+ Ŝia0t$Rt^RIHt^RIt^RIHtHtHtH t H t H t H t ^RI t ^RIt^RIHtHt^RIHt^RIHt^RIHt^RIHtHt^R IHt^R I H!t!H"t"^R I#H$t$^R I%H&t&H't'H(t(H)t)^R I*H+t+^RI,H-t-H.t.H/t/^RI0H1t1^RI2H3t3H4t4H5t5^RI6H7t7H8t8H9t9H:t:H;t;HH?t?^RI@HAtA^RIBHCtCHDtD]'d=^RIEHFtFHGtGHHtH^RIIHJtJ^RIKHLtLHMtMHNtNHOtOHPtPHQtQHRtRHStSHTtTHUtUHVtVHWtWHXtXHYtYHZtZH[t[^RI\H]t]H^t^/t_R]`R&]!R4!RR44ta!RR]a4tb!R R!4tc]!R4!R"R#]c44tdR#)$z An interface for extending pandas with custom arrays. .. warning:: This is an experimental API and subject to breaking changes without warning. ) annotationsN) TYPE_CHECKINGAnyClassVarLiteralSelfcastoverload)algoslib)set_function_name)functionAbstractMethodError)cache_readonly set_module)find_stack_level)validate_bool_kwargvalidate_insert_loc)astype_is_view) is_integer is_list_like is_scalar pandas_dtype)ExtensionDtype) ABCDataFrameABCIndex ABCSeriesisna) arraylikemissing roperator) duplicatedfactorize_arrayisin map_arraymoderankunique)quantile_with_mask)_fill_limit_area_1d) nargminmaxnargsort)CallableIteratorSequence)NAType) ArrayLike AstypeArgAxisIntDtypeDtypeObj FillnaOptionsInterpolateOptions NumpySorterNumpyValueArrayLikePositionalIndexer ScalarIndexerSequenceIndexerShapeSortKind TakeIndexernpt)IndexSerieszdict[str, str]_extension_array_shared_docszpandas.api.extensionsc r]tRt^rt$RtRtRtRt]RRRR/RR ll4t ]RR/R R ll4t ]R 4t ]R Rl4t RRlt ]RRl4t]RRl4tRRltRRltRRltRRltRRltRR ltR!R"ltRR#R$lltRR]P03R%R&llt]R'R(l4t]R)R*l4t]R+R,l4t]R-R.l4t]R/R0l4t]RR1R2ll4t ]RR3R4ll4t ]RR5R6ll4t RR8R9llt R:R;lt!]R<R=l4t"R>R?lt#R@R7RARBRCRD/RERFllt$RRGRHllt%RRIRJllt&RKRLlt'RMRRNRRR7/RORPllt(RRQRRllt)RSRTlt*RRURVllt+RRWRXllt,RYRZlt-RR[R\llt.R]R^lt/R_R`lt0RaRblt1RRcRdllt2Re]3Rf&RRgRhllt4RRiRjllt5RkRRlR/RmRnllt6RoRplt7]RqRrl4t8]RRsRtll4t8RRuRvllt8RwRxlt9RyRzlt:R{R|lt;RR}R~lltRRRllt?]RRl4t@]ARRl4tBRR7/RRlltCRR7RR/RRlltDR]ER&RRltFRRltGRRltHRRltIRRltJRRltKRRltLRRltMR^RRRRR@R7RR/RRlltN]RRl4tORRltPRRRlltQRRltRRRRlltSRRltTRtUR#)ExtensionArrayaT 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 item 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 See Also -------- api.extensions.ExtensionDtype : A custom data type, to be paired with an ExtensionArray. api.extensions.ExtensionArray.dtype : An instance of ExtensionDtype. 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 extensioniFdtypeNcopyc$V^8dQhRRRRRR/#)rH Dtype | NonerIboolreturnr)formats"`/Users/mibo/.openclaw/workspace/.venv-ak/lib/python3.14/site-packages/pandas/core/arrays/base.py __annotate__ExtensionArray.__annotate__s$#'#' ,#';?#' #'c \V4h)a* 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 See Also -------- api.extensions.ExtensionArray._from_sequence_of_strings : Construct a new ExtensionArray from a sequence of strings. api.extensions.ExtensionArray._hash_pandas_object : Hook for hash_pandas_object. Examples -------- >>> pd.arrays.IntegerArray._from_sequence([4, 5]) [4, 5] Length: 2, dtype: Int64 r)clsscalarsrHrIs&&$$rQ_from_sequenceExtensionArray._from_sequencesH"#&&rTc$V^8dQhRRRRRR/#)rKrHrrIrMrNrrO)rPs"rQrRrSBs$%'%' .%'6:%' %'rTc \V4h)a? Construct a new ExtensionArray from a sequence of strings. Parameters ---------- strings : Sequence Each element will be an instance of the scalar type for this array, ``cls.dtype.type``. dtype : ExtensionDtype Construct for this particular dtype. This should be a Dtype compatible with the ExtensionArray. copy : bool, default False If True, copy the underlying data. Returns ------- ExtensionArray See Also -------- api.extensions.ExtensionArray._from_sequence : Construct a new ExtensionArray from a sequence of scalars. api.extensions.ExtensionArray._from_factorized : Reconstruct an ExtensionArray after factorization. Examples -------- >>> pd.arrays.IntegerArray._from_sequence_of_strings( ... ["1", "2", "3"], dtype=pd.Int64Dtype() ... ) [1, 2, 3] Length: 3, dtype: Int64 r)rVstringsrHrIs&&$$rQ_from_sequence_of_strings(ExtensionArray._from_sequence_of_stringsAsL"#&&rTc \V4h)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)rVvaluesoriginals&&&rQ_from_factorizedExtensionArray._from_factorizedis8"#&&rTc V^8dQhRRRR/#)rKrHr6rNrrO)rPs"rQrRrSsh4rTc VPWRR7# \\3dh\d#\P !R\ 4R7hi;i)au 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 in ExtensionArray._cast_pointwise_result. FrHrIzm_from_scalars should only raise ValueError or TypeError. Consider overriding _from_scalars where appropriate.) stacklevel)rX ValueError TypeError Exceptionwarningswarnr)rVrWrHs&&$rQ _from_scalarsExtensionArray._from_scalarssY( %%g%G GI&    MMG+-    sA#AcV^8dQhRR/#)rKrNr2rO)rPs"rQrRrSsOO OrTc \V4PWPR7# \\3d8\ P !T\R7p\P!TRR7u#i;i)a Construct an ExtensionArray after a pointwise operation. Cast the result of a pointwise operation (e.g. Series.map) to an array. This is not required to return an ExtensionArray of the same type as self or of the same dtype. It can also return another ExtensionArray of the same "family" if you implement multiple ExtensionArrays/Dtypes that are interoperable (e.g. if you have float array with units, this method can return an int array with units). If converting to your own ExtensionArray is not possible, this method falls back to returning an array with the default type inference. If you only need to cast to `self.dtype`, it is recommended to override `_from_scalars` instead of this method. Parameters ---------- values : sequence Returns ------- ExtensionArray or ndarray rHT)convert_non_numeric) typermrHrhrinpasarrayobjectr maybe_convert_objectsselfr`s&&rQ_cast_pointwise_result%ExtensionArray._cast_pointwise_results]0 O:++F**+E EI& OZZf5F,,VN N Os$'AA/.A/c V^8dQhRRRR/#)rKitemr<rNrrO)rPs"rQrRrSs:: :#:rTc R#NrOryr}s&&rQ __getitem__ExtensionArray.