m*i(%SrSSKJr SSKrSSKJrJrJrJrJ r J r J r SSK r SSK rSSKJr SSKJr SSKJrJrJrJrJrJrJr SSKJr SS KJr SS K J!r! SS K"J#r#J$r$ SS K%J&r& SS K'J(r( SSK)J*r*J+r+ SSK,J-r- SSK.J/r/J0r0J1r1J2r2 SSK3J4r4J5r5 SSK6J7r7J8r8J9r9 SSK:J;r; SSKJ?r? SSK@JArAJBrB \(aSSKCJDrDJErE SSKJFrFJGrGJHrHJIrI SSKJJKrKJLrLJMrM 0rNS\OS'SSSSS.rP"SS \;5rQ"S!S"5rR"S#S$\\5rS"S%S\=5rTg)&z. Base and utility classes for pandas objects. ) annotationsN) TYPE_CHECKINGAnyGenericLiteralcastfinaloverload)using_copy_on_write)lib)AxisIntDtypeObj IndexLabelNDFrameTSelfShapenpt)PYPY)functionAbstractMethodError)cache_readonlydoc)find_stack_level)can_hold_element)is_object_dtype is_scalar)ExtensionDtype) ABCDataFrameABCIndex ABCMultiIndex ABCSeries)isnaremove_na_arraylike) algorithmsnanopsops) DirNamesMixin)OpsMixin)ExtensionArray)ensure_wrapped_if_datetimelike extract_array)HashableIterator)DropKeep NumpySorterNumpyValueArrayLike ScalarLike_co) DataFrameIndexSerieszdict[str, str] _shared_docs IndexOpsMixin)klassinplaceunique duplicatedcd^\rSrSr%SrS\S'\S5rS SjrS S Sjjr S U4Sjjr S r U=r $) PandasObjectez' Baseclass for various pandas objects. zdict[str, Any]_cachec[U5$)z; Class constructor (for this class it's just `__class__`). )typeselfs P/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/pandas/core/base.py _constructorPandasObject._constructorms Dzc,[RU5$)z9 Return a string representation for a particular object. )object__repr__rCs rErKPandasObject.__repr__ts t$$rHc[US5(dgUcURR5 gURRUS5 g)zF Reset cached properties. If ``key`` is passed, only clears that key. r@N)hasattrr@clearpop)rDkeys rE _reset_cachePandasObject._reset_cache{s;tX&&  ; KK    KKOOC &rHc>[USS5nU(a5U"SS9n[[U5(aU5$UR55$[TU]5$)z` Generates the total memory usage for an object that returns either a value or Series of values memory_usageNTdeep)getattrintrsumsuper __sizeof__)rDrUmem __class__s rEr\PandasObject.__sizeof__sQ t^T: D)Cinns< <#'')< <w!##rH)returnstrN)rQz str | NoneraNonerarY) __name__ __module__ __qualname____firstlineno____doc____annotations__propertyrFrKrRr\__static_attributes__ __classcell__)r^s@rEr>r>es6   % ' $ $rHr>c,\rSrSrSrSSjrSSjrSrg) NoNewAttributesMixinah Mixin which prevents adding new attributes. Prevents additional attributes via xxx.attribute = "something" after a call to `self.__freeze()`. Mainly used to prevent the user from using wrong attributes on an accessor (`Series.cat/.str/.dt`). If you really want to add a new attribute at a later time, you need to use `object.__setattr__(self, key, value)`. c2[RUSS5 g)z) Prevents setting additional attributes. __frozenTN)rJ __setattr__rCs rE_freezeNoNewAttributesMixin._freezes 4T2rHc[USS5(a;US:Xd5U[U5R;d[XS5c[SUS35e[R XU5 g)NrsFr@z"You cannot add any new attribute '')rXrB__dict__AttributeErrorrJrt)rDrQvalues rErt NoNewAttributesMixin.__setattr__s^ 4U + + 8Od4j)))t$'3 #EcU!!LM M4e,rHr`N)rard)rQrbrard)rfrgrhrirjrurtrmr`rHrErprps 3 -rHrpc\rSrSr%SrS\S'SrS\S'S\S 'S S /r\"\5r \ \ S 55r \ S 5r\ \ SSj55r\ \ S55rSrSSSjjr\ SSj5rSr\rSrg)SelectionMixinz} mixin implementing the selection & aggregation interface on a group-like object sub-classes need to define: obj, exclusions robjNzIndexLabel | None _selectionzfrozenset[Hashable] exclusionsr@ __setstate__c[UR[[[[ [ R45(d UR/$UR$rc) isinstancerlisttupler"r npndarrayrCs rE_selection_listSelectionMixin._selection_listsB OOdE9h K  OO$ $rHcURb[UR[5(a UR$URUR$rc)rrrr"rCs rE _selected_objSelectionMixin._selected_objs8 ?? "j9&E&E88O88DOO, ,rHc.URR$rc)rndimrCs rErSelectionMixin.ndims!!