.floor("2h", ambiguous=False)
|
DatetimeIndex(['2021-10-31 02:00:00+01:00'],
|
dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
|
|
>>> rng_tz.floor("2h", ambiguous=True)
|
DatetimeIndex(['2021-10-31 02:00:00+02:00'],
|
dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
|
a >>> rng.ceil('h')
|
DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
|
'2018-01-01 13:00:00'],
|
dtype='datetime64[ns]', freq=None)
|
|
**Series**
|
|
>>> pd.Series(rng).dt.ceil("h")
|
0 2018-01-01 12:00:00
|
1 2018-01-01 12:00:00
|
2 2018-01-01 13:00:00
|
dtype: datetime64[ns]
|
|
When rounding near a daylight savings time transition, use ``ambiguous`` or
|
``nonexistent`` to control how the timestamp should be re-localized.
|
|
>>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam")
|
|
>>> rng_tz.ceil("h", ambiguous=False)
|
DatetimeIndex(['2021-10-31 02:00:00+01:00'],
|
dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
|
|
>>> rng_tz.ceil("h", ambiguous=True)
|
DatetimeIndex(['2021-10-31 02:00:00+02:00'],
|
dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
|
c óÒ e Zd ZU dZded< dej df d&dZed « Z e
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d « Z e j d'd « Z e d(d
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« Ze ed)d « « Ze d*d « Zed+d « Zed,d« Zd-d.dZd Zd/ fd Zd Z eeez j7 d¬« « d0 d1d« Z eee z j7 d¬« « d0 d1d« Z eee z j7 d¬« « d0 d1d« Z dddd2d Z!dddd2d Z"d'd Z#d3d Z$d4 fd Z% d5 d6 fd! Z&e d7 d8 fd" « Z'd9d: fd# Z( d;d$Z)e
|
d<d%« Z* xZ+S )=rq zK
|
Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex.
|
znp.dtypeÚ_default_dtypeNFc óN t j t | « j dt t « ¬« | t |« }t |d¬« }t |t « r|j dt ¬« }t |dd « }|d u }|t j ur|nd }t |t | « « r|rn>| |j }n/|r-|j r!t! |« }t# ||j « }|*||j$ k7 rt' d| d |j$ « |j$ }|j( }n|
t |t* j, « r%|j$ j. d
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v r |j$ }nF| j0 }t |t* j, « r |j$ d k( r|j3 |« }t |t* j, « s8t5 d t |« j d t | « j d« |j6 dvr t5 d« |j$ d k( r|(| j0 }|j3 | j0 « }nqt j8 |d« r|j3 |« }nIt |t: « r9| j0 j. }| d|j< d}|j3 |« }| j? ||« }|dk( r"t5 dt | « j d« |r|jA « }|r?t! |« }|j$ j. dk( rt |tB « s t' d« tE jF | ||¬« || _$ | | t | « jK | |« y y y )NzV.__init__ is deprecated and will be removed in a future version. Use pd.array instead.rÆ Tr$ r rD rÚ údtype=z does not match data dtype ÚMmry zUnexpected type 'z'. 'values' must be a z6, ndarray, or Series or Index containing one of those.)r° é z.Only 1-dimensional input arrays are supported.rõ ú8[r Úinferz#Frequency inference not allowed in z$.__init__. Use 'pd.array()' instead.rò ú(TimedeltaArray/Index freq must be a Tick)r r{ )&rÉ rÊ r r rË r@ rG ra rz r^ r3 r r r Ú
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no_defaultr r# Ú_validate_inferred_freqr{ r r rÌ rÞ r÷ rG r| r r· r rJ r' Ú_validate_dtyper r r r rÚ Ú_validate_frequency) r r r{ r r rW Ú explicit_noner÷ r( s rs r zTimelikeOps.__init__¶ s9 ô äDz×"Ñ"Ð#ð $Að Aä Ü'Ó)õ