__getitem__s7:rTc V^8dQhRRRR/#)rKr}r=rNrrO)rPs"rQrRrSs===D=rTc R#rrOrs&&rQrrs:=rTc V^8dQhRRRR/#)rKr}r;rNz Self | AnyrO)rPs"rQrRrSs(( 1(j(rTc \V4h)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. rrs&&rQrrs>"$''rTcV^8dQhRR/#rKrNNonerO)rPs"rQrRrSs4S4S4SrTc jVP'd \R4h\\V4 R24h)a$ 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 Raises ------ ValueError If the array is readonly and modification is attempted. zCannot modify read-only arrayz does not implement __setitem__.) _readonlyrhNotImplementedErrorrs)rykeyvalues&&&rQ __setitem__ExtensionArray.__setitem__s1b >>><= =!T$ZL0P"QRRrTcV^8dQhRR/#rKrNintrO)rPs"rQrRrS%s(((rTc \V4h)z4 Length of this array Returns ------- length : int rrys&rQ__len__ExtensionArray.__len__%s"$''rTcV^8dQhRR/#)rKrNz Iterator[Any]rO)rPs"rQrRrS/s-rTc# X"\\V44F pW,xK R#5i)z% Iterate over elements of the array. N)rangelen)ryis& rQ__iter__ExtensionArray.__iter__/s!s4y!A'M"(*c V^8dQhRRRR/#)rKr}rvrNzbool | np.bool_rO)rPs"rQrRrS9s(((O(rTc *\V4'dr\V4'daVP'gR#WPPJg&\ WPP 4'd VP#R#W8HP4#)z Return for `item in self`. F) rr _can_hold_narHna_value isinstancers_hasnaanyrs&&rQ __contains__ExtensionArray.__contains__9sf T??tDzz$$$,,, 40Q0Q{{"L%%' 'rTc V^8dQhRRRR/#rKotherrvrNr2rO)rPs"rQrRrSMs ( (F (y (rTc \V4h)z5 Return for `self == other` (element-wise equality). rryrs&&rQ__eq__ExtensionArray.__eq__Ms"$''rTc V^8dQhRRRR/#rrO)rPs"rQrRrSZs  F y rTc W8H(#)z8 Return for `self != other` (element-wise in-equality). rOrs&&rQ__ne__ExtensionArray.__ne__Zs rTcV^8dQhRR/#)rKindex int | NonerO)rPs"rQrRrSas00*0rTc Vf%\V4^8wd \R4hV^,#\V4'g\R\ V4 24hW,#)a Return the array element at the specified position as a Python scalar. Parameters ---------- index : int, optional Position of the element. If not provided, the array must contain exactly one element. Returns ------- scalar The element at the specified position. Raises ------ ValueError If no index is provided and the array does not have exactly one element. IndexError If the specified position is out of bounds. See Also -------- numpy.ndarray.item : Return the item of an array as a scalar. Examples -------- >>> arr = pd.array([1], dtype="Int64") >>> arr.item() np.int64(1) >>> arr = pd.array([1, 2, 3], dtype="Int64") >>> arr.item(0) np.int64(1) >>> arr.item(2) np.int64(3) z6can only convert an array of size 1 to a Python scalarzindex must be an integer, got )rrhrrirs)ryrs&&rQr}ExtensionArray.itemasZN =4yA~ L7Ne$$"@e NOO; rTc(V^8dQhRRRRRRRR/#) rKrHznpt.DTypeLike | NonerIrMrrvrN np.ndarrayrO)rPs"rQrRrSs2((#(( (  (rTc \P!WR7pV'gV\PJdVP 4pMYVP 'dH\ VPVP4'd"VP4pRVPn V\PJdW4VP4&V#)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 rqF) rtrur no_defaultrIrrrHviewflags writeabler)ryrHrIrresults&&&& rQto_numpyExtensionArray.to_numpys~:D. 83>>1[[]F ^^^tzz6<< H H[[]F%*FLL " 3>> )"*499;  rTcV^8dQhRR/#)rKrNrrO)rPs"rQrRrSs((~(rTc \V4h)a An instance of ExtensionDtype. See Also -------- api.extensions.ExtensionDtype : Base class for extension dtypes. api.extensions.ExtensionArray : Base class for extension array types. api.extensions.ExtensionArray.dtype : The dtype of an ExtensionArray. Series.dtype : The dtype of a Series. DataFrame.dtype : The dtype of a DataFrame. Examples -------- >>> pd.array([1, 2, 3]).dtype Int64Dtype() rrs&rQrHExtensionArray.dtypes$"$''rTcV^8dQhRR/#)rKrNr>rO)rPs"rQrRrSsurTc \V43#)aq Return a tuple of the array dimensions. See Also -------- numpy.ndarray.shape : Similar attribute which returns the shape of an array. DataFrame.shape : Return a tuple representing the dimensionality of the DataFrame. Series.shape : Return a tuple representing the dimensionality of the Series. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.shape (3,) )rrs&rQshapeExtensionArray.shapes$D |rTcV^8dQhRR/#rrO)rPs"rQrRrSs##c#rTc B\P!VP4#)z& The number of elements in the array. )rtprodrrs&rQsizeExtensionArray.sizeswwtzz""rTcV^8dQhRR/#rrO)rPs"rQrRrSscrTc ^#)a Extension Arrays are only allowed to be 1-dimensional. See Also -------- ExtensionArray.shape: Return a tuple of the array dimensions. ExtensionArray.size: The number of elements in the array. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.ndim 1 rOrs&rQndimExtensionArray.ndims rTcV^8dQhRR/#rrO)rPs"rQrRrSs(((rTc \V4h)z The number of bytes needed to store this object in memory. See Also -------- ExtensionArray.shape: Return a tuple of the array dimensions. ExtensionArray.size: The number of elements in the array. Examples -------- >>> pd.array([1, 2, 3]).nbytes 27 rrs&rQnbytesExtensionArray.nbytess""$''rTc$V^8dQhRRRRRR/#)rKrHz npt.DTypeLikerIrMrNrrO)rPs"rQrRrSsOOMOO OrTc R#rrOryrHrIs&&&rQastypeExtensionArray.astypesLOrTc$V^8dQhRRRRRR/#)rKrHrrIrMrNrFrO)rPs"rQrRrSsTTNT$TTrTc R#rrOrs&&&rQrrsQTrTc$V^8dQhRRRRRR/#rKrHr3rIrMrNr2rO)rPs"rQrRrS"sJJIJTJIJrTc R#rrOrs&&&rQrr!sGJrTTc$V^8dQhRRRRRR/#rrO)rPs"rQrRrS$s&O:O:IO:TO:YO:rTc $\V4pWP8XdV'gV#VP4#\V\4'd$VP 4pVP WVR7#\P!VR4'd^RI H pVP WVR7#\P!VR4'd^RI H pVP WVR7#V'g\P!WR7#\P!WVR7#)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. See Also -------- Series.astype : Cast a Series to a different dtype. DataFrame.astype : Cast a DataFrame to a different dtype. api.extensions.ExtensionArray : Base class for ExtensionArray objects. core.arrays.DatetimeArray._from_sequence : Create a DatetimeArray from a sequence. core.arrays.TimedeltaArray._from_sequence : Create a TimedeltaArray from a sequence. 