&&&rHcP[UR[5(a UR$URb%URR UR 5$[ UR5S:a%URRURSSS9$UR$)NrT)axis only_slice) rrr"r_getitem_nocopyrlenr _drop_axisrCs rE_obj_with_exclusions#SelectionMixin._obj_with_exclusionss dhh * *88O ?? &88++D,@,@A A t ! # 88&&tQ4&P P88OrHcURb[SURS35e[U[[[ [ [R45(a[URRRU55[[U55:waQ[[U5RURR55n[S[!U5SS35eUR#[U5SS9$XR;a[SU35eURUR$nUR#XS9$) Nz Column(s) z already selectedzColumns not found: r)rzColumn not found: )r IndexErrorrrrr"r rrrrcolumns intersectionset differenceKeyErrorrb_gotitemr)rDrQbad_keysrs rE __getitem__SelectionMixin.__getitem__s ?? &z$//)::KLM M cD%HbjjI J J488##0056#c#h-GC 3 3DHH4D4D EF!4S]1R5H4IJKK==c=3 3(("!3C59::88C=%%D===0 0rHc[U5e)z sub-classes to define return a sliced object Parameters ---------- key : str / list of selections ndim : {1, 2} requested ndim of result subset : object, default None subset to act on r)rDrQrsubsets rErSelectionMixin._gotitems"$''rHc SnURS:Xa?[R"U5(aX;d[R"U5(aUnU$URS:Xa,[R"U5(aXR:XaUnU$)z? Infer the `selection` to pass to our constructor in _gotitem. Nrr)rr r is_list_likename)rDrQr selections rE_infer_selectionSelectionMixin._infer_selectionsu  ;;!  ]]3  CMc6F6Fs6K6KI[[A #--"4"4 9KIrHc[U5ercr)rDfuncargskwargss rE aggregateSelectionMixin.aggregates !$''rHr`rerc)rrY)rzSeries | DataFrame)rfrgrhrirjrkr_internal_namesr_internal_names_setr rlrrrrrrrrraggrmr`rHrEr~r~s M$(J!(##0Oo.   --  ' '   1 (    ( CrHr~c \rSrSr%SrSr\"S/5rS\S'\ S;Sj5r \ SS j5rS?S jr\ S?Sj5r\ S5r\ S?Sj5r\ S?Sj5r\ S@Sj5r\ SS\R.4SASjj5r\ \ SBSj55r\"SSSS9SCSDSjj5r\"\SSSS9SCSDSjj5rSr\rSESjr\ SBS j5r!\ SCSFS!jj5r"\ SGSHS"jj5r#S#r$\ SISJS$jj5r%\ SBS%j5r&\ SBS&j5r'\ SBS'j5r(\ SKSLS(jj5r)\"\*RVS)S)S)\,RZ"S*5S+9SMSNS,jj5r+S-\.S.'\/SOSPS/jj5r0\/SOSQS0jj5r0\"\.S.S1S29SRSSS3jj5r0S4S5.STS6jjr1\ SUSVS7jj5r2S8r3S9r4S:r5g)Wr7izK Common ops mixin to support a unified interface / docs for Series / Index itolistzfrozenset[str] _hidden_attrsc[U5ercrrCs rEdtypeIndexOpsMixin.dtype("$''rHc[U5ercrrCs rE_valuesIndexOpsMixin._values-rrHc2[R"X5 U$)zO Return the transpose, which is by definition self. Returns ------- %(klass)s )nvvalidate_transpose)rDrrs rE transposeIndexOpsMixin.transpose2s d+ rHa Return the transpose, which is by definition self. Examples -------- For Series: >>> s = pd.Series(['Ant', 'Bear', 'Cow']) >>> s 0 Ant 1 Bear 2 Cow dtype: object >>> s.T 0 Ant 1 Bear 2 Cow dtype: object For Index: >>> idx = pd.Index([1, 2, 3]) >>> idx.T Index([1, 2, 3], dtype='int64') )rc.URR$)zv Return a tuple of the shape of the underlying data. Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.shape (3,) )rshaperCs rErIndexOpsMixin.shape[s||!!!rHc[U5ercrrCs rE__len__IndexOpsMixin.__len__hs !