(jMdZddlmZddlZddlZddlmZmZmZm Z m Z ddl Z ddl Z ddlmZmZmZmZddlmZddlmZmZmZmZmZmZmZddlmZdd l m!Z!dd l"m#Z#m$Z$dd l%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5dd l6m7Z7dd l8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@mAZAmBZBmCZCmDZDddlEmFZFmGZGddlHmIZIddlJmKZLmMZMmNZNddlOmPZPer,ddlmQZQmRZRmSZSddlTmUZUmVZVmWZWddlXmYZYmZZZedeVeUzeZzZ[d7dZ\ d8dZ]d9dZ^ejejejejejejejejejejejejejejdZm d:dZnd;dZoe dd"Zqd?d@d#ZrepZsd$ZtdAd%Zu dB dCd&Zved  dD dEd'Zw dF dGd(Zx d? dHd)Zy dI dJd*Zz dK dLd+Z{ dM dNd,Z|ed- dO dPd.Z} dQ dRd/Z~hd0ZdSdTd1Z dU dVd2ZdWd3ZdXd4Z dYd5Z d? dZd6Zy)[zl Generic data algorithms. This module is experimental at the moment and not intended for public consumption ) annotationsN) TYPE_CHECKINGLiteralTypeVarcastoverload)algos hashtableiNaTlibNA) AnyArrayLike ArrayLike ArrayLikeTAxisIntDtypeObj TakeIndexernpt) set_module)find_stack_level)'construct_1d_object_array_from_listlikenp_find_common_type)ensure_float64 ensure_objectensure_platform_int is_bool_dtypeis_complex_dtype is_dict_likeis_dtype_equalis_extension_array_dtypeis_floatis_float_dtype is_integeris_integer_dtype is_list_likeis_object_dtypeis_signed_integer_dtypeneeds_i8_conversion) concat_compat)BaseMaskedDtypeCategoricalDtypeExtensionDtype NumpyEADtype)ABCDatetimeArrayABCExtensionArrayABCIndex ABCMultiIndexABCNumpyExtensionArray ABCSeriesABCTimedeltaArray)isnana_value_for_dtype)take_nd)arrayensure_wrapped_if_datetimelike extract_array)validate_indices)ListLike NumpySorterNumpyValueArrayLike) CategoricalIndexSeries)BaseMaskedArrayExtensionArrayT)boundct|ts t|d}t|jrt t j|St|jtrBtd|}|jst|jSt j|St|jtrtd|}|jSt|jrdt|t j r$t j|j#dSt j|j%ddSt'|jrt j|St)|jr8|jj*dvr t-|St j|St/|jrtt j |St1|jr-|j#d }tt j |}|St j|t2 }t |S) a routine to ensure that our data is of the correct input dtype for lower-level routines This will coerce: - ints -> int64 - uint -> uint64 - bool -> uint8 - datetimelike -> i8 - datetime64tz -> i8 (in local tz) - categorical -> codes Parameters ---------- values : np.ndarray or ExtensionArray Returns ------- np.ndarray T extract_numpyrCr@uint8Fcopy) i8dtype) isinstancer2r;r'rRrnpasarrayr+r_hasna _ensure_data_datar,codesrndarrayviewastyper%r#itemsizerrr)object)valuesnpvaluess 8C:\FundamentalWorkbench\vendor\pandas\core\algorithms.pyrWrWrs, fm ,vT:v||$RZZ/00 FLL/ 2'0}}  - -zz&!! FLL"2 3mV,|| v|| $ fbjj )::f%**73 3::f%,,W5,A A &,, 'zz&!!  % << K /!&) )zz&!! &,, 'BJJ'' V\\ *;;t$ H-ZZf -F   ct|tr|j|k(r|St|tjs#|j }|j ||S|j |dS)z reverse of _ensure_data Parameters ---------- values : np.ndarray or ExtensionArray dtype : np.dtype or ExtensionDtype original : AnyArrayLike Returns ------- ExtensionArray or np.ndarray rQFrK)rSr0rRrTconstruct_array_type_from_sequencer\)r_rRoriginalclss ra_reconstruct_datarhsj &+,1F eRXX &((* !!