MhfSrSSKJr SSKJr SSKJrJrJrJ r J r SSK r SSK r SSK Jr SSKJr SSKJr SS KJrJrJr SS KJrJrJrJrJr SS KJr SS KJ r SS K!J"r"J#r#J$r$J%r%J&r&J'r'J(r( SSK)J*r*J+r+J,r,J-r- SSK.J/r/ SSK0J1r1J2r2J3r3J4r4 SSK5J6r6 SSK7J8s J9r: \(aSSK;JJ?r? S$S%Sjjr@\A"1Sk5rB\ S&S'Sjj5rC\ S&S(Sjj5rCS)S(SjjrCSrDS*SjrES+SS.S,SjjjrFS-SjrGS.SjrHSS.S/S jjrIS0S!jrJS1S"jrKS2S#jrLg)3z Constructor functions intended to be shared by pd.array, Series.__init__, and Index.__new__. These should not depend on core.internals. ) annotations)Sequence) TYPE_CHECKINGOptionalUnioncastoverloadN)ma)using_pyarrow_string_dtype)lib)Periodget_supported_dtypeis_supported_dtype) AnyArrayLike ArrayLikeDtypeDtypeObjT)find_stack_level)ExtensionDtype)"construct_1d_arraylike_from_scalar'construct_1d_object_array_from_listlikemaybe_cast_to_datetimemaybe_cast_to_integer_arraymaybe_convert_platformmaybe_infer_to_datetimelike maybe_promote) is_list_likeis_object_dtypeis_string_dtype pandas_dtype) NumpyEADtype) ABCDataFrameABCExtensionArrayABCIndex ABCSeries)isna)IndexSeries)ExtensionArraycSSKJnJnJnJnJnJnJn Jn J n SSK J n [R"U5(aSUS3n [U 5e[U[ 5(a [#S5eUc([U[$[&U45(a UR(n[+USS 9nUb [-U5n[X5(a,UbUR(U:XaU(aUR/5$U$[U[05(a UR35nUR5XUS 9$UGcp[R6"USS 9nUS :Xa;[9[:[<[>[@[B4U5nU R5UUS 9$US:XaU"XS 9$URES5(aUR5XS 9$URES5(aU R5XS 9$US:Xa'U "5nUR35nUR5XUS 9$US:XaUR5XS 9$US:Xa0[GUS5(d[IU5(dUR5XS 9$US;a.[KUSS5[LRN:waUR5XS 9$US:XaUR5USUS 9$[RP"US5(a [SU5(aUR5XUS 9$[RP"US5(a [SU5(aU R5XUS 9$[RP"US5(a"[TRV"S[X[[5S9 U R5XUS 9$![a Nf=f)aB Create an array. Parameters ---------- data : Sequence of objects The scalars inside `data` should be instances of the scalar type for `dtype`. It's expected that `data` represents a 1-dimensional array of data. When `data` is an Index or Series, the underlying array will be extracted from `data`. dtype : str, np.dtype, or ExtensionDtype, optional The dtype to use for the array. This may be a NumPy dtype or an extension type registered with pandas using :meth:`pandas.api.extensions.register_extension_dtype`. If not specified, there are two possibilities: 1. When `data` is a :class:`Series`, :class:`Index`, or :class:`ExtensionArray`, the `dtype` will be taken from the data. 2. Otherwise, pandas will attempt to infer the `dtype` from the data. Note that when `data` is a NumPy array, ``data.dtype`` is *not* used for inferring the array type. This is because NumPy cannot represent all the types of data that can be held in extension arrays. Currently, pandas will infer an extension dtype for sequences of ============================== ======================================= Scalar Type Array Type ============================== ======================================= :class:`pandas.Interval` :class:`pandas.arrays.IntervalArray` :class:`pandas.Period` :class:`pandas.arrays.PeriodArray` :class:`datetime.datetime` :class:`pandas.arrays.DatetimeArray` :class:`datetime.timedelta` :class:`pandas.arrays.TimedeltaArray` :class:`int` :class:`pandas.arrays.IntegerArray` :class:`float` :class:`pandas.arrays.FloatingArray` :class:`str` :class:`pandas.arrays.StringArray` or :class:`pandas.arrays.ArrowStringArray` :class:`bool` :class:`pandas.arrays.BooleanArray` ============================== ======================================= The ExtensionArray created when the scalar type