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ð Ð Ü Ó'Eä v°TÔ:Ü flÔ +Ø__ W´t_Ó<Fä ¨°Ó6 Ø Ø¤3§>¡>Ñ1t°tä fd 4jÔ )Ù àØ Ø {{Ù&++Ü Ü.¨t°V·[±[ÓAàÐ U¨f¯l©lÒ%:ä Ø UGÐ#>¸v¿|¹|¸nÐMóð ð LLEØ__Fà ]Ü&¤"§*¡*Ô-°&·,±,×2CÑ2CÀtÑ2KØ à ×+Ñ+Ü f¤b§j¡jÔ1°f·l±lÀdÒ6JØ #[[¨Ó /Fä&¤"§*¡*Ô-Ü Ø#¤D¨£L×$9Ñ$9Ð#:Ð:PÜ:×&Ñ&Ð'ð (+ð+óð ð
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;;fÑ $Ü Ð MÓNÐ Nà <<4Ò ð }Ø ×+Ñ+Ø T×%8Ñ%8Ó9Ü ¨Ô-Ø UÓ+ÜE¤?Ô3Ø×*Ñ*×/Ñ/Ø #f B u§z¡z l°!Ð 4 Ø YÓ/à×$Ñ$ V¨UÓ3à 7?Ü Ø5´d¸4³j×6IÑ6IÐ5Jð K,ð ,óð ñ
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Ø[[]FÙ Ü T?DØ||× Ñ CÒ'´
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¸4ÄÔ0FÜ Ð JÓKÐKä× Ñ t¨F¸%Õ@Ø
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à Ð TÐ%5Ü J× *Ñ *¨4°Õ 6ð &6Ð ru c ó t | « rp r )r r r{ s rs rQ zTimelikeOps._validate_dtype s ä! #Ó&Ð&ru c ó | j S )zK
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Return the frequency object if it is set, otherwise None.
|
©rÚ r s rs r zTimelikeOps.freq s ð
|
zzÐru c óì |kt |« }| j | |« | j j dk( rt |t
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« s t d« | j dkD r t d« || _ y )Nrò rN r° z Cannot set freq with ndim > 1)
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r# rR r{ r÷ rz r r r· r rÚ r s rs r zTimelikeOps.freq se à Ð Ü eÓ$EØ × $Ñ $ T¨5Ô 1Øzz #Ò%¬j¸ÄÔ.EÜ Ð JÓKÐKàyy1}Ü Ð!@ÓAÐAà
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ru c óR |d| _ y|dk( r(| j t | j « | _ yy|t j u ry| j 0t |« } t | « j | |fi |¤ || _ yt |« }t || j « y)zº
|
Constructor helper to pin the appropriate `freq` attribute. Assumes
|
that self._freq is currently set to any freq inferred in
|
_from_sequence_not_strict.
|
NrM )rÚ r# rW r rO r rR rP )r r Ú validate_kwdss rs Ú_maybe_pin_freqzTimelikeOps._maybe_pin_freq* s ð <à DJØ W_ð zzÐ!ô ' t×'9Ñ'9Ó :
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ð "ð S^^Ñ #ð Ø ZZÐ ô T?DØ *DJ× *Ñ *¨4°Ñ G¸ Ò GØ DJô T?DÜ # D¨$¯*©*Õ 5ru c ó |j }|j dk( s||j k( ry | j d|d dt |« ||j
|
d|¤}t j |j |j « st y# t $ r0}dt |« v r|t d| d|j « |d}~ww xY w)am
|
Validate that a frequency is compatible with the values of a given
|
Datetime Array/Index or Timedelta Array/Index
|
|
Parameters
|
----------
|
index : DatetimeIndex or TimedeltaIndex
|
The index on which to determine if the given frequency is valid
|
freq : DateOffset
|
The frequency to validate
|
r N)ÚstartÚendÚperiodsr r' z non-fixedúInferred frequency ú9 from passed values does not conform to passed frequency r ) rW rj rT Ú_generate_ranger¹ r' rÌ Ú array_equalrº r r )r Úindexr r rG Úon_freqr s rs rR z TimelikeOps._validate_frequencyK sÞ ð ×&Ñ&Ø ::?h¨$¯,©,Ò 6Øð Ø)c×)Ñ)ð ØAhØÜE