&!66 ==U= ++rbcft|ttttj t fs|dk7r$t|dt|jdtj|d}|dvr(t|tr t|}t|}|St j|}|S)z5 ensure that we are arraylike if not already isin-targetszQ requires a Series, Index, ExtensionArray, np.ndarray or NumpyExtensionArray got .Fskipna)mixedstring mixed-integer)rSr1r4r0rTrZr3 TypeErrortype__name__r infer_dtypetuplelistrrU)r_ func_nameinferreds ra_ensure_arraylikerys  9/=ST   &+F|,,-Q0  ??6%8 ; ;&%(f>> pd.unique(pd.Series([2, 1, 3, 3])) array([2, 1, 3]) >>> pd.unique(pd.Series([2] + [1] * 5)) array([2, 1]) >>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")])) array(['2016-01-01T00:00:00.000000'], dtype='datetime64[us]') >>> pd.unique( ... pd.Series( ... [ ... pd.Timestamp("20160101", tz="US/Eastern"), ... pd.Timestamp("20160101", tz="US/Eastern"), ... ], ... dtype="M8[ns, US/Eastern]", ... ) ... ) ['2016-01-01 00:00:00-05:00'] Length: 1, dtype: datetime64[ns, US/Eastern] >>> pd.unique( ... pd.Index( ... [ ... pd.Timestamp("20160101", tz="US/Eastern"), ... pd.Timestamp("20160101", tz="US/Eastern"), ... ], ... dtype="M8[ns, US/Eastern]", ... ) ... ) DatetimeIndex(['2016-01-01 00:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None) >>> pd.unique(np.array(list("baabc"), dtype="O")) array(['b', 'a', 'c'], dtype=object) An unordered Categorical will return categories in the order of appearance. >>> pd.unique(pd.Series(pd.Categorical(list("baabc")))) ['b', 'a', 'c'] Categories (3, str): ['a', 'b', 'c'] >>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc")))) ['b', 'a', 'c'] Categories (3, str): ['a', 'b', 'c'] An ordered Categorical preserves the category ordering. >>> pd.unique( ... pd.Series( ... pd.Categorical(list("baabc"), categories=list("abc"), ordered=True) ... ) ... ) ['b', 'a', 'c'] Categories (3, str): ['a' < 'b' < 'c'] An array of tuples >>> pd.unique(pd.Series([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")]).values) array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object) A NumpyExtensionArray of complex >>> pd.unique(pd.array([1 + 1j, 2, 3])) [(1+1j), (2+0j), (3+0j)] Length: 3, dtype: complex128 )unique_with_maskrs rarrGsT F ##rbct|dk(ryt|}tj|j j ddk7j }|S)aH Return the number of unique values for integer array-likes. Significantly faster than pandas.unique for long enough sequences. No checks are done to ensure input is integral. Parameters ---------- values : 1d array-like Returns ------- int : The number of unique values in ``values`` rintp)lenrWrTbincountravelr\sum)r_results ra nunique_intsrsO 6{a & !Fkk&,,.//78A= B B DF Mrbct|d}t|jtr|j St|t r|j S|}t |\}}|t|}|*|j |}t||j|}|S|j ||\}}t||j|}|J||jdfS)z?See algorithms.unique for docs. Takes a mask for masked arrays.rrwmaskbool) ryrSrRr-rr1rrrhr\)r_rrfr tableuniquess rarrs v :F&,,/}}&(#}}H+F3Iv c&k "E |,,v&#GX^^XF V$ 7 #GX^^XF F+++rbi@BcDt|s"tdt|jdt|s"tdt|jdt |t t ttjfsat|}t|d}t|dkDro|jjdvrWt|sLt!||s@t#|}n4t |t$rtj&|}nt)|dd}t|d }t)|d }t |tjs|j+|St-|jrt/|j+|St-|jr:t1|js%tj2|j4t6 St-|jrt+||j9t:St |jt<r2t+tj>|tj>|St|t@kDrTt|d krF|jt:k7r3tCd |Ds!tE|jCrd}nZd}nVtG|j|j}|j9|d}|j9|d}tHjJ}|||S)z Compute the isin boolean array. Parameters ---------- comps : list-like values : list-like Returns ------- ndarray[bool] Same length as `comps`. zIonly list-like objects are allowed to be passed to isin(), you passed a ``rjrriufcbT)rI extract_rangeisinrHrQc3,K|] }|tuywrr ).0vs ra zisin..9s,VAGVsctjtj||jtj|Sr)rT logical_orrrisnan)crs rafzisin..f?s.