is :class:`str` is determined by ``pd.options.mode.string_storage`` if the dtype is not explicitly given. For all other cases, NumPy's usual inference rules will be used. copy : bool, default True Whether to copy the data, even if not necessary. Depending on the type of `data`, creating the new array may require copying data, even if ``copy=False``. Returns ------- ExtensionArray The newly created array. Raises ------ ValueError When `data` is not 1-dimensional. See Also -------- numpy.array : Construct a NumPy array. Series : Construct a pandas Series. Index : Construct a pandas Index. arrays.NumpyExtensionArray : ExtensionArray wrapping a NumPy array. Series.array : Extract the array stored within a Series. Notes ----- Omitting the `dtype` argument means pandas will attempt to infer the best array type from the values in the data. As new array types are added by pandas and 3rd party libraries, the "best" array type may change. We recommend specifying `dtype` to ensure that 1. the correct array type for the data is returned 2. the returned array type doesn't change as new extension types are added by pandas and third-party libraries Additionally, if the underlying memory representation of the returned array matters, we recommend specifying the `dtype` as a concrete object rather than a string alias or allowing it to be inferred. For example, a future version of pandas or a 3rd-party library may include a dedicated ExtensionArray for string data. In this event, the following would no longer return a :class:`arrays.NumpyExtensionArray` backed by a NumPy array. >>> pd.array(['a', 'b'], dtype=str) ['a', 'b'] Length: 2, dtype: str32 This would instead return the new ExtensionArray dedicated for string data. If you really need the new array to be backed by a NumPy array, specify that in the dtype. >>> pd.array(['a', 'b'], dtype=np.dtype(" ['a', 'b'] Length: 2, dtype: str32 Finally, Pandas has arrays that mostly overlap with NumPy * :class:`arrays.DatetimeArray` * :class:`arrays.TimedeltaArray` When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray`` rather than a ``NumpyExtensionArray``. This is for symmetry with the case of timezone-aware data, which NumPy does not natively support. >>> pd.array(['2015', '2016'], dtype='datetime64[ns]') ['2015-01-01 00:00:00', '2016-01-01 00:00:00'] Length: 2, dtype: datetime64[ns] >>> pd.array(["1h", "2h"], dtype='timedelta64[ns]') ['0 days 01:00:00', '0 days 02:00:00'] Length: 2, dtype: timedelta64[ns] Examples -------- If a dtype is not specified, pandas will infer the best dtype from the values. See the description of `dtype` for the types pandas infers for. >>> pd.array([1, 2]) [1, 2] Length: 2, dtype: Int64 >>> pd.array([1, 2, np.nan]) [1, 2, ] Length: 3, dtype: Int64 >>> pd.array([1.1, 2.2]) [1.1, 2.2] Length: 2, dtype: Float64 >>> pd.array(["a", None, "c"]) ['a', , 'c'] Length: 3, dtype: string >>> with pd.option_context("string_storage", "pyarrow"): ... arr = pd.array(["a", None, "c"]) ... >>> arr ['a', , 'c'] Length: 3, dtype: string >>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")]) ['2000-01-01', '2000-01-01'] Length: 2, dtype: period[D] You can use the string alias for `dtype` >>> pd.array(['a', 'b', 'a'], dtype='category') ['a', 'b', 'a'] Categories (2, object): ['a', 'b'] Or specify the actual dtype >>> pd.array(['a', 'b', 'a'], ... dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True)) ['a', 'b', 'a'] Categories (3, object): ['a' < 'b' < 'c'] If pandas does not infer a dedicated extension type a :class:`arrays.NumpyExtensionArray` is returned. >>> pd.array([1 + 1j, 3 + 2j]) [(1+1j), (3+2j)] Length: 2, dtype: complex128 As mentioned in the "Notes" section, new extension types may be added in the future (by pandas or 3rd party libraries), causing the return value to no longer be a :class:`arrays.NumpyExtensionArray`. Specify the `dtype` as a NumPy dtype if you need to ensure there's no future change in behavior. >>> pd.array([1, 2], dtype=np.dtype("int32")) [1, 2] Length: 2, dtype: int32 `data` must be 1-dimensional. A ValueError is raised when the input has the wrong dimensionality. >>> pd.array(1) Traceback (most recent call last): ... ValueError: Cannot pass scalar '1' to 'pandas.array'. r) BooleanArray DatetimeArrayr* FloatingArray IntegerArray IntervalArrayNumpyExtensionArray PeriodArrayTimedeltaArray StringDtypezCannot pass scalar 'z' to 'pandas.array'.z'Cannot pass DataFrame to 'pandas.array'NT) extract_numpydtypecopy)skipnaperiodr9intervaldatetime timedeltastringintegeremptyr8)floatingzmixed-integer-floatbooleanMmmMzdatetime64 and timedelta64 dtype resolutions other than 's', 'ms', 'us', and 'ns' are deprecated. In future releases passing unsupported resolutions will raise an exception.) stacklevel).pandas.core.arraysr,r-r*r.r/r0r1r2r3pandas.core.arrays.string_r5r is_scalar ValueError isinstancer# TypeErrorr&r%r8 extract_arrayr!r9rconstruct_array_type_from_sequence infer_dtyperrrrr r startswithhasattrlengetattrnpfloat16 is_np_dtyperwarningswarn FutureWarningr)datar8r9r,r-r*r.r/r0r1r2r3r5msgclsinferred_dtype period_datas J/var/www/html/env/lib/python3.13/site-packages/pandas/core/construction.pyarrayrcJsBd   7 }}T$TF*>?o D, ' 'ABB }D9h*OPP  T 2D U#$''U]djjE>Q 99;  %((((*!!$$!?? }d; X %uXhv.>%?%MNPTUK--k-E E z ) 1 1  & &z 2 2 $33D3DD  & &{ 3 3!000A A x 'ME,,.C%%dd%C C y (..t.? ? w &wtW/E/EcRVii ///@ @ A Agt, :!///@ @ y (..t94.P P  uc""'9%'@'@++DD+II uc""'9%'@'@,,TT,JJ  % %  # ')    - -dd - KKa  sM M$#M$> indexseries multiindex rangeindex periodindex datetimeindex intervalindextimedeltaindexcategoricalindexcgNobjr6 extract_ranges rbrOrOcgrnrorps rbrOrOrsrtFc[USS5nU[;a'US:XaU(a UR$U$UR$U(aUS:XaUR5$U$)a Extract the ndarray or ExtensionArray from a Series or Index. For all other types, `obj` is just returned as is. Parameters ---------- obj : object For Series / Index, the underlying ExtensionArray is unboxed. extract_numpy : bool, default False Whether to extract the ndarray from a NumpyExtensionArray. extract_range : bool, default False If we have a RangeIndex, return range._values if True (which is a materialized integer ndarray), otherwise return unchanged. Returns ------- arr : object Examples -------- >>> extract_array(pd.Series(['a', 'b', 'c'], dtype='category')) ['a', 'b', 'c'] Categories (3, object): ['a', 'b', 'c'] Other objects like lists, arrays, and DataFrames are just passed through. >>> extract_array([1, 2, 3]) [1, 2, 3] For an ndarray-backed Series / Index the ndarray is returned. >>> extract_array(pd.Series([1, 2, 