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ØØZZñ ð ñ Gô >> %§*¡*¨g¯l©lÔ;Ü Ð ð <øäò Øc #hÑ&ð ô Ø% h Zð 08Ø8<¿ ¹ °~ðGóð ð ûð ús A B Â CÂ+C Ã Cc ó t | « rp r )r r\ r] r^ r r r s rs ra zTimelikeOps._generate_rangew s ô " #Ó&Ð&ru c ó@ t | j j « S rp )r r r{ r s rs rø zTimelikeOps._creso s ä" 4§=¡=×#6Ñ#6Ó7Ð7ru c ó, t | j « S rp )Ú dtype_to_unitr{ r s rs r' zTimelikeOps.unit s ô
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TZZÓ(Ð(ru Tc ó¤ |dvr t d« t j | j j d| d« }t | j
|
||¬« }t | j t j « r |j }n#t d| « j }t ||¬« }t | « j ||| j ¬« S ) N)ÚsÚmsÚusÚnsz)Supported units are 's', 'ms', 'us', 'ns'rL r r" rl r r ) r rÌ r{ r÷ r r rz r rï rJ r r r )r r' r# r{ r½ r( rï s rs r& zTimelikeOps.as_unit s± Ø Ð.Ñ .Ü Ð HÓIÐ IäDJJOOÐ,¨B¨t¨f°AÐ6Ó7Ü(¨¯ © ¸ÈÔQ
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ä djj¤"§(¡(Ô +Ø"×(Ñ(Iäo tÓ,×/Ñ/BÜ'¨2°DÔ9Iô Dz×%Ñ%Ø i¨d¯i©ið &ó
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ð
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ru c óâ | j |j k7 rS| j |j k r | j |j « } | |fS |j | j « }| |fS rp )rø r& r' r¥ s rs r z"TimelikeOps._ensure_matching_resos s^ Ø ;;%,,Ò &à{{U\\Ò)Ø|| E§J¡JÓ/ð U{Ðð d§i¡iÓ0ØU{Ðru c óò |t j t j t j fv r2t |« dk( r$|d | u r t ||« | j fi |¤S t | ||g|¢i |¤S )Nr° r ) rÌ ÚisnanÚisinfÚisfiniter¹ r r r× Ú__array_ufunc__)r ÚufuncÚmethodÚinputsr rÜ s rs rs zTimelikeOps.__array_ufunc__ª sq ø à bhh¤§¡¬"¯+©+Ð6Ñ 6ÜF qÒ Øq TÑ!ð *75 &Ó)¨$¯-©-ÑB¸6ÑBÐ BäwÑ& u¨fÐH°vÒHÀÑHÐHru c óL t | j t « rOt d| « } | j d « }|j ||||« }|j | j ||¬« S | j d« }t t j |« }t || j « }|dk( r| j « S t |||« } | j | t ¬« }|j | j j « }| j# || j ¬« S )Nrl )Ú ambiguousÚ nonexistentry r ©rQ rÈ )rz r{ rJ r Ú tz_localizeÚ_roundrï r| rÌ rÞ r' rø r r% rR r r r )
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r r r rx ry Únaiver r ÚnanosÚ result_i8s
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rs r| zTimelikeOps._roundµ sú ä djj¤/Ô 2ä¨Ó.DØ×$Ñ$ TÓ*EØ\\ $¨¨i¸ÓEFØ×%Ñ%Ø 9¸+ð &ó ð ð 4 Übjj &Ó)Ü" 4¨¯©Ó5Ø A:à99;Ð Ü! &¨$°Ó6 Ø×)Ñ)¨)ÄÐ)ÓEØT]]× 0Ñ 0Ó1Ø× Ñ ¨d¯j©jÐ Ó9Ð9ru Úround)rr c óF | j |t j ||« S rp )r| r$ ÚNEAREST_HALF_EVEN©r r rx ry s rs r zTimelikeOps.roundË s ð {{4¤×!:Ñ!:¸IÀ{ÓSÐSru Úfloorc óF | j |t j ||« S rp )r| r$ Ú MINUS_INFTYr s rs r zTimelikeOps.floorÔ s ð {{4¤×!4Ñ!4°iÀÓMÐMru Úceilc óF | j |t j ||« S rp )r| r$ Ú
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PLUS_INFTYr s rs r zTimelikeOps.ceilÝ s ð {{4¤×!3Ñ!3°YÀ ÓLÐLru rô c ód t j | j ||| j « ¬« S ©Nr )rS Únananyr rP ©r rõ r? s rs rL zTimelikeOps.anyé s! ä}}T]]°¸fÈ4Ï9É9Ë;ÔWÐWru c ód t j | j ||| j « ¬« S r )rS Únanallr rP r s rs r zTimelikeOps.allí s# ô }}T]]°¸fÈ4Ï9É9Ë;ÔWÐWru c ó d | _ y rp rV r s rs ré z TimelikeOps._maybe_clear_freqõ s Ø
|
ru c ó |nnt | « dk( rDt |t « r4| j j dk( r7t |t
|
« s't d« |dk( sJ t | j « }| j « }||_
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|S )z×
|
Helper to get a view on the same data, with a new freq.
