Mh7L>%SrSSKJr SSKrSSKJrJrJrJrJ r J r J r SSK r SSK rSSKJrJr SSKJr SSKJr SSKJr SS KJrJrJr SS KJr SS K J!r!J"r"J#r# SS K$J%r% SS K&J'r'J(r(J)r) SSK*J+r+ SSK,J-r-J.r.J/r/ SSK0J1r1 SSK2J3r3J4r4J5r5 SSK6J7r7J8r8J9r9J:r:J;r;JJ?r? SSK@JArA SSKBJCrCJDrD \(a4SSKEJFrFJGrG SSKHJIrIJJrJJKrKJLrLJMrMJNrNJOrOJPrPJQrQJRrRJSrSJTrTJUrUJVrVJWrWJXrXJYrY SSKZJ[r[ 0r\S\]S'"SS5r^"SS\^5r_"SS 5r`"S!S"\`5rag)#z An interface for extending pandas with custom arrays. .. warning:: This is an experimental API and subject to breaking changes without warning. ) annotationsN) TYPE_CHECKINGAnyCallableClassVarLiteralcastoverload)algoslib)set_function_name)functionAbstractMethodError)Appender Substitutioncache_readonly)find_stack_level)validate_bool_kwargvalidate_fillna_kwargsvalidate_insert_loc)maybe_cast_pointwise_result) is_list_like is_scalar pandas_dtype)ExtensionDtype) ABCDataFrameABCIndex ABCSeriesisna) arraylikemissing roperator) duplicatedfactorize_arrayisin map_arraymoderankunique)quantile_with_mask)_fill_limit_area_1d) nargminmaxnargsort)IteratorSequence) ArrayLike AstypeArgAxisIntDtypeDtypeObj FillnaOptionsInterpolateOptions NumpySorterNumpyValueArrayLikePositionalIndexer ScalarIndexerSelfSequenceIndexerShapeSortKind TakeIndexernpt)Indexzdict[str, str]_extension_array_shared_docsc\rSrSr%SrSrSr\SSS.SbSjj5r\ScS j5r \SSS.SbS jj5r \S 5r \ SdS j5r \ SeS j5r SfSjr SgSjrShSjrSiSjrSjSjrSkSjrSkSjrSS\R*4SlSjjr\SmSj5r\SnSj5r\ShSj5r\ShSj5r\ShSj5r\ SoSpSjj5r\ SoSqSjj5r\ SoSrSjj5rSsSrSjjrStS jr\SuS!j5rSvS"jr SS#S$S%.SwS&jjr!SsSxS'jjr"SsSxS(jjr#SyS)jr$SSSS*.SzS+jjr%S{S|S,jjr&S}S-jr'S~SS/jjr(SSS0jjr)S}S1jr*SSS2jjr+SS3jr,SS4jr-SS5jr.SsSS6jjr/S7\0S8'\1"SS99\2"\0S85SSS:jj55r3SSS;.SS<jjr4S}S=jr5SSS>jjr6SS?jr7SS@jr8SSAjr9SSSBjjr:SSCjr;\SSDj5r\?SuSGj5r@SSH.SSIjjrASSSJ.SSKjjrBSL\CSM'SvSNjrDSSOjrESSPjrFSSQjrGSSRjrHSSSjrISSTjrJSSUjrKSSVjrLSWSXS.SSSY.SSZjjrM\SS[j5rNSS\jrOSsSS]jjrPSS^jrQSS_jrRSS`jrSSarTg)ExtensionArrayna Abstract base class for custom 1-D array types. pandas will recognize instances of this class as proper arrays with a custom type and will not attempt to coerce them to objects. They may be stored directly inside a :class:`DataFrame` or :class:`Series`. Attributes ---------- dtype nbytes ndim shape Methods ------- argsort astype copy dropna duplicated factorize fillna equals insert interpolate isin isna ravel repeat searchsorted shift take tolist unique view _accumulate _concat_same_type _explode _formatter _from_factorized _from_sequence _from_sequence_of_strings _hash_pandas_object _pad_or_backfill _reduce _values_for_argsort _values_for_factorize Notes ----- The interface includes the following abstract methods that must be implemented by subclasses: * _from_sequence * _from_factorized * __getitem__ * __len__ * __eq__ * dtype * nbytes * isna * take * copy * _concat_same_type * interpolate A default repr displaying the type, (truncated) data, length, and dtype is provided. It can be customized or replaced by by overriding: * __repr__ : A default repr for the ExtensionArray. * _formatter : Print scalars inside a Series or DataFrame. Some methods require casting the ExtensionArray to an ndarray of Python objects with ``self.astype(object)``, which may be expensive. When performance is a concern, we highly recommend overriding the following methods: * fillna * _pad_or_backfill * dropna * unique * factorize / _values_for_factorize * argsort, argmax, argmin / _values_for_argsort * searchsorted * map The remaining methods implemented on this class should be performant, as they only compose abstract methods. Still, a more efficient implementation may be available, and these methods can be overridden. One can implement methods to handle array accumulations or reductions. * _accumulate * _reduce One can implement methods to handle parsing from strings that will be used in methods such as ``pandas.io.parsers.read_csv``. * _from_sequence_of_strings This class does not inherit from 'abc.ABCMeta' for performance reasons. Methods and properties required by the interface raise ``pandas.errors.AbstractMethodError`` and no ``register`` method is provided for registering virtual subclasses. ExtensionArrays are limited to 1 dimension. They may be backed by none, one, or many NumPy arrays. For example, ``pandas.Categorical`` is an extension array backed by two arrays, one for codes and one for categories. An array of IPv6 address may be backed by a NumPy structured array with two fields, one for the lower 64 bits and one for the upper 64 bits. Or they may be backed by some other storage type, like Python lists. Pandas makes no assumptions on how the data are stored, just that it can be converted to a NumPy array. The ExtensionArray interface does not impose any rules on how this data is stored. However, currently, the backing data cannot be stored in attributes called ``.values`` or ``._values`` to ensure full compatibility with pandas internals. But other names as ``.data``, ``._data``, ``._items``, ... can be freely used. If implementing NumPy's ``__array_ufunc__`` interface, pandas expects that 1. You defer by returning ``NotImplemented`` when any Series are present in `inputs`. Pandas will extract the arrays and call the ufunc again. 2. You define a ``_HANDLED_TYPES`` tuple as an attribute on the class. Pandas inspect this to determine whether the ufunc is valid for the types present. See :ref:`extending.extension.ufunc` for more. By default, ExtensionArrays are not hashable. Immutable subclasses may override this behavior. Examples -------- Please see the following: https://github.com/pandas-dev/pandas/blob/main/pandas/tests/extension/list/array.py extensioniNFdtypecopyc[U5e)aI Construct a new ExtensionArray from a sequence of scalars. Parameters ---------- scalars : Sequence Each element will be an instance of the scalar type for this array, ``cls.dtype.type`` or be converted into this type in this method. dtype : dtype, optional Construct for this particular dtype. This should be a Dtype compatible with the ExtensionArray. copy : bool, default False If True, copy the underlying data. Returns ------- ExtensionArray Examples -------- >>> pd.arrays.IntegerArray._from_sequence([4, 5]) [4, 5] Length: 2, dtype: Int64 r)clsscalarsrJrKs