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Arguments: native_namespace: Native namespace. )r)rPANDASr(MODINr$CUDFr+PYARROWr-PYSPARKr*POLARSr%DASKr&DUCKDBr'IBISr.SQLFRAMEr,PYSPARK_CONNECTgetUNKNOWN)clsnative_namespacemappings rfrom_native_namespacez$Implementation.from_native_namespacehs L.// K-- J++ M>11  ~55 L.//  ."5"5 L.// J++ NN33  !>#A#A  {{+^-C-CDDrcR ||S#t$rtjcYSwxYw)zInstantiate Implementation object from a native namespace module. Arguments: backend_name: Name of backend, expressed as string. ) ValueErrorrr)r backend_names r from_stringzImplementation.from_strings- *|$ $ *!)) ) *s  &&ct|tr|j|St|tr|S|j |S)zInstantiate from native namespace module, string, or Implementation. 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Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"a": [1, 2, 3]}) >>> df = nw.from_native(df_native) >>> df.implementation.is_cudf() False )rrrs ris_cudfzImplementation.is_cudf~****rc&|tjuS)a6Return whether implementation is Modin. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"a": [1, 2, 3]}) >>> df = nw.from_native(df_native) >>> df.implementation.is_modin() False )rrrs ris_modinzImplementation.is_modins~++++rc&|tjuS)a:Return whether implementation is PySpark. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"a": [1, 2, 3]}) >>> df = nw.from_native(df_native) >>> df.implementation.is_pyspark() False )rrrs r is_pysparkzImplementation.is_pyspark~----rc&|tjuS)aBReturn whether implementation is PySpark. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"a": [1, 2, 3]}) >>> df = nw.from_native(df_native) >>> df.implementation.is_pyspark_connect() False )rrrs ris_pyspark_connectz!Implementation.is_pyspark_connects~5555rc&|tjuS)a:Return whether implementation is PyArrow. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"a": [1, 2, 3]}) >>> df = nw.from_native(df_native) >>> df.implementation.is_pyarrow() False )rrrs r is_pyarrowzImplementation.is_pyarrowr=rc&|tjuS)a4Return whether implementation is Dask. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"a": [1, 2, 3]}) >>> df = nw.from_native(df_native) >>> df.implementation.is_dask() False )rrrs ris_daskzImplementation.is_dask r8rc&|tjuS)a8Return whether implementation is DuckDB. 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Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"a": [1, 2, 3]}) >>> df = nw.from_native(df_native) >>> df.implementation.is_sqlframe() False )rrrs r is_sqlframezImplementation.is_sqlframeGs~....rct|S)zReturns backend version.backend_versionrs rrzImplementation._backend_versionTs t$$rNrr)r type[Self]rr<rr)rrNr rrr)rrNr$z)IntoBackend[Backend] | UnknownBackendNamerr)rr<)rboolr)$rrrrr rrrrrrrrrrrr  classmethodrr!r%r,r.r1r3r5r7r:r<r?rArCrErGrIrrrrrrIs'I F E DG!G! 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CIN|DG/DQ PP4 PPrc8t|ttfs|fS|Sr)r#rntuple)args rtupleifyrvs cD%= )v Jrczddlm}tx}"t||j|jfsDt x}Rt||j|j |j|jfrdt|d}t|t|txrt|tt|f S)Nrrrz(Expected Narwhals class or scalar, got: z`. Hint: Perhaps you - forgot a `nw.from_native` somewhere? - used `pl.col` instead of `nw.col`?) rrsr)r#rnr*Exprroqualified_type_name TypeErrorrrbytes)rurspdplr*s rrprps&| (Zbii=V-W| ( sRYYr||L M77J37O6RS3 3 n c8 $ RZc5&=Q-R)RRrc"t|tSr)r#r)vals r is_iteratorrs c8 $$rct|tr|n |j}tjdd|}t d|j dDS)zSimple version parser; split into a tuple of ints for comparison. 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Arguments: lhs: Dataframe or Series. rhs: Dataframe or Series to align with. Notes: This is only really intended for backwards-compatibility purposes, for example if your library already aligns indices for users. If you're designing a new library, we highly encourage you to not rely on the Index. For non-pandas-like inputs, this only checks that `lhs` and `rhs` are the same length. Examples: >>> import pandas as pd >>> import polars as pl >>> import narwhals as nw >>> df_pd = pd.DataFrame({"a": [1, 2]}, index=[3, 4]) >>> s_pd = pd.Series([6, 7], index=[4, 3]) >>> df = nw.from_native(df_pd) >>> s = nw.from_native(s_pd, series_only=True) >>> nw.to_native(nw.maybe_align_index(df, s)) a 4 2 3 1 r)PandasLikeDataFrame)PandasLikeSeriesrc6|js d}t|y)Nz'given index doesn't have a unique index) is_uniquer)indexr*s r_validate_indexz*maybe_align_index.._validate_index1s;CS/ !r_compliant_frameN_compliant_seriesz6Expected `lhs` and `rhs` to have the same length, got z and )rrrr)narwhals._pandas_like.dataframernarwhals._pandas_like.seriesrrr#getattrrrr_with_compliant _with_nativelocrror)lhsrhsrrrlhs_anyrhs_anyr*s rmaybe_align_indexrsI<D=" 5#G5#G+T24G WW&8$?AT U0077==>0077==>&&  $ $ 1 1((//33G4L4L4S4S4Y4YZ   +T24G WW&94@BR S0077==>1188>>?