|
|
Parameters
|
----------
|
freq : DateOffset, None, or "infer"
|
|
Returns
|
-------
|
Same type as self
|
r rò rN rM ) r¹ rz r r{ r÷ r r r# rW r| rÚ )r r r s rs Ú
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_with_freqzTimelikeOps._with_freqø sz ð <à Ü Y!^¤
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¨4´Ô <àzz #Ò%¬j¸¼tÔ.DÜ Ð JÓKÐKð 7?Ð "?Ü T× /Ñ /Ó0DàiikØ Ø
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ru c ó t | j t j « r | j S t | « S rp )rz r{ rÌ r r× Ú_values_for_json)r rÜ s rs r z TimelikeOps._values_for_json s- ø ä djj¤"§(¡(Ô +Ø==Ð ÜwÑ'Ó)Ð)ru c ó^ | j mt j t | « t j ¬« }| j « }|r)| j j dk r|d d d
}|d d d
}||fS |r"t dt | « j d« t | - |¬« S )NrÈ r éÿÿÿÿzThe 'sort' keyword in zu.factorize is ignored unless arr.freq is not None. To factorize with sort, call pd.factorize(obj, sort=True) instead.)Úuse_na_sentinel) r rÌ Úaranger¹ Úintpr r³ ÚNotImplementedErrorr r r× Ú factorize)r r ÚsortÚcodesÚuniquesrÜ s rs r zTimelikeOps.factorize sª ø ð
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99Ð äIIc $i¬r¯w©wÔ7EØiikGÙ aØ d d Ø!¡$ B $-Ø'>Ð !á ô &Ø(¬¨d«×)<Ñ)<Ð(=ð >=ð =óð ô
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wÑ ° Ð ÓAÐAru c ó0 t | ||« }|d |dk( rt|D cg c] }t |« s| }}j Mt fd|D « « r9t |d d |dd « }t fd|D « « rj }||_ |S c c}w )Nr c 3 óP K | ] }|j j k( y wrp ©r )r² r© rá s rs r´ z0TimelikeOps._concat_same_type.<locals>.<genexpr>E s øè ø Ò+RÀ1¨A¯F©F°c·h±hÕ,>Ñ+Rùs #&r r° c 3 óZ K | ]" }|d d j z |d d k( $ yw)r r r° Nr¡ )r² Úpairrá s rs r´ z0TimelikeOps._concat_same_type.<locals>.<genexpr>G s/ øè ø ÒNÀtAwr{ S§X¡XÑ-°°a±¸±Õ;ÑNùs (+)r× Ú_concat_same_typer¹ r r ÚziprÚ ) r Ú to_concatrõ Únew_objr© Úpairsr rá rÜ s @rs r¤ z TimelikeOps._concat_same_type6 s ù ô 'Ñ+¨I°tÓ<àlà 19ð %.Ö8q´°QµÐ8IÐ8àxxÐ#¬Ó+RÈ Ô+RÔ(RÜI c rN¨I°a°b¨MÓ:ÜÓNÈÔNÔNØ "xxHØ$,GMØùò 9s
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¡B²Bc óJ t | |¬« }| j |_ |S )N)Úorder)r× r r rÚ )r rª r§ rÜ s rs r zTimelikeOps.copyL s# ø Ü', U,Ó+Ø Øru c
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óø |dk7 rt |s | j } n| j j « } t j | f||||||d|¤ |s| S t | « j | | j ¬« S )z2
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See NDFrame.interpolate.__doc__.