I/var/www/html/env/lib/python3.13/site-packages/pandas/core/arrays/base.py_from_sequenceExtensionArray._from_sequence 6"#&&cURXSS9$![[4a e[a [R "S[ 5S9 ef=f)aL Strict analogue to _from_sequence, allowing only sequences of scalars that should be specifically inferred to the given dtype. Parameters ---------- scalars : sequence dtype : ExtensionDtype Raises ------ TypeError or ValueError Notes ----- This is called in a try/except block when casting the result of a pointwise operation. FrIzm_from_scalars should only raise ValueError or TypeError. Consider overriding _from_scalars where appropriate. stacklevel)rP ValueError TypeError Exceptionwarningswarnr)rMrNrJs rO _from_scalarsExtensionArray._from_scalars*sY( %%g%G GI&    MMG+-    s 9A c[U5e)a2 Construct a new ExtensionArray from a sequence of strings. Parameters ---------- strings : Sequence Each element will be an instance of the scalar type for this array, ``cls.dtype.type``. dtype : dtype, optional Construct for this particular dtype. This should be a Dtype compatible with the ExtensionArray. copy : bool, default False If True, copy the underlying data. Returns ------- ExtensionArray Examples -------- >>> pd.arrays.IntegerArray._from_sequence_of_strings(["1", "2", "3"]) [1, 2, 3] Length: 3, dtype: Int64 r)rMstringsrJrKs rO_from_sequence_of_strings(ExtensionArray._from_sequence_of_stringsJs:"#&&rSc[U5e)a Reconstruct an ExtensionArray after factorization. Parameters ---------- values : ndarray An integer ndarray with the factorized values. original : ExtensionArray The original ExtensionArray that factorize was called on. See Also -------- factorize : Top-level factorize method that dispatches here. ExtensionArray.factorize : Encode the extension array as an enumerated type. Examples -------- >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), ... pd.Interval(1, 5), pd.Interval(1, 5)]) >>> codes, uniques = pd.factorize(interv_arr) >>> pd.arrays.IntervalArray._from_factorized(uniques, interv_arr) [(0, 1], (1, 5]] Length: 2, dtype: interval[int64, right] r)rMvaluesoriginals rO_from_factorizedExtensionArray._from_factorizedirRrScgNselfitems rO __getitem__ExtensionArray.__getitem__ rScgrhrirjs rOrmrnrorSc[U5e)a Select a subset of self. Parameters ---------- item : int, slice, or ndarray * int: The position in 'self' to get. * slice: A slice object, where 'start', 'stop', and 'step' are integers or None * ndarray: A 1-d boolean NumPy ndarray the same length as 'self' * list[int]: A list of int Returns ------- item : scalar or ExtensionArray Notes ----- For scalar ``item``, return a scalar value suitable for the array's type. This should be an instance of ``self.dtype.type``. For slice ``key``, return an instance of ``ExtensionArray``, even if the slice is length 0 or 1. For a boolean mask, return an instance of ``ExtensionArray``, filtered to the values where ``item`` is True. rrjs rOrmrns>"$''rSc0[[U5S35e)a Set one or more values inplace. This method is not required to satisfy the pandas extension array interface. Parameters ---------- key : int, ndarray, or slice When called from, e.g. ``Series.__setitem__``, ``key`` will be one of * scalar int * ndarray of integers. * boolean ndarray * slice object value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object value or values to be set of ``key``. Returns ------- None z does not implement __setitem__.)NotImplementedErrortype)rkkeyvalues rO __setitem__ExtensionArray.__setitem__sV"T$ZL0P"QRRrSc[U5e)z4 Length of this array Returns ------- length : int rrks rO__len__ExtensionArray.__len__s"$''rSc#N# [[U55H nXv M g7f)z% Iterate over elements of the array. N)rangelen)rkis rO__iter__ExtensionArray.__iter__s!s4y!A'M"s#%c[U5(ak[U5(a[UR(dgXRRLd$[ XRR 5(a UR$gX:HR5$)z Return for `item in self`. F) rr! _can_hold_narJna_value isinstancert_hasnaanyrjs rO __contains__ExtensionArray.__contains__sc T??tDzz$$,,, 40Q0Q{{"L%%' 'rSc[U5e)z5 Return for `self == other` (element-wise equality). rrkothers rO__eq__ExtensionArray.__eq__"$''rSc X:H)$)z8 Return for `self != other` (element-wise in-equality). rirs rO__ne__ExtensionArray.__ne__s rSc[R"XS9nU(dU[RLaUR 5nU[RLaX4UR 5'U$)a Convert to a NumPy ndarray. This is similar to :meth:`numpy.asarray`, but may provide additional control over how the conversion is done. Parameters ---------- dtype : str or numpy.dtype, optional The dtype to pass to :meth:`numpy.asarray`. copy : bool, default False Whether to ensure that the returned value is a not a view on another array. Note that ``copy=False`` does not *ensure* that ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that a copy is made, even if not strictly necessary. na_value : Any, optional The value to use for missing values. The default value depends on `dtype` and the type of the array. Returns ------- numpy.ndarray rJ)npasarrayr no_defaultrKr!)rkrJrKrresults rOto_numpyExtensionArray.to_numpysJ:D. 83>>1[[]F 3>> )"*499;  rSc[U5e)z^ An instance of ExtensionDtype. Examples -------- >>> pd.array([1, 2, 3]).dtype Int64Dtype() rrzs rOrJExtensionArray.dtypeCrrSc[U54$)zm Return a tuple of the array dimensions. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.shape (3,) )rrzs rOshapeExtensionArray.shapeOsD |rScB[R"UR5$)z& The number of elements in the array. )rprodrrzs rOsizeExtensionArray.size\swwtzz""rScg)zx Extension Arrays are only allowed to be 1-dimensional. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.ndim 1 rirzs rOndimExtensionArray.ndimesrSc[U5e)zq The number of bytes needed to store this object in memory. Examples -------- >>> pd.array([1, 2, 3]).nbytes 27 rrzs rOnbytesExtensionArray.nbytesrs"$''rScgrhrirkrJrKs rOastypeExtensionArray.astyperorScgrhrirs rOrrrorScgrhrirs rOrrrorSTc[U5nXR:XaU(dU$UR5$[U[5(a UR 5nUR XUS9$[R"US5(aSSK J n UR XUS9$[R"US5(aSSK J n UR XUS9$U(d[R"XS9$[R"XUS9$)a Cast to a NumPy array or ExtensionArray with 'dtype'. Parameters ---------- dtype : str or dtype Typecode or data-type to which the array is cast. copy : bool, default True Whether to copy the data, even if not necessary. If False, a copy is made only if the old dtype does not match the new dtype. Returns ------- np.ndarray or pandas.api.extensions.ExtensionArray An ``ExtensionArray`` if ``dtype`` is ``ExtensionDtype``, otherwise a Numpy ndarray with ``dtype`` for its dtype. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr [1, 2, 3] Length: 3, dtype: Int64 Casting to another ``ExtensionDtype`` returns an ``ExtensionArray``: >>> arr1 = arr.astype('Float64') >>> arr1 [1.0, 2.0, 3.0] Length: 3, dtype: Float64 >>> arr1.dtype Float64Dtype() Otherwise, we will get a Numpy ndarray: >>> arr2 = arr.astype('float64') >>> arr2 array([1., 2., 3.]) >>> arr2.dtype dtype('float64') rIMr) DatetimeArraym)TimedeltaArrayr)rrJrKrrconstruct_array_typerPr is_np_dtypepandas.core.arraysrrrrarray)rkrJrKrMrrs rOrrsZU# JJ  yy{" e^ , ,,,.C%%dd%C C __UC ( ( 8 ///M M __UC ( ( 9!000N N::d0 088DD9 9rSc[U5e)a A 1-D array indicating if each value is missing. Returns ------- numpy.ndarray or pandas.api.extensions.ExtensionArray In most cases, this should return a NumPy ndarray. For exceptional cases like ``SparseArray``, where returning an ndarray would be expensive, an ExtensionArray may be returned. Notes ----- If returning an ExtensionArray, then * ``na_values._is_boolean`` should be True * `na_values` should implement :func:`ExtensionArray._reduce` * ``na_values.any`` and ``na_values.all`` should be implemented Examples -------- >>> arr = pd.array([1, 2, np.nan, np.nan]) >>> arr.isna() array([False, False, True, True]) rrzs rOr!ExtensionArray.isnas4"$''rScP[UR5R55$)zh Equivalent to `self.isna().any()`. Some ExtensionArray subclasses may be able to optimize this check. )boolr!rrzs rOrExtensionArray._hasnasDIIKOO%&&rSc.[R"U5$)a/ Return values for sorting. Returns ------- ndarray The transformed values should maintain the ordering between values within the array. See Also -------- ExtensionArray.argsort : Return the indices that would sort this array. Notes ----- The caller is responsible for *not* modifying these values in-place, so it is safe for implementers to give views on ``self``. Functions that use this (e.g. ``ExtensionArray.argsort``) should ignore entries with missing values in the original array (according to ``self.isna()``). This means that the corresponding entries in the returned array don't need to be modified to sort correctly. Examples -------- In most cases, this is the underlying Numpy array of the ``ExtensionArray``: >>> arr = pd.array([1, 2, 3]) >>> arr._values_for_argsort() array([1, 2, 3]) )rrrzs rO_values_for_argsort"ExtensionArray._values_for_argsortsBxx~rS quicksortlast) ascendingkind na_positionc [R"USU5nUR5n[UUUU[R "UR 55S9$)a" Return the indices that would sort this array. Parameters ---------- ascending : bool, default True Whether the indices should result in an ascending or descending sort. kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional Sorting algorithm. na_position : {'first', 'last'}, default 'last' If ``'first'``, put ``NaN`` values at the beginning. If ``'last'``, put ``NaN`` values at the end. *args, **kwargs: Passed through to :func:`numpy.argsort`. Returns ------- np.ndarray[np.intp] Array of indices that sort ``self``. If NaN values are contained, NaN values are placed at the end. See Also -------- numpy.argsort : Sorting implementation used internally. Examples -------- >>> arr = pd.array([3, 1, 2, 5, 4]) >>> arr.argsort() array([1, 2, 0, 4, 3]) ri)rrrmask)nvvalidate_argsort_with_ascendingrr/rrr!)rkrrrkwargsrcs rOargsortExtensionArray.argsort sRZ66y"fM ))+ #DIIK(   rScn[US5 U(dUR(a[e[US5$)a| Return the index of minimum value. In case of multiple occurrences of the minimum value, the index corresponding to the first occurrence is returned. Parameters ---------- skipna : bool, default True Returns ------- int See Also -------- ExtensionArray.argmax : Return the index of the maximum value. Examples -------- >>> arr = pd.array([3, 1, 2, 5, 4]) >>> arr.argmin() 1 skipnaargminrrrsr.rkrs rOrExtensionArray.argminX*: FH-$++% %$))rScn[US5 U(dUR(a[e[US5$)a| Return the index of maximum value. In case of multiple occurrences of the maximum value, the index corresponding to the first occurrence is returned. Parameters ---------- skipna : bool, default True Returns ------- int See Also -------- ExtensionArray.argmin : Return the index of the minimum value. Examples -------- >>> arr = pd.array([3, 1, 2, 5, 4]) >>> arr.argmax() 3 rargmaxrrs rOrExtensionArray.argmaxzrrSc D[[U5RS35e)a See DataFrame.interpolate.__doc__. Examples -------- >>> arr = pd.arrays.NumpyExtensionArray(np.array([0, 1, np.nan, 3])) >>> arr.interpolate(method="linear", ... limit=3, ... limit_direction="forward", ... index=pd.Index([1, 2, 3, 4]), ... fill_value=1, ... copy=False, ... axis=0, ... limit_area="inside" ... ) [0.0, 1.0, 2.0, 3.0] Length: 4, dtype: float64 z does not implement interpolate)rsrt__name__) rkmethodaxisindexlimitlimit_direction limit_arearKrs rO interpolateExtensionArray.interpolates(@"Dz""##B C  rS)rrrKc$[U5R[RLa{[U5R[RLaU[R "S[ [5S9 Ub![[U5RS35eURXS9$UR5nUR5(a[R"U5n[R"U5nUb UR!5(d [#Xs5 US:Xa$[$R&"XrS9nUR)USS 9$[$R&"USSS 2US9SSS 2nUSSS 2R)USS 9$U(dU$UR+5n U $) a& Pad or backfill values, used by Series/DataFrame ffill and bfill. Parameters ---------- method : {'backfill', 'bfill', 'pad', 'ffill'} Method to use for filling holes in reindexed Series: * pad / ffill: propagate last valid observation forward to next valid. * backfill / bfill: use NEXT valid observation to fill gap. limit : int, default None This is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. copy : bool, default True Whether to make a copy of the data before filling. If False, then the original should be modified and no new memory should be allocated. For ExtensionArray subclasses that cannot do this, it is at the author's discretion whether to ignore "copy=False" or to raise. The base class implementation ignores the keyword if any NAs are present. Returns ------- Same type as self Examples -------- >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan]) >>> arr._pad_or_backfill(method="backfill", limit=1) [, 2, 2, 3, , ] Length: 6, dtype: Int64 zExtensionArray.fillna 'method' keyword is deprecated. In a future version. arr._pad_or_backfill will be called instead. 3rd-party ExtensionArray authors need to implement _pad_or_backfill.rUNz does not implement limit_area (added in pandas 2.2). 