}}RWWQ]%8%8%:BHHQKHHrbcJtj||jSr)rTrr)abs razisin..CsRWWQ]002rbFrK)&r&rqrrrsrSr1r4r0rTrZrvryrrRkindr(r rr2r9r;rr)pd_arrayr'zerosshaperr\r^r-rU_MINIMUM_COMP_ARR_LENanyr6rhtableismember)compsr_ orig_values comps_arrayrcommons rarrs   ((,U (<(<'=Q @     ((,V (=(='>a A  fx4ErzzR S6l ";.I K!O !!W,+E2"651=[IF FM *&!vTN#EVrbct|ttfr|j||St |d}|}t|t t fr%|j|j|\}}||fSt|tjs|j|\}}ntj|}|s\|jtk(rIt|}|jr.t|jd}tj |||}t#|||\}}|r!t%|d kDrt'|||d d \}}t)||j|}||fS) a1 Encode the object as an enumerated type or categorical variable. This method is useful for obtaining a numeric representation of an array when all that matters is identifying distinct values. `factorize` is available as both a top-level function :func:`pandas.factorize`, and as a method :meth:`Series.factorize` and :meth:`Index.factorize`. Parameters ---------- values : sequence A 1-D sequence. Sequences that aren't pandas objects are coerced to ndarrays before factorization. sort : bool, default False Sort `uniques` and shuffle `codes` to maintain the relationship. 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. size_hint : int, optional Hint to the hashtable sizer. Returns ------- codes : ndarray An integer ndarray that's an indexer into `uniques`. ``uniques.take(codes)`` will have the same values as `values`. uniques : ndarray, Index, or Categorical The unique valid values. When `values` is Categorical, `uniques` is a Categorical. When `values` is some other pandas object, an `Index` is returned. Otherwise, a 1-D ndarray is returned. .. note:: Even if there's a missing value in `values`, `uniques` will *not* contain an entry for it. See Also -------- cut : Discretize continuous-valued array. unique : Find the unique value in an array. Notes ----- Reference :ref:`the user guide ` for more examples. Examples -------- These examples all show factorize as a top-level method like ``pd.factorize(values)``. The results are identical for methods like :meth:`Series.factorize`. >>> codes, uniques = pd.factorize(np.array(["b", "b", "a", "c", "b"], dtype="O")) >>> codes array([0, 0, 1, 2, 0]) >>> uniques array(['b', 'a', 'c'], dtype=object) With ``sort=True``, the `uniques` will be sorted, and `codes` will be shuffled so that the relationship is the maintained. >>> codes, uniques = pd.factorize( ... np.array(["b", "b", "a", "c", "b"], dtype="O"), sort=True ... ) >>> codes array([1, 1, 0, 2, 1]) >>> uniques array(['a', 'b', 'c'], dtype=object) When ``use_na_sentinel=True`` (the default), missing values are indicated in the `codes` with the sentinel value ``-1`` and missing values are not included in `uniques`. >>> codes, uniques = pd.factorize(np.array(["b", None, "a", "c", "b"], dtype="O")) >>> codes array([ 0, -1, 1, 