&&  $ $ 1 1((//33--44::   ,d35E WW&8$?AT U1188>>?0077==>&&  % % 2 2))0044,,3399   ,d35E WW&94@BR S1188>>?1188>>?&&  % % 2 2))0044--44::    7|s7|#Fs7|nTYZ]^eZfYgho Jrctd|}|j}t|s t|r |jSy)aGet the index of a DataFrame or a Series, if it's pandas-like. Arguments: obj: Dataframe or Series. Notes: This is only really intended for backwards-compatibility purposes, for example if your library already aligns indices for users. If you're designing a new library, we highly encourage you to not rely on the Index. For non-pandas-like inputs, this returns `None`. Examples: >>> import pandas as pd >>> import polars as pl >>> import narwhals as nw >>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]}) >>> df = nw.from_native(df_pd) >>> nw.maybe_get_index(df) RangeIndex(start=0, stop=2, step=1) >>> series_pd = pd.Series([1, 2]) >>> series = nw.from_native(series_pd, series_only=True) >>> nw.maybe_get_index(series) RangeIndex(start=0, stop=2, step=1) rN)r to_nativer5r6r)objobj_any native_objs rmaybe_get_indexrls=45#G""$J +/DZ/P r)rcVddlm}td|}|j}|| d}t||s| d}t||.t |r|Dcgc] }||dc}n ||d}n|}t |r9|j |jj|j|St|r^ddl m } |r d }t|| |||jj }|j |jj|S|Scc}w) aSet the index of a DataFrame or a Series, if it's pandas-like. Arguments: obj: object for which maybe set the index (can be either a Narwhals `DataFrame` or `Series`). column_names: name or list of names of the columns to set as index. For dataframes, only one of `column_names` and `index` can be specified but not both. If `column_names` is passed and `df` is a Series, then a `ValueError` is raised. index: series or list of series to set as index. Raises: ValueError: If one of the following conditions happens - none of `column_names` and `index` are provided - both `column_names` and `index` are provided - `column_names` is provided and `df` is a Series Notes: This is only really intended for backwards-compatibility purposes, for example if your library already aligns indices for users. If you're designing a new library, we highly encourage you to not rely on the Index. For non-pandas-like inputs, this is a no-op. Examples: >>> import pandas as pd >>> import polars as pl >>> import narwhals as nw >>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]}) >>> df = nw.from_native(df_pd) >>> nw.to_native(nw.maybe_set_index(df, "b")) # doctest: +NORMALIZE_WHITESPACE a b 4 1 5 2 r)rrz8Only one of `column_names` or `index` should be providedz3Either `column_names` or `index` should be providedT) pass_through) set_indexz/Cannot set index using column names on a SeriesrS)narwhals.translaterrrrpr5rrrrr6narwhals._pandas_like.utilsrr) r column_namesrrdf_anyrr*idxkeysrs rmaybe_set_indexrs7X- % F!!#JE$5Ho EMCo E";@ @3Ys . @5t4  +%%  # # 0 01E1Ed1K L  Z(9 CCS/ !  00@@ %%f&>&>&K&KJ&WXX M1 AsD&ctd|}|j}t|rX|j}t ||r|S|j |j j|jdSt|rX|j}t ||r|S|j |jj|jdS|S)aReset the index to the default integer index of a DataFrame or a Series, if it's pandas-like. Arguments: obj: Dataframe or Series. Notes: This is only really intended for backwards-compatibility purposes, for example if your library already resets the index for users. If you're designing a new library, we highly encourage you to not rely on the Index. For non-pandas-like inputs, this is a no-op. Examples: >>> import pandas as pd >>> import polars as pl >>> import narwhals as nw >>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]}, index=([6, 7])) >>> df = nw.from_native(df_pd) >>> nw.to_native(nw.maybe_reset_index(df)) a b 0 1 4 1 2 5 >>> series_pd = pd.Series([1, 2]) >>> series = nw.from_native(series_pd, series_only=True) >>> nw.maybe_get_index(series) RangeIndex(start=0, stop=2, step=1) rT)drop) rrr5__native_namespace___has_default_indexrrr reset_indexr6r)rrrrs rmaybe_reset_indexrs85#G""$J +"779 j*: ;N&&  $ $ 1 1*2H2Hd2H2S