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') rfM) DatetimeArraym)TimedeltaArrayrq)rrHrIrrconstruct_array_typerXr is_np_dtypepandas.core.arraysrrrtruarray)ryrHrIrVrrs&&& rQrr$snU# JJ  yy{" e^ , ,,,.C%%dd%C C __UC ( ( 8 ///M M __UC ( ( 9!000N N::d0 088DD9 9rTcV^8dQhRR/#)rKrNz)np.ndarray | ExtensionArraySupportsAnyAllrO)rPs"rQrRrSus ( (? (rTc \V4h)af A 1-D array indicating if each value is missing. Returns ------- numpy.ndarray or pandas.api.extensions.ExtensionArray In most cases, this should return a NumPy ndarray. For exceptional cases like ``SparseArray``, where returning an ndarray would be expensive, an ExtensionArray may be returned. See Also -------- ExtensionArray.dropna: Return ExtensionArray without NA values. ExtensionArray.fillna: Fill NA/NaN values using the specified method. Notes ----- If returning an ExtensionArray, then * ``na_values._is_boolean`` should be True * ``na_values`` should implement :func:`ExtensionArray._reduce` * ``na_values`` should implement :func:`ExtensionArray._accumulate` * ``na_values.any`` and ``na_values.all`` should be implemented Examples -------- >>> arr = pd.array([1, 2, np.nan, np.nan]) >>> arr.isna() array([False, False, True, True]) rrs&rQrExtensionArray.isnaus@"$''rTcV^8dQhRR/#rKrNrMrO)rPs"rQrRrSs'''rTc P\VP4P44#)zh Equivalent to `self.isna().any()`. Some ExtensionArray subclasses may be able to optimize this check. )rMrrrs&rQrExtensionArray._hasnasDIIKOO%&&rTcV^8dQhRR/#rKrNrrO)rPs"rQrRrSs!!Z!rTc .\P!V4#)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]) )rtrrs&rQ_values_for_argsort"ExtensionArray._values_for_argsortsBxx~rT ascendingkind quicksort na_positionlastc(V^8dQhRRRRRRRR/#) rKrrMrr?rstrrNrrO)rPs"rQrRrSs46 6 6  6  6  6 rTc \P!VRV4pVP4p\VVVV\P !VP 44R7#)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. **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]) )rrrmaskrO)nvvalidate_argsort_with_ascendingrr-rtrur)ryrrrkwargsr`s&$$$, rQargsortExtensionArray.argsortsRZ66y"fM ))+ #DIIK(   rTc V^8dQhRRRR/#rKskipnarMrNrrO)rPs"rQrRrS * *T *S *rTc |\VR4V'gVP'd \R4h\VR4#)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() np.int64(1) r)Encountered an NA value with skipna=Falseargminrrrhr,ryrs&&rQr ExtensionArray.argmin2: FH-$+++HI I$))rTc V^8dQhRRRR/#rrO)rPs"rQrRrSrrTc |\VR4V'gVP'd \R4h\VR4#)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() np.int64(3) rr argmaxr r s&&rQrExtensionArray.argmaxrrTc ,V^8dQhRRRRRRRRR R /#) rKmethodr8axisrrrBrIrMrNrrO)rPs"rQrRrS@sEl l #l  l  l l  l rTc D\\V4P R24h)ar Fill NaN values using an interpolation method. Parameters ---------- method : str, default 'linear' Interpolation technique to use. One of: * 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. * 'time': Works on daily and higher resolution data to interpolate given length of interval. * 'index', 'values': use the actual numerical values of the index. * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'polynomial': Passed to scipy.interpolate.interp1d, whereas 'spline' is passed to scipy.interpolate.UnivariateSpline. These methods use the numerical values of the index. Both 'polynomial' and 'spline' require that you also specify an order (int), e.g. arr.interpolate(method='polynomial', order=5). * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima', 'cubicspline': Wrappers around the SciPy interpolation methods of similar names. See Notes. * 'from_derivatives': Refers to scipy.interpolate.BPoly.from_derivatives. axis : int Axis to interpolate along. For 1-dimensional data, use 0. index : Index Index to use for interpolation. limit : int or None Maximum number of consecutive NaNs to fill. Must be greater than 0. limit_direction : {'forward', 'backward', 'both'} Consecutive NaNs will be filled in this direction. limit_area : {'inside', 'outside'} or None If limit is specified, consecutive NaNs will be filled with this restriction. * None: No fill restriction. * 'inside': Only fill NaNs surrounded by valid values (interpolate). * 'outside': Only fill NaNs outside valid values (extrapolate). copy : bool If True, a copy of the object is returned with interpolated values. **kwargs : optional Keyword arguments to pass on to the interpolating function. Returns ------- ExtensionArray An ExtensionArray with interpolated values. See Also -------- Series.interpolate : Interpolate values in a Series. DataFrame.interpolate : Interpolate values in a DataFrame. Notes ----- - All parameters must be specified as keyword arguments. - The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. Examples -------- Interpolating values in a NumPy array: >>> arr = pd.arrays.NumpyExtensionArray(np.array([0, 1, np.nan, 3])) >>> arr.interpolate( ... method="linear", ... limit=3, ... limit_direction="forward", ... index=pd.Index(range(len(arr))), ... fill_value=1, ... copy=False, ... axis=0, ... limit_area="inside", ... ) [0.0, 1.0, 2.0, 3.0] Length: 4, dtype: float64 Interpolating values in a FloatingArray: >>> arr = pd.array([1.0, pd.NA, 3.0, 4.0, pd.NA, 6.0], dtype="Float64") >>> arr.interpolate( ... method="linear", ... axis=0, ... index=pd.Index(range(len(arr))), ... limit=None, ... limit_direction="both", ... limit_area=None, ... copy=True, ... ) [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] Length: 6, dtype: Float64 z does not implement interpolate)rrs__name__) ryrrrlimitlimit_direction limit_arearIrs &$$$$$$$,rQ interpolateExtensionArray.interpolate@s(T"Dz""##B C  rTrrc ,V^8dQhRRRRRRRRR R /#) rKrr7rrrz#Literal['inside', 'outside'] | NonerIrMrNrrO)rPs"rQrRrSsETTT T 8 T  T TrTc VP4pVP4'd\P!V4p\P !V4pVe"VP 4'g \Ws4VR8Xd+\P!WrR7pVPVRR7#\P!VRRR1,VR7RRR1,pVRRR1,PVRR7#V'gV#VP4p V #)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. limit_area : {'inside', 'outside'} or None, default None Specifies which area to limit filling. - 'inside': Limit the filling to the area within the gaps. - 'outside': Limit the filling to the area outside the gaps. If `None`, no limitation is applied. 