$''rHcg)a Number of dimensions of the underlying data, by definition 1. Examples -------- >>> s = pd.Series(['Ant', 'Bear', 'Cow']) >>> s 0 Ant 1 Bear 2 Cow dtype: object >>> s.ndim 1 For Index: >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.ndim 1 rr`rCs rErIndexOpsMixin.ndimos0rHc^[U5S:Xa[[U55$[S5e)aH Return the first element of the underlying data as a Python scalar. Returns ------- scalar The first element of Series or Index. Raises ------ ValueError If the data is not length = 1. Examples -------- >>> s = pd.Series([1]) >>> s.item() 1 For an index: >>> s = pd.Series([1], index=['a']) >>> s.index.item() 'a' rz6can only convert an array of size 1 to a Python scalar)rnextiter ValueErrorrCs rEitemIndexOpsMixin.items*6 t9>T # #QRRrHc.URR$)a% Return the number of bytes in the underlying data. Examples -------- For Series: >>> s = pd.Series(['Ant', 'Bear', 'Cow']) >>> s 0 Ant 1 Bear 2 Cow dtype: object >>> s.nbytes 24 For Index: >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.nbytes 24 )rnbytesrCs rErIndexOpsMixin.nbytess4||"""rHc,[UR5$)a" Return the number of elements in the underlying data. Examples -------- For Series: >>> s = pd.Series(['Ant', 'Bear', 'Cow']) >>> s 0 Ant 1 Bear 2 Cow dtype: object >>> s.size 3 For Index: >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.size 3 )rrrCs rEsizeIndexOpsMixin.sizes44<<  rHc[U5e)a The ExtensionArray of the data backing this Series or Index. Returns ------- ExtensionArray An ExtensionArray of the values stored within. For extension types, this is the actual array. For NumPy native types, this is a thin (no copy) wrapper around :class:`numpy.ndarray`. ``.array`` differs from ``.values``, which may require converting the data to a different form. See Also -------- Index.to_numpy : Similar method that always returns a NumPy array. Series.to_numpy : Similar method that always returns a NumPy array. Notes ----- This table lays out the different array types for each extension dtype within pandas. ================== ============================= dtype array type ================== ============================= category Categorical period PeriodArray interval IntervalArray IntegerNA IntegerArray string StringArray boolean BooleanArray datetime64[ns, tz] DatetimeArray ================== ============================= For any 3rd-party extension types, the array type will be an ExtensionArray. For all remaining dtypes ``.array`` will be a :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray stored within. If you absolutely need a NumPy array (possibly with copying / coercing data), then use :meth:`Series.to_numpy` instead. Examples -------- For regular NumPy types like int, and float, a NumpyExtensionArray is returned. >>> pd.Series([1, 2, 3]).array [1, 2, 3] Length: 3, dtype: int64 For extension types, like Categorical, the actual ExtensionArray is returned >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.array ['a', 'b', 'a'] Categories (2, object): ['a', 'b'] rrCs rEarrayIndexOpsMixin.arrays~"$''rHNFc [UR[5(aURR"U4X#S.UD6$U(a1[ [ UR555n[SUS35eU[RL=(aM U[RL=(a/ [R"UR[R5(+nURnU(aV[!Xs5(d[R""XqS9nOUR%5nX7[R&"[)U55'[R""XqS9nU(aU(aU(d[+5(au[R,"URSSUSS5(aI[+5(a*U(d#UR/5nSUR0lU$UR%5nU$)ac A NumPy ndarray representing the values in this Series or Index. 