2, 0]) >>> uniques array(['b', 'a', 'c'], dtype=object) Thus far, we've only factorized lists (which are internally coerced to NumPy arrays). When factorizing pandas objects, the type of `uniques` will differ. For Categoricals, a `Categorical` is returned. >>> cat = pd.Categorical(["a", "a", "c"], categories=["a", "b", "c"]) >>> codes, uniques = pd.factorize(cat) >>> codes array([0, 0, 1]) >>> uniques ['a', 'c'] Categories (3, str): ['a', 'b', 'c'] Notice that ``'b'`` is in ``uniques.categories``, despite not being present in ``cat.values``. For all other pandas objects, an Index of the appropriate type is returned. >>> cat = pd.Series(["a", "a", "c"]) >>> codes, uniques = pd.factorize(cat) >>> codes array([0, 0, 1]) >>> uniques Index(['a', 'c'], dtype='str') If NaN is in the values, and we want to include NaN in the uniques of the values, it can be achieved by setting ``use_na_sentinel=False``. >>> values = np.array([1, 2, 1, np.nan]) >>> codes, uniques = pd.factorize(values) # default: use_na_sentinel=True >>> codes array([ 0, 1, 0, -1]) >>> uniques array([1., 2.]) >>> codes, uniques = pd.factorize(values, use_na_sentinel=False) >>> codes array([0, 1, 0, 2]) >>> uniques array([ 1., 2., nan]) )sortrrr)r)rF)compat)rrrT)r assume_uniqueverify)rSr1r4rryr/r5freqrTrZrUrRr^r6rr7whererr safe_sortrh) r_rrrrfrYr null_maskrs rarrsVP&8Y/0T?KK v =FH 6,.?@A KK # ))t)4wg~  +))/)JwF#6<<6#9 V I}}-fll5I)Xv>( + w  G q "  +   BG '>rbclddlm}m}m}m} t |dd} |rdnd} |ddlm} t||r |j} | ||d} | j| }| |_ ||jj}|jjd |_ |j!}|r,|jdk(j#r|j$dd}t'| }nwd}t)|r>||d jj| }| |_ | |j_ n,t|t*rct-t/|j0}||| j3||j5}|j6|j_nt9|d}t;||\}}}|j<t>j@k(r|jt>jB}|||j<| d }|s>* 4 2 are not supported.)r)rRrWndimr rank_1drank_2drq) r_rmethodrrrrrrankss rarankr sD*&,,7O & !F {{a + . L  || +  L@AArbzpandas.api.extensionsc>t|tjttt t fs"tdt|jdt|}|r+t||j|t|||d|}|S|j||}|S)a Take elements from an array. Parameters ---------- arr : numpy.ndarray, ExtensionArray, Index, or Series Input array. indices : sequence of int or one-dimensional np.ndarray of int Indices to be taken. axis : int, default 0 The axis over which to select values. 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 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 multi-dimensional `arr`, each *element* is filled with `fill_value`. Returns ------- ndarray or ExtensionArray Same type as the input. Raises ------ IndexError When `indices` is out of bounds for the array. ValueError When the indexer contains negative values other than ``-1`` and `allow_fill` is True. Notes ----- When `allow_fill` is False, `indices` may be whatever dimensionality is accepted by NumPy for `arr`. When `allow_fill` is True, `indices` should be 1-D. See Also -------- numpy.take : Take elements from an array along an axis. Examples -------- >>> import pandas as pd With the default ``allow_fill=False``, negative numbers indicate positional indices from the right. >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1]) array([10, 