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 The filled array with the same type as the original. See Also -------- Series.ffill : Forward fill missing values. Series.bfill : Backward fill missing values. DataFrame.ffill : Forward fill missing values in DataFrame. DataFrame.bfill : Backward fill missing values in DataFrame. api.types.isna : Check for missing values. api.types.isnull : Check for missing values. 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 Npad)rT allow_fill) rrr!clean_fill_methodrtruallr+libalgosget_fill_indexertakerI) ryrrrrIrmethnpmaskindexer new_valuess &$$$$ rQ_pad_or_backfillExtensionArray._pad_or_backfills~yy{ 88::,,V4DZZ%F%fjjll#F7u}"33FHyyTy::#33F4R4LNtQStTDbDzw4@@ JrTc(V^8dQhRRRRRRRR/#) rKrzobject | ArrayLikerrrIrMrNrrO)rPs"rQrRrSs8HH!HH H  HrTc VP4pVeNV\V48d>VP4V8pVP4'dVP 4pRWE&\ P !VV\V44pVP4'd)V'g VR,pMVP 4pWV&V#V'g VR,pV#VP 4pV#)aH 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'. limit : int, default None The maximum number of entries where NA values 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. Returns ------- ExtensionArray With NA/NaN filled. See Also -------- api.extensions.ExtensionArray.dropna : Return ExtensionArray without NA values. api.extensions.ExtensionArray.isna : A 1-D array indicating if each value is missing. 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 FNNN)rrcumsumrrIr!check_value_size)ryrrrIrmodifyr,s&&&& rQfillnaExtensionArray.fillnasVyy{  T!2[[]U*Fzz||yy{$ ((   I  88::!!W !YY[ $t   aJJrTcV^8dQhRR/#rKrNrrO)rPs"rQrRrSNs"""rTc 0WP4(,#)a Return ExtensionArray without NA values. Returns ------- Self An ExtensionArray of the same type as the original but with all NA values removed. See Also -------- Series.dropna : Remove missing values from a Series. DataFrame.dropna : Remove missing values from a DataFrame. api.extensions.ExtensionArray.isna : Check for missing values in an ExtensionArray. Examples -------- >>> pd.array([1, 2, np.nan]).dropna() [1, 2] Length: 2, dtype: Int64 rrs&rQdropnaExtensionArray.dropnaNs2YY[L!!rTc V^8dQhRRRR/#)rKkeepzLiteral['first', 'last', False]rNnpt.NDArray[np.bool_]rO)rPs"rQrRrSis = =3 =  =rTc zVP4P\PRR7p\ WVR7#)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] With true in indices where elements are duplicated and false otherwise. See Also -------- DataFrame.duplicated : Return boolean Series denoting duplicate rows. Series.duplicated : Indicate duplicate Series values. api.extensions.ExtensionArray.unique : Compute the ExtensionArray of unique values. Examples -------- >>> pd.array([1, 1, 2, 3, 3], dtype="Int64").duplicated() array([False, True, False, False, True]) FrI)r`r=r)rrrtbool_r#)ryr=rs&& rQr#ExtensionArray.duplicatedis0>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 rqN) rrIrrHrrXminabs_concat_same_type)ryrDrEemptyabs&&& rQshiftExtensionArray.shiftsd4yyGqL99;    ,,J## L3s7|SY7 7tzz$  Q;AYwhAS\^$AA%%qf--rTcV^8dQhRR/#r8rO)rPs"rQrRrSs>>>rTc v\VP\44pVPWPR7#)a Compute the ExtensionArray of unique values. Returns ------- pandas.api.extensions.ExtensionArray With unique values from the input array. See Also -------- Index.unique: Return unique values in the index. Series.unique: Return unique values of Series object. unique: Return unique values based on a hash table. Examples -------- >>> arr = pd.array([1, 2, 3, 1, 2, 3]) >>> arr.unique() [1, 2, 3] Length: 3, dtype: Int64 rq)r)rrvrXrH)ryuniquess& rQr)ExtensionArray.uniques/.V,-""7**"==rTc(V^8dQhRRRRRRRR/#) rKrz$NumpyValueArrayLike | ExtensionArraysidezLiteral['left', 'right']sorterzNumpySorter | NonerNznpt.NDArray[np.intp] | np.intprO)rPs"rQrRrSs8:A:A3:A':A# :A ( :ArTc VP\4p\V\4'dVP\4pVP WVR7#)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]) )rTrU)rrvrrF searchsorted)ryrrTrUarrs&&&& rQrWExtensionArray.searchsortedsDnkk&! e^ , ,LL(E@@rTc V^8dQhRRRR/#)rKrrvrNrMrO)rPs"rQrRrS$s2929F29t29rTc \V4\V48wdR#\\V4pVPVP8wdR#\ V4\ V48wdR#W8Hp\ V\4'dVP R4pVP4VP4,p\W#,P44#)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. See Also -------- numpy.array_equal : Equivalent method for numpy array. Series.equals : Equivalent method for Series. DataFrame.equals : Equivalent method for DataFrame. Examples -------- >>> arr1 = pd.array([1, 2, np.nan]) >>> arr2 = pd.array([1, 2, np.nan]) >>> arr1.equals(arr2) True >>> arr1 = pd.array([1, 3, np.nan]) >>> arr2 = pd.array([1, 2, np.nan]) >>> arr1.equals(arr2) False F) rsrrFrHrrr5rrMr%)ryr equal_valuesequal_nas&& rQequalsExtensionArray.equals$sH :e $^U+ :: $ Y#e* $=L,77+2259 yy{UZZ\1H05578 8rTc V^8dQhRRRR/#)rKr`r2rNr>rO)rPs"rQrRrSXs..9.)