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 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. **kwargs Additional keywords passed through to the ``to_numpy`` method of the underlying array (for extension arrays). Returns ------- numpy.ndarray See Also -------- Series.array : Get the actual data stored within. Index.array : Get the actual data stored within. DataFrame.to_numpy : Similar method for DataFrame. Notes ----- The returned array will be the same up to equality (values equal in `self` will be equal in the returned array; likewise for values that are not equal). When `self` contains an ExtensionArray, the dtype may be different. For example, for a category-dtype Series, ``to_numpy()`` will return a NumPy array and the categorical dtype will be lost. For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming ``copy=False``). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that). For extension types, ``to_numpy()`` *may* require copying data and coercing the result to a NumPy type (possibly object), which may be expensive. When you need a no-copy reference to the underlying data, :attr:`Series.array` should be used instead. This table lays out the different dtypes and default return types of ``to_numpy()`` for various dtypes within pandas. ================== ================================ dtype array type ================== ================================ category[T] ndarray[T] (same dtype as input) period ndarray[object] (Periods) interval ndarray[object] (Intervals) IntegerNA ndarray[object] datetime64[ns] datetime64[ns] datetime64[ns, tz] ndarray[object] (Timestamps) ================== ================================ Examples -------- >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.to_numpy() array(['a', 'b', 'a'], dtype=object) Specify the `dtype` to control how datetime-aware data is represented. Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp` objects, each with the correct ``tz``. >>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET")) >>> ser.to_numpy(dtype=object) array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'), Timestamp('2000-01-02 00:00:00+0100', tz='CET')], dtype=object) Or ``dtype='datetime64[ns]'`` to return an ndarray of native datetime64 values. The values are converted to UTC and the timezone info is dropped. >>> ser.to_numpy(dtype="datetime64[ns]") ... # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'], dtype='datetime64[ns]') )copyna_valuez/to_numpy() got an unexpected keyword argument 'rxrNrF)rrrrto_numpyrrkeys TypeErrorr no_defaultrnan issubdtypefloatingrrasarrayr asanyarrayr#r shares_memoryviewflags writeable) rDrrrrrfillnavaluesresults rErIndexOpsMixin.to_numpy!sl~ djj. 1 1::&&uU4UfU U D/0HA(1M  CNN * T'RBMM$**bkk,RS   #F55F8082==d, -F0 2E2G2G Ra 0&!*==&((#[[]F-2FLL* $[[]F rHc$UR(+$rc)rrCs rEemptyIndexOpsMixin.emptys99}rHmaxminlargest)opopposer{c0URn[R"U5 [R"X#U5n[ U[ 5(atU(d]UR 5R5(a:[R"S[U5RS3[[5S9 gUR5$[R "XRS9nUS:Xa9[R"S[U5RS3[[5S9 U$)a Return int position of the {value} value in the Series. If the {op}imum is achieved in multiple locations, the first row position is returned. Parameters ---------- axis : {{None}} Unused. Parameter needed for compatibility with DataFrame. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- int Row position of the {op}imum value. See Also -------- Series.arg{op} : Return position of the {op}imum value. Series.arg{oppose} : Return position of the {oppose}imum value. numpy.ndarray.arg{op} : Equivalent method for numpy arrays. Series.idxmax : Return index label of the maximum values. Series.idxmin : Return index label of the minimum values. Examples -------- Consider dataset containing cereal calories >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0, ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}}) >>> s Corn Flakes 100.0 Almond Delight 110.0 Cinnamon Toast Crunch 120.0 Cocoa Puff 110.0 dtype: float64 >>> s.argmax() 2 >>> s.argmin() 0 The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed. The behavior of x.argmax/argmin with skipna=False and NAs, or with all-NAs is deprecated. In a future version this will raise ValueError. stacklevelrskipna)rrvalidate_minmax_axisvalidate_argmax_with_skipnarr*r#anywarningswarnrBrf FutureWarningrargmaxr& nanargmaxrDrrrrdelegaters rEr IndexOpsMixin.argmaxsl<< %//fE h / /hmmo1133 &tDz':':&;F| &tDz':':&;F| &tDz':':&;>> s = pd.Series([1, 2, 3]) >>> s.to_list() [1, 2, 3] For Index: >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.to_list() [1, 2, 3] )rrrCs rErIndexOpsMixin.tolist"sD||""$$rHc[UR[R5(d[ UR5$[ URR [URR55$)a Return an iterator 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 ------- iterator Examples -------- >>> s = pd.Series([1, 2, 3]) >>> for x in s: ... print(x) 1 2 3 ) rrrrrmaprrangerrCs rE__iter__IndexOpsMixin.__iter__HsN,$,, 33 % %t||((% 0A0A*BC CrHcF[[U5R55$)z Return True if there are any NaNs. Enables various performance speedups. Returns ------- bool Examples -------- >>> s = pd.Series([1, 2, 3, None]) >>> s 0 1.0 1 2.0 2 3.0 3 NaN dtype: float64 >>> s.hasnans True )boolr#rrCs rEhasnansIndexOpsMixin.hasnansds2DJNN$%%rHcURn[U[5(aURXS9$[R "XAX#S9$)a An internal function that maps values using the input correspondence (which can be a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series The input correspondence object na_action : {None, 'ignore'} If 'ignore', propagate NA values, without passing them to the mapping function convert : bool, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object. Note that the dtype is always preserved for some extension array dtypes, such as Categorical. Returns ------- Union[Index, MultiIndex], inferred The output of the mapping function applied to the index. If the function returns a tuple with more than one element a MultiIndex will be returned. ) na_action)r!convert)rrr*rr% map_array)rDmapperr!r"arrs rE _map_valuesIndexOpsMixin._map_valuess>2ll c> * *77677 7##C9VVrHc 4[R"UUUUUUS9$)a= Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Parameters ---------- normalize : bool, default False If True then the object returned will contain the relative frequencies of the unique values. sort : bool, default True Sort by frequencies when True. Preserve the order of the data when False. ascending : bool, default False Sort in ascending order. bins : int, optional Rather than count values, group them into half-open bins, a convenience for ``pd.cut``, only works with numeric data. dropna : bool, default True Don't include counts of NaN. Returns ------- Series See Also -------- Series.count: Number of non-NA elements in a Series. DataFrame.count: Number of non-NA elements in a DataFrame. DataFrame.value_counts: Equivalent method on DataFrames. Examples -------- >>> index = pd.Index([3, 1, 2, 3, 4, np.nan]) >>> index.value_counts() 3.0 2 1.0 1 2.0 1 4.0 1 Name: count, dtype: int64 With `normalize` set to `True`, returns the relative frequency by dividing all values by the sum of values. >>> s = pd.Series([3, 1, 2, 3, 4, np.nan]) >>> s.value_counts(normalize=True) 3.0 0.4 1.0 0.2 2.0 0.2 4.0 0.2 Name: proportion, dtype: float64 **bins** Bins can be useful for going from a continuous variable to a categorical variable; instead of counting unique apparitions of values, divide the index in the specified number of half-open bins. >>> s.value_counts(bins=3) (0.996, 2.0] 2 (2.0, 3.0] 2 (3.0, 4.0] 1 Name: count, dtype: int64 **dropna** With `dropna` set to `False` we can also see NaN index values. >>> s.value_counts(dropna=False) 3.0 2 1.0 1 2.0 1 4.0 1 NaN 1 Name: count, dtype: int64 )sort ascending normalizebinsdropna)r%value_counts_internal)rDr+r)r*r,r-s rE value_countsIndexOpsMixin.value_countss*n//    rHcURn[U[R5(dUR 5nU$[ R "U5nU$rc)rrrrr;r%unique1d)rDrrs rEr;IndexOpsMixin.uniquesE&"**--]]_F  ((0F rHc\UR5nU(a [U5n[U5$)a Return number of unique elements in the object. Excludes NA values by default. Parameters ---------- dropna : bool, default True Don't include NaN in the count. Returns ------- int See Also -------- DataFrame.nunique: Method nunique for DataFrame. Series.count: Count non-NA/null observations in the Series. Examples -------- >>> s = pd.Series([1, 3, 5, 7, 7]) >>> s 0 1 1 3 2 5 3 7 4 7 dtype: int64 >>> s.nunique() 4 )r;r$r)rDr-uniqss rEnuniqueIndexOpsMixin.nuniques'F  '.E5zrHc8URSS9[U5:H$)z Return boolean if values in the object are unique. Returns ------- bool Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.is_unique True >>> s = pd.Series([1, 2, 3, 1]) >>> s.is_unique False F)r-)r6rrCs rE is_uniqueIndexOpsMixin.is_unique0s&||5|)SY66rHc2SSKJn U"U5R$)z Return boolean if values in the object are monotonically increasing. Returns ------- bool Examples -------- >>> s = pd.Series([1, 2, 2]) >>> s.is_monotonic_increasing True >>> s = pd.Series([3, 2, 1]) >>> s.is_monotonic_increasing False rr4)pandasr4is_monotonic_increasingrDr4s rEr>%IndexOpsMixin.is_monotonic_increasingE& !T{222rHc2SSKJn U"U5R$)z Return boolean if values in the object are monotonically decreasing. Returns ------- bool Examples -------- >>> s = pd.Series([3, 2, 2, 1]) >>> s.is_monotonic_decreasing True >>> s = pd.Series([1, 2, 3]) >>> s.is_monotonic_decreasing False rr<)r=r4is_monotonic_decreasingr?s rErC%IndexOpsMixin.is_monotonic_decreasing\rArHcl[URS5(aURRUS9$URRnU(ab[ UR 5(aH[ (d=[[RUR5nU[R"U5- nU$)a  Memory usage of the values. Parameters ---------- deep : bool, default False Introspect the data deeply, interrogate `object` dtypes for system-level memory consumption. Returns ------- bytes used See Also -------- numpy.ndarray.nbytes : Total bytes consumed by the elements of the array. Notes ----- Memory usage does not include memory consumed by elements that are not components of the array if deep=False or if used on PyPy Examples -------- >>> idx = pd.Index([1, 2, 3]) >>> idx.memory_usage() 24 rUrV) rNrrUrrrrrrrrr memory_usage_of_objects)rDrWvrs rE _memory_usageIndexOpsMixin._memory_usagess> 4::~ . .::**+  JJ   ODJJ//"**dll3F ,,V4 4ArHr8z sort : bool, default False Sort `uniques` and shuffle `codes` to maintain the relationship. )rorder size_hintr)cx[R"URXS9up4UR[R :XaUR [R5n[U[5(aURU5nX44$SSK J n U"X@RS9nX44$![a U"U5nX44$f=f)N)r)use_na_sentinelrr<r)r% factorizerrrfloat16astypefloat32rr!rFr=r4NotImplementedError)rDr)rMcodesuniquesr4s rErNIndexOpsMixin.factorizes$$-- LLt  ==BJJ &nnRZZ0G dM * *''0G~ % )zz: ~ ' ) .