3rd-party ExtnsionArray authors need to add this argument to _pad_or_backfill.)rrpadrT allow_fill)rtfillnarF_pad_or_backfillrZr[DeprecationWarningrrsrr!rr#clean_fill_methodrrallr-libalgosget_fill_indexertakerK) rkrrrrKrmethnpmaskindexer new_valuess rOrExtensionArray._pad_or_backfillsif J  ^%:%: :T ++~/N/NN MM$#+-  %)Dz**+,EE ;;f;: :yy{ 88::,,V4DZZ%F%fjjll#F7u}"33FHyyTy::#33F4R4LNtQStTDbDzw4@@ JrScUb9[R"S[U5RS3[[ 5S9 [ X5upUR5n[R"X[U55nUR5(aUb[R"U5n[R"U5nUS:Xa$[R "XsS9nUR#USS9$[R "USSS 2US9SSS 2nUSSS 2R#USS9$U(dUSSn OUR%5n XU'U $U(dUSSn U $UR%5n U $) a< Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, array-like If a scalar value is passed it is used to fill all missing values. Alternatively, an array-like "value" can be given. It's expected that the array-like have the same length as 'self'. method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None Method to use for filling holes in reindexed Series: * pad / ffill: propagate last valid observation forward to next valid. * backfill / bfill: use NEXT valid observation to fill gap. .. deprecated:: 2.1.0 limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. .. deprecated:: 2.1.0 copy : bool, default True Whether to make a copy of the data before filling. If False, then the original should be modified and no new memory should be allocated. For ExtensionArray subclasses that cannot do this, it is at the author's discretion whether to ignore "copy=False" or to raise. The base class implementation ignores the keyword in pad/backfill cases. Returns ------- ExtensionArray With NA/NaN filled. Examples -------- >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan]) >>> arr.fillna(0) [0, 0, 2, 3, 0, 0] Length: 6, dtype: Int64 NzThe 'method' keyword in z>.fillna is deprecated and will be removed in a future version.rUrrTrr)rZr[rtr FutureWarningrrr!r#check_value_sizerrrrrrrrrK) rkrvrrrKrrrrrs rOrExtensionArray.fillnascn   MM*4:+>+>*?@FF+-  /u= yy{(( T  88::!008D)5=&77LG99W9>>'77tt ERSWUWSWXG":??7t?DD!%aJ!%J#(4  !!W "YY[ rSc(XR5)$)z Return ExtensionArray without NA values. Returns ------- Examples -------- >>> pd.array([1, 2, np.nan]).dropna() [1, 2] Length: 2, dtype: Int64 r rzs rOdropnaExtensionArray.dropnasYY[L!!rSkeepcnUR5R[RSS9n[ XUS9$)a Return boolean ndarray denoting duplicate values. Parameters ---------- keep : {'first', 'last', False}, default 'first' - ``first`` : Mark duplicates as ``True`` except for the first occurrence. - ``last`` : Mark duplicates as ``True`` except for the last occurrence. - False : Mark all duplicates as ``True``. Returns ------- ndarray[bool] Examples -------- >>> pd.array([1, 1, 2, 3, 3], dtype="Int64").duplicated() array([False, True, False, False, True]) F)rK)rcrr)r!rrbool_r%)rkrrs rOr%ExtensionArray.duplicateds0,yy{!!"((!7t< len(self)``, then an array of size len(self) is returned, with all values filled with ``self.dtype.na_value``. For 2-dimensional ExtensionArrays, we are always shifting along axis=0. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.shift(2) [, , 1] Length: 3, dtype: Int64 rrN) rrKr!rJrrPminabs_concat_same_type)rkperiods fill_valueemptyabs rOshiftExtensionArray.shiftsV4yyGqL99;    ,,J## L3s7|SY7 7tzz$  Q;AYwhAS\^$AA%%qf--rScp[UR[55nURXRS9$)z Compute the ExtensionArray of unique values. Returns ------- pandas.api.extensions.ExtensionArray Examples -------- >>> arr = pd.array([1, 2, 3, 1, 2, 3]) >>> arr.unique() [1, 2, 3] Length: 3, dtype: Int64 r)r+robjectrPrJ)rkuniquess rOr+ExtensionArray.uniques/ V,-""7**"==rScUR[5n[U[5(aUR[5nUR XUS9$)a Find indices where elements should be inserted to maintain order. Find the indices into a sorted array `self` (a) such that, if the corresponding elements in `value` were inserted before the indices, the order of `self` would be preserved. Assuming that `self` is sorted: ====== ================================ `side` returned index `i` satisfies ====== ================================ left ``self[i-1] < value <= self[i]`` right ``self[i-1] <= value < self[i]`` ====== ================================ Parameters ---------- value : array-like, list or scalar Value(s) to insert into `self`. side : {'left', 'right'}, optional If 'left', the index of the first suitable location found is given. If 'right', return the last such index. If there is no suitable index, return either 0 or N (where N is the length of `self`). sorter : 1-D array-like, optional Optional array of integer indices that sort array a into ascending order. They are typically the result of argsort. Returns ------- array of ints or int If value is array-like, array of insertion points. If value is scalar, a single integer. See Also -------- numpy.searchsorted : Similar method from NumPy. Examples -------- >>> arr = pd.array([1, 2, 3, 5]) >>> arr.searchsorted([4]) array([3]) )sidesorter)rr rrF searchsorted)rkrvrrarrs rOrExtensionArray.searchsortedsDnkk&! e^ , ,LL(E@@rSc[U5[U5:wag[[U5nURUR:wag[ U5[ U5:wagX:Hn[ U[5(aUR S5nUR5UR5-n[X#-R55$)a Return if another array is equivalent to this array. Equivalent means that both arrays have the same shape and dtype, and all values compare equal. Missing values in the same location are considered equal (in contrast with normal equality). Parameters ---------- other : ExtensionArray Array to compare to this Array. Returns ------- boolean Whether the arrays are equivalent. Examples -------- >>> arr1 = pd.array([1, 2, np.nan]) >>> arr2 = pd.array([1, 2, np.nan]) >>> arr1.equals(arr2) True F) rtr rFrJrrrr!rr)rkr equal_valuesequal_nas rOequalsExtensionArray.equals6s2 :e $^U+ :: $ Y#e* $=L,77+2259 yy{UZZ\1H05578 8rScB[[R"U5U5$)a^ Pointwise comparison for set containment in the given values. Roughly equivalent to `np.array([x in values for x in self])` Parameters ---------- values : np.ndarray or ExtensionArray Returns ------- np.ndarray[bool] Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.isin([1]) [True, False, False] Length: 3, dtype: boolean )r'rr)rkrcs rOr'ExtensionArray.isin_s,BJJt$f--rScLUR[5[R4$)a- Return an array and missing value suitable for factorization. Returns ------- values : ndarray An array suitable for factorization. This should maintain order and be a supported dtype (Float64, Int64, UInt64, String, Object). By default, the extension array is cast to object dtype. na_value : object The value in `values` to consider missing. This will be treated as NA in the factorization routines, so