10, 30]) Setting ``allow_fill=True`` will place `fill_value` in those positions. >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True) array([10., 10., nan]) >>> pd.api.extensions.take( ... np.array([10, 20, 30]), [0, 0, -1], allow_fill=True, fill_value=-10 ... ) array([ 10, 10, -10]) zkpd.api.extensions.take requires a numpy.ndarray, ExtensionArray, Index, Series, or NumpyExtensionArray got rkT)r allow_fill fill_value)r)rSrTrZr0r1r4r3rqrrrsrr<rr8take)arrindicesrr"r#rs rar$r$Wsb  &)=ST   99=c9K9K8LA O  "'*G#))D/2  !   M'- Mrbc| t|}t|tjr(|jj dvrt |s t|rtj|jj}t |rtj|gntj|}||jk\jr*||jkjr |j}n |j}t |r tt|j|}n't!tt"||}n t%|}|j'|||S)a Find indices where elements should be inserted to maintain order. Find the indices into a sorted array `arr` (a) such that, if the corresponding elements in `value` were inserted before the indices, the order of `arr` would be preserved. Assuming that `arr` is sorted: ====== ================================ `side` returned index `i` satisfies ====== ================================ left ``arr[i-1] < value <= self[i]`` right ``arr[i-1] <= value < self[i]`` ====== ================================ Parameters ---------- arr: np.ndarray, ExtensionArray, Series Input array. If `sorter` is None, then it must be sorted in ascending order, otherwise `sorter` must be an array of indices that sort it. value : array-like or scalar Values to insert into `arr`. 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. iurQ)sidesorter)rrSrTrZrRrr$r%iinforrr9minrmaxrintrrr: searchsorted)r%valuer)r*r+ value_arrrRs rar/r/s`$V, 3 # IINNd "  "25"9 ()3E):BHHeW%  " ' ' )yEII/E.J.J.LIIEOOE e ejj/0ET)U35AE-S1   EV  <rrrrr}r|ctj|s1t|r|js tdt |}t j }|j}t|}|rtj}ntj}t|tr|j}|j}t|t js|t!|d|j"drA|dk7r$tdt%|j"d||||j'|St)t%|j"dd}|jj*dvr*t j,}|j/d }t0}d }nZ|rt j2}nG|j*d vr9|jj4d vrt j6}nt j8}|j:}|d k(r|j=dd }t j|}t j>|j@|} tCdgdz} |dk\r tCd|n tC|d| |<|| tE| <|jj4tFvr$tIjJ|| t |||ntCdgdz} |dk\r tC|dn tCd|| |<tE| } tCdgdz} |dkDr tCd| n tC| d| |<tE| }||| ||| | <|r| j/d} |d k(r | dddf} | S)aQ difference of n between self, analogous to s-s.shift(n) Parameters ---------- arr : ndarray or ExtensionArray n : int number of periods axis : {0, 1} axis to shift on stacklevel : int, default 3 The stacklevel for the lost dtype warning. Returns ------- shifted zperiods must be an integer__rz cannot diff z on axis=zK has no 'diff' method. Convert to a suitable dtype prior to calling 'diff'.FrrPTr()rrrrrQNrM) datetimelikeztimedelta64[ns])&r r$r" ValueErrorr.rTnanrRroperatorxorsubrSr.to_numpyrZhasattrrsrrshiftrqrrr[r object_rr}r|rreshapeemptyrsliceru _diff_specialr diff_2d)r%nrnarRis_boolop is_timedelta orig_ndimout_arr na_indexer _res_indexer res_indexer _lag_indexer lag_indexers radiffrO(s, >>!   9: : F B IIEE"G \\ \\%&lln  c2:: & 3"R[[M, -qy <S 0B0B/C9TF!STTc399Q<( (9%%&'GG  L yy~~hhtn    t  99>>. .JJEJJEIA~kk"a  HHUOEhhsyy.G+"J)*auT1~U1d^Jt!#GE*  yy~~&  c7CFD|Ld }q( /0AvU1d^5q> TL) d }q( 01AU4!