~  )sB""B98B9a Find indices where elements should be inserted to maintain order. Find the indices into a sorted {klass} `self` such that, if the corresponding elements in `value` were inserted before the indices, the order of `self` would be preserved. .. note:: The {klass} *must* be monotonically sorted, otherwise wrong locations will likely be returned. Pandas does *not* check this for you. Parameters ---------- value : array-like or scalar Values 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 `self` into ascending order. They are typically the result of ``np.argsort``. Returns ------- int or array of int A scalar or array of insertion points with the same shape as `value`. See Also -------- sort_values : Sort by the values along either axis. numpy.searchsorted : Similar method from NumPy. Notes ----- Binary search is used to find the required insertion points. Examples -------- >>> ser = pd.Series([1, 2, 3]) >>> ser 0 1 1 2 2 3 dtype: int64 >>> ser.searchsorted(4) 3 >>> ser.searchsorted([0, 4]) array([0, 3]) >>> ser.searchsorted([1, 3], side='left') array([0, 2]) >>> ser.searchsorted([1, 3], side='right') array([1, 3]) >>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000'])) >>> ser 0 2000-03-11 1 2000-03-12 2 2000-03-13 dtype: datetime64[ns] >>> ser.searchsorted('3/14/2000') 3 >>> ser = pd.Categorical( ... ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True ... ) >>> ser ['apple', 'bread', 'bread', 'cheese', 'milk'] Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk'] >>> ser.searchsorted('bread') 1 >>> ser.searchsorted(['bread'], side='right') array([3]) If the values are not monotonically sorted, wrong locations may be returned: >>> ser = pd.Series([2, 1, 3]) >>> ser 0 2 1 1 2 3 dtype: int64 >>> ser.searchsorted(1) # doctest: +SKIP 0 # wrong result, correct would be 1 searchsortedcgrcr`rDr{sidesorters rErVIndexOpsMixin.searchsorted, rHcgrcr`rXs rErVr[5r\rHr4)r9c[U[5(a$S[U5RS3n[ U5eUR n[U[ R5(dURXUS9$[R"UUUUS9$)Nz(Value must be 1-D array-like or scalar, z is not supported)rYrZ) rrrBrfrrrrrVr%)rDr{rYrZmsgrs rErVr[>s e\ * *:;''((9; S/ !&"**--&&u&G G&&     rHfirstkeepc*URUS9nX)$Nra) _duplicated)rDrbr<s rEdrop_duplicatesIndexOpsMixin.drop_duplicatesXs %%4%0 K  rHcURn[U[5(aURUS9$[R"X!S9$rd)rrr*r<r%)rDrbr%s rEreIndexOpsMixin._duplicated]s:ll c> * *>>t>, ,$$S44rHc[R"X5nURn[USSS9n[R"XTR 5n[ U5n[U[5(a6[R"URURUR5n[R"SS9 [R"XEU5nSSS5 UR!WUS9$!,(df  N=f)NT) extract_numpy extract_rangeignore)all)r)r'get_op_result_namerr,maybe_prepare_scalar_for_oprr+rrrarangestartstopsteperrstate arithmetic_op_construct_result)rDotherrres_namelvaluesrvaluesrs rE _arith_methodIndexOpsMixin._arith_methodds))$6,,TN11'==I09 gu % %ii w||W\\JG [[X &&&wrCrHr%rNtextwrapdedentr6r rVrfrer|rwrmr`rHrEr7r7sM $-  %M>((((       A: " "(2 S S<##6!!6>(>(@ '+>> C#CC C  C CJ    E%y1:>Q"Q37Q Q2Qf E%z::>"37 ;B"%HGD8&&4 W W>    ] ] ]  ]  ]  ]  ] ~ % %N77(33,33, ' 'R  __   $ ,  6` R*-!   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