it will be coded as `-1` and not included in `uniques`. By default, ``np.nan`` is used. Notes ----- The values returned by this method are also used in :func:`pandas.util.hash_pandas_object`. If needed, this can be overridden in the ``self._hash_pandas_object()`` method. Examples -------- >>> pd.array([1, 2, 3])._values_for_factorize() (array([1, 2, 3], dtype=object), nan) )rr rnanrzs rO_values_for_factorize$ExtensionArray._values_for_factorizews6{{6"BFF**rScfUR5up#[X!US9upEURXP5nXF4$)al Encode the extension array as an enumerated type. Parameters ---------- use_na_sentinel : bool, default True If True, the sentinel -1 will be used for NaN values. If False, NaN values will be encoded as non-negative integers and will not drop the NaN from the uniques of the values. .. versionadded:: 1.5.0 Returns ------- codes : ndarray An integer NumPy array that's an indexer into the original ExtensionArray. uniques : ExtensionArray An ExtensionArray containing the unique values of `self`. .. note:: uniques will *not* contain an entry for the NA value of the ExtensionArray if there are any missing values present in `self`. See Also -------- factorize : Top-level factorize method that dispatches here. Notes ----- :meth:`pandas.factorize` offers a `sort` keyword as well. Examples -------- >>> idx1 = pd.PeriodIndex(["2014-01", "2014-01", "2014-02", "2014-02", ... "2014-03", "2014-03"], freq="M") >>> arr, idx = idx1.factorize() >>> arr array([0, 0, 1, 1, 2, 2]) >>> idx PeriodIndex(['2014-01', '2014-02', '2014-03'], dtype='period[M]') )use_na_sentinelr)rr&re)rkr"rrcodesr uniques_eas rO factorizeExtensionArray.factorizesAp224 ( 8 **79   rSa; Repeat elements of a %(klass)s. Returns a new %(klass)s where each element of the current %(klass)s is repeated consecutively a given number of times. Parameters ---------- repeats : int or array of ints The number of repetitions for each element. This should be a non-negative integer. Repeating 0 times will return an empty %(klass)s. axis : None Must be ``None``. Has no effect but is accepted for compatibility with numpy. Returns ------- %(klass)s Newly created %(klass)s with repeated elements. See Also -------- Series.repeat : Equivalent function for Series. Index.repeat : Equivalent function for Index. numpy.repeat : Similar method for :class:`numpy.ndarray`. ExtensionArray.take : Take arbitrary positions. Examples -------- >>> cat = pd.Categorical(['a', 'b', 'c']) >>> cat ['a', 'b', 'c'] Categories (3, object): ['a', 'b', 'c'] >>> cat.repeat(2) ['a', 'a', 'b', 'b', 'c', 'c'] Categories (3, object): ['a', 'b', 'c'] >>> cat.repeat([1, 2, 3]) ['a', 'b', 'b', 'c', 'c', 'c'] Categories (3, object): ['a', 'b', 'c'] repeat)klassc[R"SSU05 [R"[ U55R U5nUR U5$)Nrir)rvalidate_repeatrarangerr'r)rkrepeatsrinds rOr'ExtensionArray.repeatsC 2~.iiD "))'2yy~rS)rrc[U5e)a Take elements from an array. Parameters ---------- indices : sequence of int or one-dimensional np.ndarray of int Indices to be taken. allow_fill : bool, default False How to handle negative values in `indices`. * False: negative values in `indices` indicate positional indices from the right (the default). This is similar to :func:`numpy.take`. * True: negative values in `indices` indicate missing values. These values are set to `fill_value`. Any other other negative values raise a ``ValueError``. fill_value : any, optional Fill value to use for NA-indices when `allow_fill` is True. This may be ``None``, in which case the default NA value for the type, ``self.dtype.na_value``, is used. For many ExtensionArrays, there will be two representations of `fill_value`: a user-facing "boxed" scalar, and a low-level physical NA value. `fill_value` should be the user-facing version, and the implementation should handle translating that to the physical version for processing the take if necessary. Returns ------- ExtensionArray Raises ------ IndexError When the indices are out of bounds for the array. ValueError When `indices` contains negative values other than ``-1`` and `allow_fill` is True. See Also -------- numpy.take : Take elements from an array along an axis. api.extensions.take : Take elements from an array. Notes ----- ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``, ``iloc``, when `indices` is a sequence of values. Additionally, it's called by :meth:`Series.reindex`, or any other method that causes realignment, with a `fill_value`. Examples -------- Here's an example implementation, which relies on casting the extension array to object dtype. This uses the helper method :func:`pandas.api.extensions.take`. .. code-block:: python def take(self, indices, allow_fill=False, fill_value=None): from pandas.core.algorithms import take # If the ExtensionArray is backed by an ndarray, then # just pass that here instead of coercing to object. data = self.astype(object) if allow_fill and fill_value is None: fill_value = self.dtype.na_value # fill value should always be translated from the scalar # type for the array, to the physical storage type for # the data, before passing to take. result = take(data, indices, fill_value=fill_value, allow_fill=allow_fill) return self._from_sequence(result, dtype=self.dtype) r)rkindicesrrs rOrExtensionArray.take sz"$''rSc[U5e)z Return a copy of the array. Returns ------- ExtensionArray Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr2 = arr.copy() >>> arr[0] = 2 >>> arr2 [1, 2, 3] Length: 3, dtype: Int64 rrzs rOrKExtensionArray.copyks$"$''rSc(Ub [U5eUSS$)a* Return a view on the array. Parameters ---------- dtype : str, np.dtype, or ExtensionDtype, optional Default None. Returns ------- ExtensionArray or np.ndarray A view on the :class:`ExtensionArray`'s data. Examples -------- This gives view on the underlying data of an ``ExtensionArray`` and is not a copy. Modifications on either the view or the original ``ExtensionArray`` will be reflectd on the underlying data: >>> arr = pd.array([1, 2, 3]) >>> arr2 = arr.view() >>> arr[0] = 2 >>> arr2 [2, 2, 3] Length: 3, dtype: Int64 N)rs)rkrJs rOviewExtensionArray.views@  %e, ,AwrScURS:aUR5$SSKJn U"XR 5SS9R S5nS[ U5RS3nUR5nUUS U3$) Nrrformat_object_summaryFindent_for_name,   ) r_repr_2dpandas.io.formats.printingr9 _formatterrstriprtr_get_repr_footer)rkr9data class_namefooters rO__repr__ExtensionArray.