>.rTc B\\P!V4V4#)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 Values to compare every element in the array against. Returns ------- np.ndarray[bool] With true at indices where value is in `values`. See Also -------- DataFrame.isin: Whether each element in the DataFrame is contained in values. Index.isin: Return a boolean array where the index values are in values. Series.isin: Whether elements in Series are contained in values. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.isin([1]) [True, False, False] Length: 3, dtype: boolean )r%rtrurxs&&rQr%ExtensionArray.isinXs<BJJt$f--rTcV^8dQhRR/#)rKrNztuple[np.ndarray, Any]rO)rPs"rQrRrSxs++'=+rTc LVP\4\P3#)ar 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. See Also -------- util.hash_pandas_object : Hash the pandas object. 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) )rrvrtnanrs&rQ_values_for_factorize$ExtensionArray._values_for_factorizexs>{{6"BFF**rTc V^8dQhRRRR/#)rKuse_na_sentinelrMrNz!tuple[np.ndarray, ExtensionArray]rO)rPs"rQrRrSs?!?!?! +?!rTc lVP4wr#\W!VR7wrEVPWP4pWF3#)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. 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]') )rir)rfr$rb)ryrirXrcodesrQ uniques_eas&& rQ factorizeExtensionArray.factorizesAp224 ( 8 **79   rTa2 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, str): ['a', 'b', 'c'] >>> cat.repeat(2) ['a', 'a', 'b', 'b', 'c', 'c'] Categories (3, str): ['a', 'b', 'c'] >>> cat.repeat([1, 2, 3]) ['a', 'b', 'b', 'c', 'c', 'c'] Categories (3, str): ['a', 'b', 'c'] repeatc$V^8dQhRRRRRR/#)rKrepeatszint | Sequence[int]rzAxisInt | NonerNrrO)rPs"rQrRrSs#,,1,,SW,rTc \P!RRV/4\P!\ V44P V4pVP V4#)a9 Repeat elements of an ExtensionArray. Returns a new ExtensionArray where each element of the current ExtensionArray 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 ExtensionArray. axis : None Must be ``None``. Has no effect but is accepted for compatibility with numpy. Returns ------- ExtensionArray Newly created ExtensionArray 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, str): ['a', 'b', 'c'] >>> cat.repeat(2) ['a', 'a', 'b', 'b', 'c', 'c'] Categories (3, str): ['a', 'b', 'c'] >>> cat.repeat([1, 2, 3]) ['a', 'b', 'b', 'c', 'c', 'c'] Categories (3, str): ['a', 'b', 'c'] rrO)rvalidate_repeatrtarangerror()ryrqrinds&&& rQroExtensionArray.repeatsDT 2~.iiD "))'2yy~rTc V^8dQhRRRR/#)rKr:rMrNrCrO)rPs"rQrRrS2s%%4%6%rTc ^RIHpV!VPRR7RVR7pVPP VP 4VnV#)a Return a Series containing counts of unique values. This method returns a Series with unique values as the index and their counts as the values. Parameters ---------- dropna : bool, default True Don't include counts of NA values. Returns ------- Series A Series with unique values as index and counts as values. See Also -------- Series.value_counts : Equivalent method on Series. DataFrame.value_counts : Equivalent method on DataFrame. Index.value_counts : Equivalent method on Index. Examples -------- >>> from pandas.core.arrays import IntegerArray >>> arr = IntegerArray._from_sequence([3, 3, 3, 1, 2, 2]) >>> arr.value_counts() 3 3 1 1 2 2 Name: count, dtype: Int64 )value_counts_internalFr@)sortr:)pandas.core.algorithmsryrrrrH)ryr: value_countsrs&& rQr|ExtensionArray.value_counts2sAB Qdmmm7eFS||**4::6  rTr"rEc(V^8dQhRRRRRRRR/#) rKindicesr@r"rMrErrNrrO)rPs"rQrRrS]s8_(_(_( _(  _(  _(rTc \V4h)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 An array formed with selected `indices`. 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.api.extensions 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)ryrr"rEs&&$$rQr(ExtensionArray.take]s~"$''rTcV^8dQhRR/#r8rO)rPs"rQrRrSs((d(rTc \V4h)aZ Return a copy of the array. This method creates a copy of the `ExtensionArray` where modifying the data in the copy will not affect the original array. This is useful when you want to manipulate data without altering the original dataset. Returns ------- ExtensionArray A new `ExtensionArray` object that is a copy of the current instance. See Also -------- DataFrame.copy : Return a copy of the DataFrame. Series.copy : Return a copy of the Series. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr2 = arr.copy() >>> arr[0] = 2 >>> arr2 [1, 2, 3] Length: 3, dtype: Int64 rrs&rQrIExtensionArray.copys8"$''rTcV^8dQhRR/#r8rO)rPs"rQrRrSs drTc R#rrOrs&rQrExtensionArray.viewsrTc V^8dQhRRRR/#rKrHrLrNr2rO)rPs"rQrRrSs??,??rTc R#rrOryrHs&&rQrrs>> arr = pd.array([1, 2, 3]) >>> arr2 = arr.view() >>> arr[0] = 2 >>> arr2 [2, 2, 3] Length: 3, dtype: Int64 r1)rrs&&rQrrsL  %e, ,AwrTcV^8dQhRR/#rKrNrrO)rPs"rQrRrSs//#/rTc VP^8dVP4#^RIHpV!WP 4RR7P R4pR\ V4P R2pVP4pV V RV 2#)format_object_summaryFindent_for_name,   ) r_repr_2dpandas.io.formats.printingr _formatterrstriprsr_get_repr_footer)ryrdata class_namefooters& rQ__repr__ExtensionArray.__repr__s~ 99q===? "D % //#U &. d,,-S1 &&(dV2fX..rTcV^8dQhRR/#rrO)rPs"rQrRrS s;;#;rTc VP^8dRVP RVP 2#R\V4 RVP 2#)rzShape: z , dtype: zLength: )rrrHrrs&rQrExtensionArray._get_repr_footer sC 99q=TZZL $**> >#d)Idjj\::rTcV^8dQhRR/#rrO)rPs"rQrRrS&s77#7rTc  ^RIHpVUu.uF*pV!W P4RR7PR4NK, ppRP V4pR\ V4P R2pVP4pV RV R V 2#uupi) rFrrz, r>z [ z ] )rrrrjoinrsrr)ryrxlinesrrrs& rQrExtensionArray._repr_2d&sD   "!