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Úlinear)ru rõ rc ÚlimitÚlimit_directionÚ
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limit_arearÈ )r r r rR Úinterpolate_2d_inplacer r r{ )
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r ru rõ rc r r® r¯ r r Úout_datas
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rs Ú interpolatezTimelikeOps.interpolateQ s ð XÒ Ü%Ð %áØ}}Hà}}×)Ñ)Ó+Hä×&Ñ&Ø ð
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ñ ØKÜDz×%Ñ% h°d·j±jÐ%ÓAÐAru c ó
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t j | j « sy| j }|t k7 }t | j « }t |« }t j |||z dk7 « j « dk( }|S )zÓ
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Check if we are round times at midnight (and no timezone), which will
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be given a more compact __repr__ than other cases. For TimedeltaArray
|
we are checking for multiples of 24H.
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Fr )
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r r r{ rº r r r" rÌ Ú logical_andr )r Ú
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values_intÚconsider_valuesrñ ÚppdÚ even_dayss rs Ú_is_dates_onlyzTimelikeOps._is_dates_onlyz ss ô tzzÔ*ààYY
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int | Noner+ r4 )r+ r7 r5 r2 )r' r r# rD r+ r4 )rt znp.ufuncru r )Úraiser¼ )rx r6 ry r7 r+ r4 )rõ r6 r? rD r+ rD r3 r/ )TF)r rD r rD )r )r¦ zSequence[Self]rõ r* r+ r4 )ÚC)rª r r+ r4 )
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ru r/ rõ r7 rc rk r rD r+ r4 r* ),r r8 r9 r: r; r rO r Ú classmethodrQ r< r Úsetterr rZ rR ra r? rø r' r& r rs r| r= Ú
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« } d}| |fS t | t « r&| j! « } | j « } d}| |fS t | t j" t$ f« st j& | « } | |fS t | t( « r7| j* j- | j. t0 ¬ « j2 } d}| |fS ) Nr{ r FzCannot create a z from a MultiIndex.Tr$ rô r rD rz )r rz rÍ ÚtuplerÌ r· rA rN r ra r^ r\ r{ r÷ r3 r Ú _maybe_convert_datelike_arrayrÞ r] r rM r* Útaker r Ú_values)r r Úcls_names rs Ú!ensure_arraylike_for_datetimelikerÊ s\ ô 4Ô !ä$¤¤u Ô.´2·7±7¸4³=ÀAÒ3Eä:Dä6°tÓ<ØÜ D-Ô (ÜÐ*¨8¨*Ð4GÐHÓIÐIä T°Ô6ä$ Ô%Ü4Ô,Ô-°$·*±*·/±/ÀTÑ2Ià}}W¬t}Ó4Øð :Ðô
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D.Ô )ð ×#Ñ# D§J¡J¼3Ð#Ó?×GÑGØà :Ðru c ó y rp r ©r^ s rs Úvalidate_periodsrÍ » ó àru c ó y rp r rÌ s rs rÍ rÍ À rÎ ru c óÜ | it j | « r1t j dt t « ¬« t | « } | S t j | « st d| « | S )a9
|
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
|
constructor, cast it to an integer.
|
|
Parameters
|
----------
|
periods : None, float, int
|
|
Returns
|
-------
|
periods : None or int
|
|
Raises
|
------
|
TypeError
|
if periods is None, float, or int
|
zNon-integer 'periods' in pd.date_range, pd.timedelta_range, pd.period_range, and pd.interval_range are deprecated and will raise in a future version.rÆ z periods must be a number, got ) r Úis_floatrÉ rÊ rË r@ r7 rÚ r rÌ s rs rÍ rÍ Å sj ð$ ÐÜ <<Ô Ü MMð2ô Ü+Ó-õ ô 'lGð Nô Ô(ÜÐ <¸W¸IÐ FÓGÐ GØ Nru c óV |&| | |k7 rt d| d| j « | |} | S )a
|
|
If the user passes a freq and another freq is inferred from passed data,
|
require that they match.
|
|
Parameters
|
----------
|
freq : DateOffset or None
|
inferred_freq : DateOffset or None
|
|
Returns
|
-------
|
freq : DateOffset or None
|
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Return the unit str corresponding to the dtype's resolution.
|
|
Parameters
|
----------
|
dtype : DatetimeTZDtype or np.dtype
|
If np.dtype, we assume it is a datetime64 dtype.
|
|
Returns
|
-------
|
str
|
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