__repr__s~ 99q===? "D % //#U &. d,,-S1 &&(dV2fX..rScURS:aSURSUR3$S[U5SUR3$)NrzShape: z , dtype: zLength: )rrrJrrzs rOrCExtensionArray._get_repr_footersC 99q=TZZL $**> >#d)Idjj\::rSc SSKJn UVs/sH'nU"X R5SS9RS5PM) nnSR U5nS[ U5R S3nUR5nUS US U3$s snf) Nrr8Fr:r<z, r=>z [ z ] )r@r9rArBjoinrtrrC)rkr9xlinesrDrErFs rOr?ExtensionArray._repr_2dsD   "!__%6 N U U   zz% d,,-Q/ &&(U4&fX66 s.A?c(U(a[$[$)a Formatting function for scalar values. This is used in the default '__repr__'. The returned formatting function receives instances of your scalar type. Parameters ---------- boxed : bool, default False An indicated for whether or not your array is being printed within a Series, DataFrame, or Index (True), or just by itself (False). This may be useful if you want scalar values to appear differently within a Series versus on its own (e.g. quoted or not). Returns ------- Callable[[Any], str] A callable that gets instances of the scalar type and returns a string. By default, :func:`repr` is used when ``boxed=False`` and :func:`str` is used when ``boxed=True``. Examples -------- >>> class MyExtensionArray(pd.arrays.NumpyExtensionArray): ... def _formatter(self, boxed=False): ... return lambda x: '*' + str(x) + '*' if boxed else repr(x) + '*' >>> MyExtensionArray(np.array([1, 2, 3, 4])) [1*, 2*, 3*, 4*] Length: 4, dtype: int64 )strrepr)rkboxeds rOrAExtensionArray._formattersD J rSc USS$)a Return a transposed view on this array. Because ExtensionArrays are always 1D, this is a no-op. It is included for compatibility with np.ndarray. Returns ------- ExtensionArray Examples -------- >>> pd.array([1, 2, 3]).transpose() [1, 2, 3] Length: 3, dtype: Int64 Nri)rkaxess rO transposeExtensionArray.transposes $AwrSc"UR5$rh)rXrzs rOTExtensionArray.T s~~rScU$)a| Return a flattened view on this array. Parameters ---------- order : {None, 'C', 'F', 'A', 'K'}, default 'C' Returns ------- ExtensionArray Notes ----- - Because ExtensionArrays are 1D-only, this is a no-op. - The "order" argument is ignored, is for compatibility with NumPy. Examples -------- >>> pd.array([1, 2, 3]).ravel() [1, 2, 3] Length: 3, dtype: Int64 ri)rkorders rOravelExtensionArray.ravels 0 rSc[U5e)aJ Concatenate multiple array of this dtype. Parameters ---------- to_concat : sequence of this type Returns ------- ExtensionArray Examples -------- >>> arr1 = pd.array([1, 2, 3]) >>> arr2 = pd.array([4, 5, 6]) >>> pd.arrays.IntegerArray._concat_same_type([arr1, arr2]) [1, 2, 3, 4, 5, 6] Length: 6, dtype: Int64 r)rM to_concats rOr ExtensionArray._concat_same_type*rRrSc.URR$rh)rJrrzs rOrExtensionArray._can_hold_naLszz&&&rSrc 8[SUSUR35e)a Return an ExtensionArray performing an accumulation operation. The underlying data type might change. Parameters ---------- name : str Name of the function, supported values are: - cummin - cummax - cumsum - cumprod skipna : bool, default True If True, skip NA values. **kwargs Additional keyword arguments passed to the accumulation function. Currently, there is no supported kwarg. Returns ------- array Raises ------ NotImplementedError : subclass does not define accumulations Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr._accumulate(name='cumsum') [1, 3, 6] Length: 3, dtype: Int64 zcannot perform z with type )rsrJ)rknamerrs rO _accumulateExtensionArray._accumulatePs!L"OD6TZZL"QRRrS)rkeepdimsc [XS5nUc2[S[U5RSURSUS35eU"SSU0UD6nU(a[ R "U/5nU$)a Return a scalar result of performing the reduction operation. Parameters ---------- name : str Name of the function, supported values are: { any, all, min, max, sum, mean, median, prod, std, var, sem, kurt, skew }. skipna : bool, default True If True, skip NaN values. keepdims : bool, default False If False, a scalar is returned. If True, the result has dimension with size one along the reduced axis. .. versionadded:: 2.1 This parameter is not required in the _reduce signature to keep backward compatibility, but will become required in the future. If the parameter is not found in the method signature, a FutureWarning will be emitted. **kwargs Additional keyword arguments passed to the reduction function. Currently, `ddof` is the only supported kwarg. Returns ------- scalar Raises ------ TypeError : subclass does not define reductions Examples -------- >>> pd.array([1, 2, 3])._reduce("min") 1 N'z ' with dtype z does not support reduction 'rri)getattrrXrtrrJrr)rkrhrrkrrrs rO_reduceExtensionArray._reducexs}Pt4( <DJ''( djj\B//3fA7 .V.v. XXvh'F rSzClassVar[None]__hash__c.[R"U5$)a Specify how to render our entries in to_json. Notes ----- The dtype on the returned ndarray is not restricted, but for non-native types that are not specifically handled in objToJSON.c, to_json is liable to raise. In these cases, it may be safer to return an ndarray of strings. )rrrzs rO_values_for_jsonExtensionArray._values_for_jsonszz$rSc@SSKJn UR5upVU"XQX#S9$)a Hook for hash_pandas_object. Default is to use the values returned by _values_for_factorize. Parameters ---------- encoding : str Encoding for data & key when strings. hash_key : str Hash_key for string key to encode. categorize : bool Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values. Returns ------- np.ndarray[uint64] Examples -------- >>> pd.array([1, 2])._hash_pandas_object(encoding='utf-8', ... hash_key="1000000000000000", ... categorize=False ... ) array([ 6238072747940578789, 15839785061582574730], dtype=uint64) r) hash_array)encodinghash_key categorize)pandas.core.util.hashingrvr)rkrwrxryrvrc_s rO_hash_pandas_object"ExtensionArray._hash_pandas_objects)< 8..0    rScUR5n[R"[U54[RS9nX4$)a Transform each element of list-like to a row. For arrays that do not contain list-like elements the default implementation of this method just returns a copy and an array of ones (unchanged index). Returns ------- ExtensionArray Array with the exploded values. np.ndarray[uint64] The original lengths of each list-like for determining the resulting index. See Also -------- Series.explode : The method on the ``Series`` object that this extension array method is meant to support. Examples -------- >>> import pyarrow as pa >>> a = pd.array([[1, 2, 3], [4], [5, 6]], ... dtype=pd.ArrowDtype(pa.list_(pa.int64()))) >>> a._explode() ( [1, 2, 3, 4, 5, 6] Length: 6, dtype: int64[pyarrow], array([3, 1, 2], dtype=int32)) )rrJ)rKronesruint64)rkrccountss rO_explodeExtensionArray._explodes1>D |299=~rScURS:a UVs/sHoR5PM sn$[U5$s snf)a  Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns ------- list Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.tolist() [1, 2, 3] r)rtolistlist)rkrNs rOrExtensionArray.tolist s7$ 99q=(,-1HHJ- -Dz.s<c[R"[R"[U55U5nUR U5$rh)rdeleter+rr)rklocrs rOrExtensionArray.delete s.))BIIc$i0#6yy!!rSc[U[U55n[U5RU/URS9n[U5R USUX0US/5$)a Insert an item at the given position. Parameters ---------- loc : int item : scalar-like Returns ------- same type as self Notes ----- This method should be both type and dtype-preserving. If the item cannot be held in an array of this type/dtype, either ValueError or TypeError should be raised. The default implementation relies on _from_sequence to raise on invalid items. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.insert(2, -1) [1, 2, -1, 3] Length: 4, dtype: Int64 rN)rrrtrPrJr)rkrrlitem_arrs rOinsertExtensionArray.insert$sY<"#s4y1:,,dV4::,FDz++T$3Z:,NOOrSc:[U5(aX!nOUnX0U'g)aq Analogue to np.putmask(self, mask, value) Parameters ---------- mask : np.ndarray[bool] value : scalar or listlike If listlike, must be arraylike with same length as self. Returns ------- None Notes ----- Unlike np.putmask, we do not repeat listlike values with mismatched length. 'value' should either be a scalar or an arraylike with the same length as self. N)r)rkrrvvals rO_putmaskExtensionArray._putmaskHs"(   +CCT rSc`UR5n[U5(aX!)nOUnXCU)'U$)z Analogue to np.where(mask, self, value) Parameters ---------- mask : np.ndarray[bool] value : scalar or listlike Returns ------- same type as self )rKr)rkrrvrrs rO_whereExtensionArray._wherecs6   ,CCu  rSc[R"U5nUR[5nU"XRUR 5S9 UR XPR S9nXcX'g)zq Replace values in locations specified by 'mask' using pad or backfill. See also -------- ExtensionArray.fillna )rrrN)r# get_fill_funcrr rKrPrJ)rkrrrfuncnpvaluesrs rO_fill_mask_inplace!ExtensionArray._fill_mask_inplace{sU$$V,;;v& X5(((D % rSraveragerr na_optionrpctc RUS:wa[e[UR5UUUUUS9$)z See Series.rank.__doc__. rr)rsr*r)rkrrrrrs rO_rankExtensionArray._ranks9 19% %  $ $ &   rScUR/US9n[R"[R"S5U5nUR USS9n[ XP5(aX%R :wa[SUS35eU$)z Create an ExtensionArray with the given shape and dtype. See also -------- ExtensionDtype.empty ExtensionDtype.empty is the 'official' public version of this API. rrTrz5Default 'empty' implementation is invalid for dtype='rm)rPr broadcast_tointprrrJrs)rMrrJobjtakerrs rO_emptyExtensionArray._emptysw  5 1 U3%D1&&&%<<*?%GwaP  rSc[R"UR55n[R"U5n[Rn[ XCXQU5n[ U5R U5$)z Compute the quantiles of self for each quantile in `qs`. Parameters ---------- qs : np.ndarray[float64] interpolation: str Returns ------- same type as self )rrr!rr,rtrP)rkqs interpolationrrr res_valuess rO _quantileExtensionArray._quantilesRzz$))+&jjVV ':=Q Dz((44rSc[XS9$)z Returns the mode(s) of the ExtensionArray. Always returns `ExtensionArray` even if only one value. Parameters ---------- dropna : bool, default True Don't consider counts of NA values. Returns ------- same type as self Sorted, if possible. )r)r))rkrs rO_modeExtensionArray._modes$D((rScX[SU55(a[$[R"XU/UQ70UD6nU[LaU$SU;a[R"XU/UQ70UD6$US:Xa&[R "XU/UQ70UD6nU[LaU$[R "XU/UQ70UD6$)Nc3X# UH n[U[[[45v M" g7frh)rrrr).0rs rO 1ExtensionArray.__array_ufunc__..s% PVuJuy(LA B BPVs(*outreduce)rNotImplementedr"!maybe_dispatch_ufunc_to_dunder_opdispatch_ufunc_with_outdispatch_reduction_ufuncdefault_array_ufunc)rkufuncrinputsrrs rO__array_ufunc__ExtensionArray.__array_ufunc__s  PV   " !<<  "( ,2   'M F?44V&,06  X 77V&,06F^+ ,,T&T6TVTTrSc[XUS9$)a3 Map values using an input mapping or function. Parameters ---------- mapper : function, dict, or Series Mapping correspondence. na_action : {None, 'ignore'}, default None If 'ignore', propagate NA values, without passing them to the mapping correspondence. If 'ignore' is not supported, a ``NotImplementedError`` should be raised. Returns ------- Union[ndarray, Index, ExtensionArray] The output of the mapping function applied to the array. If the function returns a tuple with more than one element a MultiIndex will be returned. ) na_action)r()rkmapperrs rOmapExtensionArray.maps(;;rSc SSKJn SSKJn UR U5n U"XUS9n [ UR U5(asU RS;a@U RU RU R[R "[5S5 UR[[RS9n O[SUR 35eU R"U 4UUUS S .UD6n U RU R ;aU $[ UR U5(a+UR n U R#5nUR%XS 9$[e) a3 Dispatch GroupBy reduction or transformation operation. This is an *experimental* API to allow ExtensionArray authors to implement reductions and transformations. The API is subject to change. Parameters ---------- how : {'any', 'all', 'sum', 'prod', 'min', 'max', 'mean', 'median', 'median', 'var', 'std', 'sem', 'nth', 'last', 'ohlc', 'cumprod', 'cumsum', 'cummin', 'cummax', 'rank'} has_dropped_na : bool min_count : int ngroups : int ids : np.ndarray[np.intp] ids[i] gives the integer label for the group that self[i] belongs to. **kwargs : operation-specific 'any', 'all' -> ['skipna'] 'var', 'std', 'sem' -> ['ddof'] 'cumprod', 'cumsum', 'cummin', 'cummax' -> ['skipna'] 'rank' -> ['ties_method', 'ascending', 'na_option', 'pct'] Returns ------- np.ndarray or ExtensionArray r) StringDtype)WrappedCythonOp)howrhas_dropped_na)rrF)rz,function is not implemented for this dtype: N) min_countngroupscomp_idsrr)pandas.core.arrays.string_rpandas.core.groupby.opsrget_kind_from_howrrJr_get_cython_functionrrr rrrs_cython_op_ndim_compatcast_blocklistrrP)rkrrrridsrrrroprrrJstring_array_clss rO _groupby_opExtensionArray._groupby_op s-H ;;005  O djj+ . .vv^+''&9I5Q}}Vbff}=H%>tzzlK ..       