__%6 N U U   zz% d,,-Q/ &&(U4&fX66 s0Bc V^8dQhRRRR/#)rKboxedrMrNzCallable[[Any], str | None]rO)rPs"rQrRrS7s///1L/rTc *V'd\#\#)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``. See Also -------- api.extensions.ExtensionArray._concat_same_type : Concatenate multiple array of this dtype. api.extensions.ExtensionArray._explode : Transform each element of list-like to a row. api.extensions.ExtensionArray._from_factorized : Reconstruct an ExtensionArray after factorization. api.extensions.ExtensionArray._from_sequence : Construct a new ExtensionArray from a sequence of scalars. Examples -------- >>> class MyExtensionArray(pd.arrays.NumpyExtensionArray): ... def _formatter(self, boxed=False): ... return lambda x: "*" + str(x) + "*" >>> MyExtensionArray(np.array([1, 2, 3, 4])) [*1*, *2*, *3*, *4*] Length: 4, dtype: int64 )rrepr)ryrs&&rQrExtensionArray._formatter7sZ J rTc V^8dQhRRRR/#)rKaxesrrNrrO)rPs"rQrRrSlsstrTc VR,#)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 r1rO)ryrs&*rQ transposeExtensionArray.transposels $AwrTcV^8dQhRR/#r8rO)rPs"rQrRrSs  4 rTc "VP4#r)rrs&rQTExtensionArray.Ts~~rTc V^8dQhRRRR/#)rKorderz"Literal['C', 'F', 'A', 'K'] | NonerNrrO)rPs"rQrRrSs=rTc V#)a Return a flattened view on this array. Parameters ---------- order : {None, 'C', 'F', 'A', 'K'}, default 'C' Returns ------- ExtensionArray A flattened view on the array. See Also -------- ExtensionArray.tolist: Return a list of the values. Notes ----- - Because ExtensionArrays are 1D-only, this is a no-op. - The "order" argument is ignored, is for compatibility with NumPy. Examples -------- >>> pd.array([1, 2, 3]).ravel() [1, 2, 3] Length: 3, dtype: Int64 rO)ryrs&&rQravelExtensionArray.ravels : rTc V^8dQhRRRR/#)rK to_concatzSequence[Self]rNrrO)rPs"rQrRrSs$'$'.$'T$'rTc \V4h)a Concatenate multiple array of this dtype. Parameters ---------- to_concat : sequence of this type An array of the same dtype to concatenate. Returns ------- ExtensionArray See Also -------- api.extensions.ExtensionArray._explode : Transform each element of list-like to a row. api.extensions.ExtensionArray._formatter : Formatting function for scalar values. api.extensions.ExtensionArray._from_factorized : Reconstruct an ExtensionArray after factorization. Examples -------- >>> arr1 = pd.array([1, 2, 3]) >>> arr2 = pd.array([4, 5, 6]) >>> pd.arrays.IntegerArray._concat_same_type([arr1, arr2]) [1, 2, 3, 4, 5, 6] Length: 6, dtype: Int64 r)rVrs&&rQrI ExtensionArray._concat_same_typesJ"#&&rTcV^8dQhRR/#rrO)rPs"rQrRrSs''d'rTc .VPP#r)rHrrs&rQrExtensionArray._can_hold_naszz&&&rTrc$V^8dQhRRRRRR/#)rKnamerrrMrNrFrO)rPs"rQrRrSs)/S/S/S$(/S /SrTc 8\RV RVP 24h)a Return an ExtensionArray performing an accumulation operation. The underlying data type might change. Parameters ---------- name : str Name of the function, supported values are: - cummin - cummax - cumsum - cumprod skipna : bool, default True If True, skip NA values. **kwargs Additional keyword arguments passed to the accumulation function. Currently, there is no supported kwarg. Returns ------- array An array performing the accumulation operation. Raises ------ NotImplementedError : subclass does not define accumulations See Also -------- api.extensions.ExtensionArray._concat_same_type : Concatenate multiple array of this dtype. api.extensions.ExtensionArray.view : Return a view on the array. api.extensions.ExtensionArray._explode : Transform each element of list-like to a row. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr._accumulate(name="cumsum") [1, 3, 6] Length: 3, dtype: Int64 zcannot perform z with type )rrH)ryrrrs&&$,rQ _accumulateExtensionArray._accumulates!^"OD6TZZL"QRRrTkeepdimsc$V^8dQhRRRRRR/#)rKrrrrMrrO)rPs"rQrRrS s)JJJ$(J;?JrTc *\WR4pVf3\R\V4P RVP RV R24hV!RRV/VBpV'd?VR9d!VP V.VPR7pV#\ P!V.4pV#) az 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. **kwargs Additional keyword arguments passed to the reduction function. Currently, `ddof` is the only supported kwarg. Returns ------- scalar or ndarray: The result of the reduction operation. The type of the result depends on `keepdims`: - If `keepdims` is `False`, a scalar value is returned. - If `keepdims` is `True`, the result is wrapped in a numpy array with a single element. Raises ------ TypeError : subclass does not define operations See Also -------- Series.min : Return the minimum value. Series.max : Return the maximum value. Series.sum : Return the sum of values. Series.mean : Return the mean of values. Series.median : Return the median of values. Series.std : Return the standard deviation. Series.var : Return the variance. Series.prod : Return the product of values. Series.sem : Return the standard error of the mean. Series.kurt : Return the kurtosis. Series.skew : Return the skewness. Examples -------- >>> pd.array([1, 2, 3])._reduce("min") np.int64(1) >>> pd.array([1, 2, 3])._reduce("max") np.int64(3) >>> pd.array([1, 2, 3])._reduce("sum") np.int64(6) >>> pd.array([1, 2, 3])._reduce("mean") np.float64(2.0) >>> pd.array([1, 2, 3])._reduce("median") np.float64(2.0) N'z ' with dtype z does not support operation 'rrqrO)rGmax)getattrrirsrrHrXrtr)ryrrrrr)rs&&$$, rQ_reduceExtensionArray._reduce szt4( <DJ''( djj\B//3fA7 .V.v. ~%,,fXTZZ,H 6(+ rTzClassVar[None]__hash__cV^8dQhRR/#rrO)rPs"rQrRrSY s   *  rTc .