_5!T? TL) !#k"2C 4DE ,,01A~!Q$- Nrbct|tjttfs t dd}t|j ts&tj|ddk(r t|}n" |j}|j|}||St%|s t dt'tj(|}|s+t+t-|t+|k(s t/d|Jt1|\}}|t+|}|j3|t'|j5|}|rD|j} |r#|t+| k|t+|k\z} d || <t7| |d } netj8t+|t: } | j=|tj>t+|| j|d } |t'| fS#t tjf$r:|jrt|dt r t#|}n t|}YwxYw)a Sort ``values`` and reorder corresponding ``codes``. ``values`` should be unique if ``codes`` is not None. Safe for use with mixed types (int, str), orders ints before strs. Parameters ---------- values : list-like Sequence; must be unique if ``codes`` is not None. codes : np.ndarray[intp] or None, default None Indices to ``values``. All out of bound indices are treated as "not found" and will be masked with ``-1``. use_na_sentinel : bool, default True If True, the sentinel -1 will be used for NaN values. If False, NaN values will be encoded as non-negative integers and will not drop the NaN from the uniques of the values. assume_unique : bool, default False When True, ``values`` are assumed to be unique, which can speed up the calculation. Ignored when ``codes`` is None. verify : bool, default True Check if codes are out of bound for the values and put out of bound codes equal to ``-1``. If ``verify=False``, it is assumed there are no out of bound codes. Ignored when ``codes`` is None. Returns ------- ordered : AnyArrayLike Sorted ``values`` new_codes : ndarray Reordered ``codes``; returned when ``codes`` is not None. Raises ------ TypeError * If ``values`` is not list-like or if ``codes`` is neither None nor list-like * If ``values`` cannot be sorted ValueError * If ``codes`` is not None and ``values`` contain duplicates. zbOnly np.ndarray, ExtensionArray, and Index objects are allowed to be passed to safe_sort as valuesNFrlrprzMOnly list-like objects or None are allowed to be passed to safe_sort as codesz,values should be unique if codes is not Nonerr#rQwrap)r ) rSrTrZr0r1rqrRr-r rt _sort_mixedargsortr$decimalInvalidOperationrru _sort_tuplesr&rrUrrr5r map_locationslookupr8r?r.putarange) r_rYrrrr*orderedrtorder2r new_codesreverse_indexers rarrs ` frzz+>AIIc Vtj|Dcgc]}t|tc}t}tj|Dcgc] }t |c}t}||z}tj ||}tj ||}|jdj|}|jdj|}|jd} tj||| g} |j| Scc}wcc}w)z3order ints before strings before nulls in 1d arraysrQr) rTr9rSstrrr6rTnonzeror$ concatenate) r_xstr_posnull_posnum_pos str_argsort num_argsortstr_locsnum_locs null_locslocss rarSrSshhF;Fq 1c*F;4HGxx&1&Qa&1>Hh("G**VG_-K**VG_-K #((5H #((5H  "1%I >>8Xy9 :D ;;t <1s D! D&cPddlm}ddlm}||d\}}||d}||S)a Convert array of tuples (1d) to array of arrays (2d). We need to keep the columns separately as they contain different types and nans (can't use `np.sort` as it may fail when str and nan are mixed in a column as types cannot be compared). r) to_arrays)lexsort_indexerNT)orders)"pandas.core.internals.constructionrppandas.core.sortingrq)r_rprqarraysrindexers rarWrW's0=3&$'IFAfT2G '?rbcddlm}t|d}t|d}|j|d\}}t j |j |j }|||jdd}t|tr0t|tr |j|j}n[t|tr |j}t|tr |j}t||g}t|}t|}|j!