66R&& &  djj+ . .JJE$99; #22:2K K& %rSri)rJ Dtype | NonerKr)rJr6returnr=)rlr<rr)rlr>rr=)rlr;rz Self | AnyrNone)rint)rz Iterator[Any])rlr rzbool | np.bool_)rr rr2)rJznpt.DTypeLike | NonerKrrr r np.ndarray)rr)rr?).)rJz npt.DTypeLikerKrrr)rJrrKrrrF)rJr3rKrrr2)T)rz)np.ndarray | ExtensionArraySupportsAnyAll)rr)rr)rrrr@rrRrr)rrrr) rr8rrrrCrKrrr=) rr7r int | Nonerz#Literal['inside', 'outside'] | NonerKrrr=)NNNT) rvzobject | ArrayLike | NonerzFillnaOptions | NonerrrKrrr=)rr=)first)rzLiteral['first', 'last', False]rnpt.NDArray[np.bool_])rN)rrrr rrF)leftN)rvz$NumpyValueArrayLike | ExtensionArrayrzLiteral['left', 'right']rzNumpySorter | Nonerznpt.NDArray[np.intp] | np.intp)rr rr)rcr2rr)rztuple[np.ndarray, Any])r"rrz!tuple[np.ndarray, ExtensionArray]rh)r,zint | Sequence[int]rzAxisInt | Nonerr=)r0rArrrrrr=)rJrrr2)rrR)F)rTrrzCallable[[Any], str | None])rWrrrF)rrF)C)r^z"Literal['C', 'F', 'A', 'K'] | NonerrF)rbzSequence[Self]rr=)rhrRrrrrF)rhrRrrrkr)rwrRrxrRryrrznpt.NDArray[np.uint64])rz#tuple[Self, npt.NDArray[np.uint64]])rr)rr;rr=)rrrr=)rrrr)rrrr=)rrRrrrrrr) rr4rrRrrRrrrr)rr?rJr)rznpt.NDArray[np.float64]rrRrr=)rrrr=)rznp.ufuncrrR) rrRrrrrrrrznpt.NDArray[np.intp]rr2)Ur __module__ __qualname____firstlineno____doc___typ__pandas_priority__ classmethodrPr\r`rer rmrwr{rrrrr rrpropertyrJrrrrrr!rrrrrrrrrr%r r+rrr'rr%rDrrr'rrKr5rGrCr?rArXr[r_rrrriro__annotations__rsr|rrrrrrrrrrrrrr__static_attributes__rirSrOrFrFnsNd D  >BQV''8>/3%' ,';?''<''>    (B+SZ((( ( '+>> "#"" "  "P ( (  ##   ( ("      E:N(8''!L$! 6 6  6  6  6 p *D *D" #"  "  " "  " P!:> ]] ] 8 ]  ] ]B,0'+  `(`%` `  `  `D"$7>=3= =2:.x>,*0%) :A3:A':A# :A ( :Ax'9R.0+>!%?!?! +?!F( !X() *8456*! ](]( ](  ](  ](~(("P/ ; 7"$T(  4''B'',0&S&S$(&S &SR,0%22$(2;?2n  # # *-# ;?# # J!F,""PH60&&",&4I& &*         0,5()(U4<2J&J& J&  J&  J&"J& J&rSrFc8\rSrSrSS.SSjjrSS.SSjjrSrg) ExtensionArraySupportsAnyAllid Trfc[U5erhrrs rOr ExtensionArraySupportsAnyAll.anye !$''rSc[U5erhrrs rOr ExtensionArraySupportsAnyAll.allh rrSriN)rrrr)rrrrrrrrirSrOrrd s$((%)((rSrc\rSrSrSr\S5r\S Sj5r\S5r\S Sj5r \S5r \S Sj5r S r g ) ExtensionOpsMixinil z A base class for linking the operators to their dunder names. .. note:: You may want to set ``__array_priority__`` if you want your implementation to be called when involved in binary operations with NumPy arrays. c[U5erhrrMrs rO_create_arithmetic_method+ExtensionOpsMixin._create_arithmetic_methodw !#&&rSc0[USUR[R55 [USUR[R 55 [USUR[R 55 [USUR[R55 [USUR[R55 [USUR[R55 [USUR[R55 [USUR[R55 [US UR[R55 [US UR[R55 [US UR[R55 [US UR[R55 [US UR[R 55 [USUR[R"55 [USUR[$55 [USUR[R&55 g)N__add____radd____sub____rsub____mul____rmul____pow____rpow____mod____rmod__ __floordiv__ __rfloordiv__ __truediv__ __rtruediv__ __divmod__ __rdivmod__)setattrroperatoraddr$raddsubrsubmulrmulpowrpowmodrmodfloordiv rfloordivtruedivrtruedivdivmodrdivmodrMs rO_add_arithmetic_ops%ExtensionOpsMixin._add_arithmetic_ops{ sY = =hll KLZ!>!>y~~!NOY = =hll KLZ!>!>y~~!NOY = =hll KLZ!>!>y~~!NOY = =hll KLZ!>!>y~~!NOY = =hll KLZ!>!>y~~!NO^S%B%B8CTCT%UV #"?"? @S@S"T  ]C$A$A(BRBR$ST^S%B%B9CUCU%VW\3#@#@#HI]C$A$A)BSBS$TUrSc[U5erhrrs rO_create_comparison_method+ExtensionOpsMixin._create_comparison_method rrSc[USUR[R55 [USUR[R55 [USUR[R 55 [USUR[R 55 [USUR[R55 [USUR[R55 g)Nrr__lt____gt____le____ge__) rr&reqneltgtleger"s rO_add_comparison_ops%ExtensionOpsMixin._add_comparison_ops sXs<> JKY : :8<< HIZ!;!;INN!KLrSriNr) rrrrrrrr#r&r3r6rErrirSrOrrl s''VV(''KK''MMrSrcP\rSrSrSr\SS Sjj5r\S5r\S5rSr g) ExtensionScalarOpsMixini a A mixin for defining ops on an ExtensionArray. It is assumed that the underlying scalar objects have the operators already defined. Notes ----- If you have defined a subclass MyExtensionArray(ExtensionArray), then use MyExtensionArray(ExtensionArray, ExtensionScalarOpsMixin) to get the arithmetic operators. After the definition of MyExtensionArray, insert the lines MyExtensionArray._add_arithmetic_ops() MyExtensionArray._add_comparison_ops() to link the operators to your class. .. note:: You may want to set ``__array_priority__`` if you want your implementation to be called when involved in binary operations with NumPy arrays. NcP^^^UUU4SjnSTRS3n[XEU5$)a A class method that returns a method that will correspond to an operator for an ExtensionArray subclass, by dispatching to the relevant operator defined on the individual elements of the ExtensionArray. Parameters ---------- op : function An operator that takes arguments op(a, b) coerce_to_dtype : bool, default True boolean indicating whether to attempt to convert the result to the underlying ExtensionArray dtype. If it's not possible to create a new ExtensionArray with the values, an ndarray is returned instead. Returns ------- Callable[[Any, Any], Union[ndarray, ExtensionArray]] A method that can be bound to a class. When used, the method receives the two arguments, one of which is the instance of this class, and should return an ExtensionArray or an ndarray. Returning an ndarray may be necessary when the result of the `op` cannot be stored in the ExtensionArray. The dtype of the ndarray uses NumPy's normal inference rules. Examples -------- Given an ExtensionArray subclass called MyExtensionArray, use __add__ = cls._create_method(operator.add) in the class definition of MyExtensionArray to create the operator for addition, that will be based on the operator implementation of the underlying elements of the ExtensionArray c<>^U4Sjn[U[[[45(a[$TnU"U5n[ X45VVs/sH upVT "XV5PM nnnU U U4SjnT R S;a[ U6upVU"U5U"U54$U"U5$s snnf)Ncx>[U[5(d[U5(aUnU$U/[T5-nU$rh)rrFrr)paramovaluesrks rOconvert_valuesNExtensionScalarOpsMixin._create_method.._binop..convert_values s=e^44 U8K8K#G %gD 1GrSc>T(aG[UTRSS9n[U[T55(d[R "U5nU$[R "UTS9nU$)NF) same_dtyper)rrJrrtrr)rrescoerce_to_dtype result_dtyperks rO_maybe_convertNExtensionScalarOpsMixin._create_method.._binop.._maybe_convert sW"6c4::RWXC%c4:66 jjo **S =C rS>r r!)rrrrrzipr) rkrrNlvaluesrvaluesrr rRrUrSrrTs ` rO_binop6ExtensionScalarOpsMixin._create_method.._binop s %)X|!DEE%%G$U+G+.g*?@*?2a8*?C@ {{33Cy%a(.*;;;!#& &'AsB__)rr )rMrrSrTrZop_names ``` rO_create_method&ExtensionScalarOpsMixin._create_method s+P$ 'Lr{{m2& #66rSc$URU5$rh)r^rs rOr1ExtensionScalarOpsMixin._create_arithmetic_method s!!"%%rSc,URUS[S9$)NF)rSrT)r^rrs rOr&1ExtensionScalarOpsMixin._create_comparison_method s!!"e$!OOrSri)TN)rSr) rrrrrrr^rr&rrirSrOrHrH sH2N7N7`&&PPrSrH)br __future__rrtypingrrrrrr r rZnumpyr pandas._libsr rr pandas.compatr pandas.compat.numpyrr pandas.errorsrpandas.util._decoratorsrrrpandas.util._exceptionsrpandas.util._validatorsrrrpandas.core.dtypes.castrpandas.core.dtypes.commonrrrpandas.core.dtypes.dtypesrpandas.core.dtypes.genericrrrpandas.core.dtypes.missingr! pandas.corer"r#r$pandas.core.algorithmsr%r&r'r(r)r*r+ pandas.core.array_algos.quantiler,pandas.core.missingr-pandas.core.sortingr.r/collections.abcr0r1pandas._typingr2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBpandasrCrDrrFrrrHrirSrOr{s#,.- 5 @ 5 , @3  (/1n1s#&s#&lG(>(<M<M~qP/qPrS