\P!V4#)a Specify how to render our entries in to_json. Notes ----- The dtype on the returned ndarray is not restricted, but for non-native types that are not specifically handled in objToJSON.c, to_json is liable to raise. In these cases, it may be safer to return an ndarray of strings. )rtrurs&rQ_values_for_jsonExtensionArray._values_for_jsonY szz$rTc(V^8dQhRRRRRRRR/#)rKencodingrhash_key categorizerMrNznpt.NDArray[np.uint64]rO)rPs"rQrRrSf s,) ) ) *-) ;?) ) rTc F^RIHpVP4wrVV!WQW#R7#)a[ Hook for hash_pandas_object. Default is to use the values returned by _values_for_factorize. Parameters ---------- encoding : str Encoding for data & key when strings. hash_key : str Hash_key for string key to encode. categorize : bool Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values. Returns ------- np.ndarray[uint64] An array of hashed values. See Also -------- api.extensions.ExtensionArray._values_for_factorize : Return an array and missing value suitable for factorization. util.hash_array : Given a 1d array, return an array of hashed values. Examples -------- >>> pd.array([1, 2])._hash_pandas_object( ... encoding="utf-8", hash_key="1000000000000000", categorize=False ... ) array([ 6238072747940578789, 15839785061582574730], dtype=uint64) ) hash_array)rrr)pandas.core.util.hashingrrf)ryrrrrr`_s&$$$ rQ_hash_pandas_object"ExtensionArray._hash_pandas_objectf s*H 8..0    rTcV^8dQhRR/#)rKrNz#tuple[Self, npt.NDArray[np.uint64]]rO)rPs"rQrRrS s""="rTc VP4p\P!\V43\PR7pW3#)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)) )rrH)rIrtonesruint64)ryr`countss& rQ_explodeExtensionArray._explode s2@D |299=~rTcV^8dQhRR/#)rKrNlistrO)rPs"rQrRrS srTc VP^8d VUu.uFqP4NK up#\V4#uupi)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 Python list of values in array. See Also -------- Index.to_list: Return a list of the values in the Index. Series.to_list: Return a list of the values in the Series. Examples -------- >>> arr = pd.array([1, 2, 3]) >>> arr.tolist() [1, 2, 3] )rtolistr)ryrs& rQrExtensionArray.tolist s70 99q=(,-1HHJ- -Dz.s<c V^8dQhRRRR/#)rKlocr;rNrrO)rPs"rQrRrS s""+""rTc \P!\P!\V44V4pVP V4#r)rtdeletertrr()ryrr+s&& rQrExtensionArray.delete s.))BIIc$i0#6yy!!rTc V^8dQhRRRR/#)rKrrrNrrO)rPs"rQrRrS s)P)P#)P)PrTc \V\V44p\V4PV.VPR7p\V4P VRVW0VR.4#)a Insert an item at the given position. Parameters ---------- loc : int Index where the `item` needs to be inserted. item : scalar-like Value to be inserted. Returns ------- ExtensionArray With `item` inserted at `loc`. See Also -------- Index.insert: Make new Index inserting new item at location. 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 rqN)rrrsrXrHrI)ryrr}item_arrs&&& rQinsertExtensionArray.insert sZJ"#s4y1:,,dV4::,FDz++T$3Z:,NOOrTc V^8dQhRRRR/#)rKrr>rNrrO)rPs"rQrRrS s2drTc F\V4'd W!,pMTpW0V&R#)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)ryrrvals&&& rQ_putmaskExtensionArray._putmask s"(   +CCT rTc V^8dQhRRRR/#)rKrr>rNrrO)rPs"rQrRrS s0DrTc jVP4p\V4'd W!(,pMTpWCV(&V#)z Analogue to np.where(mask, self, value) Parameters ---------- mask : np.ndarray[bool] value : scalar or listlike Returns ------- same type as self )rIr)ryrrrrs&&& rQ_whereExtensionArray._where s6   ,CCu  rTrraverage na_optionr=pctc ,V^8dQhRRRRRRRRRR/#) rKrr4rrrrrMrrO)rPs"rQrRrS2 s>          rTc  >V^8wd\h\VVVVVVR7#)z See Series.rank.__doc__. )rrrrr)rr()ryrrrrrs&$$$$$rQ_rankExtensionArray._rank2 s0 19% %    rTc V^8dQhRRRR/#)rKrr>rHrrO)rPs"rQrRrSK s5rTc VP.VR7p\P!\P!R4V4pVP VRR7p\ WP4'dW%P 8wd\RV R24hV#)z Create an ExtensionArray with the given shape and dtype. See also -------- ExtensionDtype.empty ExtensionDtype.empty is the 'official' public version of this API. rqTr!z5Default 'empty' implementation is invalid for dtype='rr#)rXrt broadcast_tointpr(rrHr)rVrrHobjtakerrs&&& rQ_emptyExtensionArray._emptyJ sw  5 1 U3%D1&&&%<<*?%GwaP  rTc$V^8dQhRRRRRR/#)rKqsznpt.NDArray[np.float64] interpolationrrNrrO)rPs"rQrRrSa s"5535C5D5rTc \P!VP44p\P!V4p\Pp\ WCWQV4p\ V4P V4#)z Compute the quantiles of self for each quantile in `qs`. Parameters ---------- qs : np.ndarray[float64] interpolation: str Returns ------- same type as self )rtrurrer*rsrX)ryrrrrXrE res_valuess&&& rQ _quantileExtensionArray._quantilea sRzz$))+&jjVV ':=Q Dz((44rTc V^8dQhRRRR/#)rKr:rMrNrrO)rPs"rQrRrSu sDDrTc "\WR7wr#V#)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')ryr:rrs&& rQ_modeExtensionArray._modeu s$-  rTc V^8dQhRRRR/#)rKufuncznp.ufuncrrrO)rPs"rQrRrS sUUXUsUrTc \;QJdRV4F 'gK RM RM !RV44'd\#\P!WV.VO5/VBpV\JdV#RV9d\P!WV.VO5/VB#VR8Xd(\P !WV.VO5/VBpV\JdV#\P !WV.VO5/VB#)c3X"TF p\V\\\34xK" R#5ir)rrrr).0rs& rQ 1ExtensionArray.__array_ufunc__.. s% PVuJuy(LA B BPVrTFoutreduce)rNotImplementedr !maybe_dispatch_ufunc_to_dunder_opdispatch_ufunc_with_outdispatch_reduction_ufuncdefault_array_ufunc)ryrrinputsrrs&&&*, rQ__array_ufunc__ExtensionArray.__array_ufunc__ s 3 PV 333 PV   " !<<  "( ,2   'M F?44V&,06  X 77V&,06F^+ ,,T&T6TVTTrTcV^8dQhRR/#)rK na_actionzLiteral['ignore'] | NonerO)rPs"rQrRrS s<<%= ['skipna'] 'var', 'std', 'sem' -> ['ddof'] 'cumprod', 'cumsum', 'cummin', 'cummax' -> ['skipna'] 'rank' -> ['ties_method', 'ascending', 'na_option', 'pct'] Returns ------- np.ndarray or ExtensionArray ) StringDtype)WrappedCythonOp)r5rr6zdtype 'z' does not support operation 'rFsumr)rz,function is not implemented for this dtype: r7r8comp_idsrNinitialrq) rmeanmedianr2cumprodstdsemvarskewkurt)rr%)pandas.core.arrays.string_r;pandas.core.groupby.opsr<get_kind_from_howrrHr5ri_get_cython_functionrrtrvr5rrer_cython_op_ndim_compatcast_blocklistrrX)ryr5r6r7r8r9rr;r<ropr@rXnpvaluesrrHstring_array_clss&$$$$$, rQ _groupby_opExtensionArray._groupby_op sH ;;005  O djj+ . .vv   djj\)GuANvv^+''&9I5QCvv 6)))(## Q**R.C||FRVV|tzzlK ..            