3])) array([1, 2, 3]) To extract all the way down to the ndarray, pass ``extract_numpy=True``. >>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True) array([1, 2, 3]) _typNrg npy_extension)rV_typs_valuesto_numpy)rqr6rrtyps rbrOrOs]Z #vt $C e| , {{"J{{ 3/1||~ JrtcT[U[R5(aURRS:Xa*SSKJn [UR5nURXS9$URRS:Xa*SSKJ n [UR5nURXS9$U$)zK Wrap datetime64 and timedelta64 ndarrays in DatetimeArray/TimedeltaArray. rEr)r-r8rF)r3) rMrWndarrayr8kindrIr-rrQr3)arrr-r8r3s rbensure_wrapped_if_datetimelikers#rzz"" 99>>S 8' 2E ///A A YY^^s " 9' 2E!000B B Jrtcp[R"U5nUR5(az[UR[ R 5up#[[ RU5n[R"URUSS95nUR5 X0U'U$UR5nU$)z7 Convert numpy MaskedArray to ensure mask is softened. Tr<) r getmaskarrayanyrr8rWnanrasarrayastype soften_maskr9)r]maskr8 fill_values rbsanitize_masked_arrayrs ??4 D xxzz)$**bff=RXXu%zz$++e$+78 T  Kyy{ Krtallow_2dcUn[U[R5(a [U5n[U[5(a UR nSn[U[ 5(aUR[:XaUcSn[USSS9n[U[R5(a6URS:Xa&Uc URn[R"U5nO"[U[5(a [!U5nSn[#U5(d[Uc [%S5e[U[&5(a [)5(aUcSSKJn U"S5n[/U[1U5U5nU$[U[25(a1UbUR5X#S9nGOaU(aUR75nGOHUnGOD[U[85(a-[;U5 UR=5n U R?XUS 9nGO[U[R5(a[U[R@5(a URBnUcUnUR[:Xa4[EU5nU(a![)5(a[GU5(aUnORURRHS :Xa8[)5(a)SSKJn U"SS 9nUR=5R?XS 9nXLaU(aUR75nO[KXU5nO[MUS 5(a?U(d[RN"U5nO[RP"XS9n[SUUUSUS9$[;U5 [UU5n[1U5S:Xa'Uc$[RP"/[RVS 9nOTUb [KXU5nOD[YU5nUR[:Xa%[[[RU5n[EU5n[]XX!US9n[U[R5(a&[[[RU5n[_XX#5nU$)a Sanitize input data to an ndarray or ExtensionArray, copy if specified, coerce to the dtype if specified. Parameters ---------- data : Any index : Index or None, default None dtype : np.dtype, ExtensionDtype, or None, default None copy : bool, default False allow_2d : bool, default False If False, raise if we have a 2D Arraylike. Returns ------- np.ndarray or ExtensionArray FT)r6rrrz2index must be specified when data is not list-liker4 pyarrow_numpyr<r7U)storager~ __array__)rdr8r9rr)0rMr MaskedArrayrr" numpy_dtyper%r8objectrOrWrndimr item_from_zerodimrangerange_to_ndarrayrrLstrr rJr5rrUr$rr9r_sanitize_non_orderedrPrQmatrixArr r _try_castrTrrcsanitize_arraylistfloat64rr_sanitize_ndim_sanitize_str_dtypes) r]rdr8r9roriginal_dtype object_indexr5subarrr_s rbrrse2N$''$T*%&&!!L$!!djjF&:u}  T FD$ ## Q =JJE$$T* D% %    =QR R tS ! !*,,& >0E1$E EJ D+ , ,  [[[2F YY[FF E> * *d#((*##DD#A D"** % % dBII & &66D =FzzV#4T: 244'//"FC',F,H,HB#O<335DDTDW~$tD1F { # #::d#D88D,D     d#Dz t9>emXXb 3F  tD1F,D1F||v%bjj&14V< F% JF&"**%%RXXu%%fE@ Mrtc[R"URURURSS9nU$![ a URS:aURS:d$URSs=:aUR::a^O O[[R"URURURSS9nO8![ a [ [U55nOf=f[ [U55nU$f=f)z! Cast a range object to ndarray. int64r~ruint64)rWarangestartstopstep OverflowErrorrr)rngrs rbrrs Eii 388SXXWE J E IINsxx!|A1I1I Iii 388SXXXN  I=d3iH I:$s)DC JEs459AC:5B>=C:>CC:CC:9C:c~[U[[45(a"[S[ U5R S35eg)z8 Raise only for unordered sets, e.g., not for dict_keys 'z' type is unorderedN)rMset frozensetrNtype__name__)r]s rbrrs;$i())!DJ//00CDEE*rtc [USS5S:Xa [S5eURS:Xa [X5nU$URS:a[ U[ R 5(a"U(aU$[SURS35e[U5(a_[ U[5(aJ[R"U[ R"S5S9nUR5nURXS9nU$[R"XS9nU$) z. Ensure we have a 1-dimensional result array. rrz(result should be arraylike with ndim > 0z1Data must be 1-dimensional, got ndarray of shape z insteadrr~)rVrLr _maybe_repeatrMrWrshaperrcomasarray_tuplesafer8rPrQ)resultr]r8rdrr_s rbrrsvvq!Q&CDD {{av-( M% q dBJJ ' ' CDJJrsc #$5 52 3 , 7 vL )vL vL vL vLr    JM (,CG   =@ 6: @E> >>8<>>B( &" U  U U U  U  UUp&F $ $ $  $  $$N %4<@.7 7 7 7 7rt