|j }t j"||S)a Extracts the union from lvals and rvals with respect to duplicates and nans in both arrays. Parameters ---------- lvals: np.ndarray or ExtensionArray left values which is ordered in front. rvals: np.ndarray or ExtensionArray right values ordered after lvals. Returns ------- np.ndarray or ExtensionArray Containing the unsorted union of both arrays. Notes ----- Caller is responsible for ensuring lvals.dtype == rvals.dtype. rrBFrrQr.)rrRrL)rrBralignrTmaximumr_rrSr2appendrr1rr*r:reindexrepeat) lvalsrvalsrBl_countr_count final_count unique_valscombinedrepeatss raunion_with_duplicatesr6s.#E%8G#E%8G}}W};GW**W^^W^^.s-0$Q.288A; DE rbrxrQF) tupleize_cols)rrrKr)rrAr5rrSdictr;rBrrTr|r_rr4rr get_indexerr8rrLr\r^r map_infermap_infer_maskr6r[rJ) r%mapper na_actionrAmsgrBrv new_valuesr_rs @ra map_arrayresh.((;I;kRo F fd # (F!' F &6{abjj9FD)MMO5e+T &)$  FLL..01F,,**3/V^^W5  s8xxzZZUZ +F}}VV,,!!&&tF|7H7H7RSSrb)r_rreturn np.ndarray)r_rrRrrfrrr)rwrbrr)r_rrz)tuple[type[htable.HashTable], np.ndarray])r_rrrb)r_rErrE)r_znp.ndarray | Seriesrr)r_rrr.r)rnpt.NDArray[np.bool_] | None)rr=r_r=rnpt.NDArray[np.bool_])TNNN) r_rrrr int | Nonerr^rrrz'tuple[npt.NDArray[np.intp], np.ndarray])FTN)rrrrrrrz%tuple[np.ndarray, np.ndarray | Index])TFFNT) rrrrrrrrrrB)r_rrrrrrz,tuple[ArrayLike, npt.NDArray[np.int64], int])firstN)r_rr zLiteral['first', 'last', False]rrrr)TN)r_rrrrrrz9tuple[np.ndarray, npt.NDArray[np.bool_]] | ExtensionArray)raverager TFN)r_rrrrrbrrbrrrrrrrznpt.NDArray[np.float64])rFN)r&rrrr"r)leftN) r%rr0z$NumpyValueArrayLike | ExtensionArrayr)zLiteral['left', 'right']r*zNumpySorter | Nonerznpt.NDArray[np.intp] | np.intp)r)rCz&int | float | np.integer | np.floatingrr)NTFT) r_zIndex | ArrayLikerYznpt.NDArray[np.intp] | Nonerrrrrrrz.AnyArrayLike | tuple[AnyArrayLike, np.ndarray])rr)r_rrr)r~ArrayLike | Indexrrrr)r%rrzLiteral['ignore'] | Nonerz#np.ndarray | ExtensionArray | Index)__doc__ __future__rrUr7typingrrrrrrnumpyrT pandas._libsr r rr r pandas._libs.missingrpandas._typingrrrrrrrpandas.util._decoratorsrpandas.util._exceptionsrpandas.core.dtypes.castrrpandas.core.dtypes.commonrrrrrrr r!r"r#r$r%r&r'r(r)pandas.core.dtypes.concatr*pandas.core.dtypes.dtypesr+r,r-r.pandas.core.dtypes.genericr/r0r1r2r3r4r5pandas.core.dtypes.missingr6r7pandas.core.array_algos.taker8pandas.core.constructionr9rr:r;pandas.core.indexersr<r=r>r?rr@rArBpandas.core.arraysrCrDrErWrhryComplex128HashTableComplex64HashTableFloat64HashTableFloat32HashTableUInt64HashTableUInt32HashTableUInt16HashTableUInt8HashTableInt64HashTableInt32HashTableInt16HashTable Int8HashTableStringHashTablePyObjectHashTablerrrrrrunique1drrrrrrr r r r$r/rArOrrSrWrrrrbrars # $/4$4  1 2   5;.?@A K!\!, !,'!,3?!,!,H:,,**&&&&$$$$$$  " "  " "  " "  " "  $$&& $ .(6   : : Hi$i$X.,8 "^"F! )- ; ;;; ; ' ; - ;| H  y yy y + yy|  [ [[ [  [ [@LP( ( $(,H(1(B-4)-; ; ); '; ;<RV+ +#+2N+>+`)-= = = = =  =  = '==J #$ m m m m%mp&,!% Q= Q= /Q= #Q=  Q= $ Q=pJ lp*.  w3 w3 &w3w3 w3  w3 4 w3t  ,+ ,+%6,+,+d+/PT PT(PT) PTrb