66R&& &  djj+ . .JJE$99; #22:2K K& %rTrOr).)T)NT)first)rN)leftN)F)C)Vr __module__ __qualname____firstlineno____doc___typ__pandas_priority__r classmethodrXr]rbrmrzr rrrrrrrr}r rrpropertyrHrrrrrrrrrr rrr-r5r:r#rMr)rWr^r%rfrmrDror|r(rIrrrrrrrrrIrrrr__annotations__rrrrrrrrrrrrr,r2rR__static_attributes__rOrTrQrFrFrsUr D I #'/3#'BG#'#'J%'=B%'%'N'':>OB:: ==(B4Sl((( ( 0h'+>> (\((&&##"((,OO TT JJO:b (D''!F6 6 % 6 " 6 p *D *Dl \T! T ;? T  TlHT"6 =DA.F>4:Ax29h.@+B?!B(.  *T,\%V_(! _(  _(B(< ??(\/ ; 7"/j(  >$'$'V''/S+//SbJ+/JBGJ^  ) V"H8")PV6.         0,5(*U4<2d&d&rTrFc]tRtRt]RR/RRll4t]RRl4tRR/R R llt]RR/R R ll4t]R Rl4tRR/RRlltRtR#)ExtensionArraySupportsAnyAlli$ r.c V^8dQhRRRR/#rKrz Literal[True]rNrMrO)rPs"rQrR)ExtensionArraySupportsAnyAll.__annotate__& >>]>T>rTc R#rrOr s&$rQr ExtensionArraySupportsAnyAll.any% ;>rTc V^8dQhRRRR/#rKrrMrNz bool | NATyperO)rPs"rQrRre) 88T8m8rTc R#rrOr s&$rQrrh( 58rTTc V^8dQhRRRR/#rkrO)rPs"rQrRre+ ((T(](rTc \V4hrrr s&$rQrrh+ !$''rTc V^8dQhRRRR/#rdrO)rPs"rQrRre/ rfrTc R#rrOr s&$rQr% ExtensionArraySupportsAnyAll.all. rirTc V^8dQhRRRR/#rkrO)rPs"rQrRre2 rlrTc R#rrOr s&$rQr%ru1 rnrTc V^8dQhRRRR/#rkrO)rPs"rQrRre4 rprTc \V4hrrr s&$rQr%ru4 rrrTrON)rrWrXrYr rr%r`rOrTrQrbrb$ sc >S>> 88(D(>S>> 88(D((rTrbc]tRtRtRt]R4t]RRl4t]R4t]RRl4t ]R 4t ]R R l4t R t R #)ExtensionOpsMixini8 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 \V4hrrrVrOs&&rQ_create_arithmetic_method+ExtensionOpsMixin._create_arithmetic_methodC !#&&rTcV^8dQhRR/#rrO)rPs"rQrRExtensionOpsMixin.__annotate__H sVVDVrTc 2\VRVP\P44\VRVP\P 44\VRVP\P 44\VRVP\P44\VRVP\P44\VRVP\P44\VRVP\P44\VRVP\P44\VRVP\P44\VR VP\P44\VR VP\P44\VR VP\P44\VR VP\P 44\VR VP\P"44\VRVP\$44\VRVP\P&44R#)__add____radd____sub____rsub____mul____rmul____pow____rpow____mod____rmod__ __floordiv__ __rfloordiv__ __truediv__ __rtruediv__ __divmod__ __rdivmod__N)setattrr~operatoraddr"raddsubrsubmulrmulpowrpowmodrmodfloordiv rfloordivtruedivrtruedivdivmodrdivmodrVs&rQ_add_arithmetic_ops%ExtensionOpsMixin._add_arithmetic_opsG 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$A)BSBS$TUrTc \V4hrrr}s&&rQ_create_comparison_method+ExtensionOpsMixin._create_comparison_method\ rrTcV^8dQhRR/#rrO)rPs"rQrRra sKKDKrTc \VRVP\P44\VRVP\P44\VRVP\P 44\VRVP\P 44\VRVP\P44\VRVP\P44R#)rr__lt____gt____le____ge__N) rrreqneltgtlegers&rQ_add_comparison_ops%ExtensionOpsMixin._add_comparison_ops` sXs<> JKY : :8<< HIZ!;!;INN!KLrTrON) rrWrXrYrZr]r~rrrrrr`rOrTrQr{r{8 s''VV(''KK''MMrTr{cT]tRtRtRt]R RRll4t]R4t]R4tRt R#) ExtensionScalarOpsMixiniw 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. NcV^8dQhRR/#)rKcoerce_to_dtyperMrO)rPs"rQrR$ExtensionScalarOpsMixin.__annotate__ sN7N7N7rTc PaaaVVV3RlpRSP R2p\WEV4#)a A class method that returns a method that will correspond to an operator for an ExtensionArray subclass, by dispatching to the relevant operator defined on the individual elements of the ExtensionArray. Parameters ---------- op : function An operator that takes arguments op(a, b) coerce_to_dtype : bool, default True boolean indicating whether to attempt to convert the result to the underlying ExtensionArray dtype. If it's not possible to create a new ExtensionArray with the values, an ndarray is returned instead. Returns ------- Callable[[Any, Any], Union[ndarray, ExtensionArray]] A method that can be bound to a class. When used, the method receives the two arguments, one of which is the instance of this class, and should return an ExtensionArray or an ndarray. Returning an ndarray may be necessary when the result of the `op` cannot be stored in the ExtensionArray. The dtype of the ndarray uses NumPy's normal inference rules. Examples -------- Given an ExtensionArray subclass called MyExtensionArray, use __add__ = cls._create_method(operator.add) in the class definition of MyExtensionArray to create the operator for addition, that will be based on the operator implementation of the underlying elements of the ExtensionArray cH<aV3Rlp\V\\\34'd\#SpV!V4p\ W4RR7UUu.uF wrVS !WV4NK pppV V V3RlpS P R9d\ VRR/wrVV!V4V!V43#V!V4#uuppi)c<\V\4'g\V4'dTpV#V.\S4,pV#r)rrFrr)paramovaluesrys& rQconvert_valuesNExtensionScalarOpsMixin._create_method.._binop..convert_values s=e^44 U8K8K#G %gD 1GrTT)strictc<S'dESPV4p\V\S44'g\P!V4pV#\P!VSR7pV#)rq)rzrrsrtru)rXresr result_dtyperys& rQ_maybe_convertNExtensionScalarOpsMixin._create_method.._binop.._maybe_convert sT"55c:C%c4:66 jjo **S =C rTr>rr)rrrrr&zipr) ryrrlvaluesrvaluesrKrLrrrrOrs f& rQ_binop6ExtensionScalarOpsMixin._create_method.._binop s %)X|!DEE%%G$U+G+.gt*LM*L2a8*LCM {{33C--%a(.*;;;!#& &'Ns B__)rr )rVrOrrrop_names&fff rQ_create_method&ExtensionScalarOpsMixin._create_method s+P$ 'Lr{{m2& #66rTc $VPV4#r)rr}s&&rQr~1ExtensionScalarOpsMixin._create_arithmetic_method s!!"%%rTc 2VPVR\R7#)F)rr)rrMr}s&&rQr1ExtensionScalarOpsMixin._create_comparison_method s!!"e$!OOrTrO)TN) rrWrXrYrZr]rr~rr`rOrTrQrrw sH2N7N7`&&PPrTr)e__conditional_annotations__rZ __future__rrtypingrrrrrrr rknumpyrt pandas._libsr r&r pandas.compatr pandas.compat.numpyr r pandas.errorsrpandas.util._decoratorsrrpandas.util._exceptionsrpandas.util._validatorsrrpandas.core.dtypes.astyperpandas.core.dtypes.commonrrrrpandas.core.dtypes.dtypesrpandas.core.dtypes.genericrrrpandas.core.dtypes.missingr pandas.corer r!r"r{r#r$r%r&r'r(r) pandas.core.array_algos.quantiler*pandas.core.missingr+pandas.core.sortingr,r-collections.abcr.r/r0pandas._libs.missingr1pandas._typingr2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rApandasrBrCrDr_rFrbr{r)rs@rQrs7#,.-5 5 5 , @3  ,& 02n1 #$n*&n*&%n*&bU(>((<M<M~ #$qP/qP%qPrT