T  Z("779 j*: ;N&&  % % 2 2:3I3It3I3T U   Nrc.t||jSr)r# RangeIndex)rrs r_is_range_indexrs c+66 77rc|j}t||xr:|jdk(xr)|jt |k(xr|j dk(S)NrrV)rrstartstoprostep)native_frame_or_seriesrrs rrrsW # ( (E/0  KK1   JJ#e* $  JJ!O rc |jjs|S|j|jj |j j |i|}td|S)a-Convert columns or series to the best possible dtypes using dtypes supporting ``pd.NA``, if df is pandas-like. Arguments: obj: DataFrame or Series. *args: Additional arguments which gets passed through. **kwargs: Additional arguments which gets passed through. Notes: For non-pandas-like inputs, this is a no-op. Also, `args` and `kwargs` just get passed down to the underlying library as-is. Examples: >>> import pandas as pd >>> import polars as pl >>> import narwhals as nw >>> import numpy as np >>> df_pd = pd.DataFrame( ... { ... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), ... "b": pd.Series([True, False, np.nan], dtype=np.dtype("O")), ... } ... ) >>> df = nw.from_native(df_pd) >>> nw.to_native( ... nw.maybe_convert_dtypes(df) ... ).dtypes # doctest: +NORMALIZE_WHITESPACE a Int32 b boolean dtype: object r)rTr1r _compliantrrconvert_dtypesr)rrqkwargsresults rmaybe_convert_dtypesr"sgB    , , .   ##$BCMMO$B$BD$SF$STF  & ))rcv|dvr|S|dvr|dz S|dvr|dz S|dvr|dz S|dvr|d z Sd |}t|) zScale size in bytes to other size units (eg: "kb", "mb", "gb", "tb"). Arguments: sz: original size in bytes unit: size unit to convert into >br{>kb kilobytesi>mb megabytesi>gb gigabytesi@>tb terabyteslz9`unit` must be one of {'b', 'kb', 'mb', 'gb', 'tb'}, got r)szunitr*s r scale_bytesrKss ~  ""Dy ""G| ""G| ""G| Gx PC S/rcddlm}|jjj}|j}d}t ||r;t |j |jr|jjd}|S|j |jk(rd}|S|j |jk7rd}|S|j}|j}|jr8|jdkr%td|j j dk(}|S|j#r!t%|j&j(}|S|j+r0dd lm}||j0xr|j0j(}|S) aReturn whether indices of categories are semantically meaningful. This is a convenience function to accessing what would otherwise be the `is_ordered` property from the DataFrame Interchange Protocol, see https://data-apis.org/dataframe-protocol/latest/API.html. - For Polars: - Enums are always ordered. - Categoricals are ordered if `dtype.ordering == "physical"`. - For pandas-like APIs: - Categoricals are ordered if `dtype.cat.ordered == True`. - For PyArrow table: - Categoricals are ordered if `dtype.type.ordered == True`. Arguments: series: Input Series. Examples: >>> import narwhals as nw >>> import pandas as pd >>> import polars as pl >>> data = ["x", "y"] >>> >>> s_pd = nw.from_native( ... pd.Series(data, dtype=pd.CategoricalDtype(ordered=True)), series_only=True ... ) >>> nw.is_ordered_categorical(s_pd) True >>> s_pl = nw.from_native( ... pl.Series(data, dtype=pl.Categorical()), series_only=True ... ) >>> nw.is_ordered_categorical(s_pl) False r)InterchangeSeriesF is_orderedT)rV zpl.Categoricalphysical) is_dictionary)narwhals._interchange.seriesrrrrr#dtype Categoricalrdescribe_categoricalr rrTr5rrorderingr1rOcatorderedrAnarwhals._arrow.utilsrr)rrrrrrrirs ris_ordered_categoricalr`saF?  % % . . 5 5F((IF)./J f((5!!66|D& M%  $" M! ++ + M!!#$$ >>  5 5 7' A*FLL9BBjPF M  "&**,,-F M __  ;"6;;/GFKK4G4GF Mrc<d}t|dt|||S)Nz}Use `generate_temporary_column_name` instead. `generate_unique_token` is deprecated and it will be removed in future versionsz1.13.0r)n_bytesrprefix)r!generate_temporary_column_name)rrrr*s rgenerate_unique_tokenrs( ?cH5 )'7SY ZZrctd} |t|dz x}|vr|S|dz }|dkDrd|d|}t|6)aGenerates a unique column name that is not present in the given list of columns. 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J   99B J ' .  % rrc|j|j|jnd}|j|j|jdznd}|j}|||fS)NrV)rrrr) str_slicerrrrs rconvert_str_slice_to_int_slicerwsZ/8oo.IGMM)// *tE090J7== (1 ,PTD >>D 4 rcdfd }|S)zSteal the class-level docstring from parent and attach to child `__init__`. Returns: Decorated constructor. Notes: - Passes static typing (mostly) - Passes at runtime c|jdk(r+tttrt|_|Sdt jd|j d}t|)Nrz`@zL` is only allowed to decorate an `__init__` with a class-level doc. Method: z Parent: )rrrrr inherit_docrrz) init_childr* tp_parents rrKzinherit_doc..decoratesp   * ,DOT1R!' !2J  %%&'!..12 m % nr)r_Constructor[_T, P, R2]rrr)rrKs` rrrs  Orct|tr|n t|}|jdk7r |jnd}|d|jj dS)Nbuiltinsrr)r#rrrlstrip)rr,modules rryrysL3%49B mmz9R]]rFXQr{{m $ + +C 00rrPc:t||sdjd|D}d|dt|}|rYd}t|}t |dkDrt|d}||d}d t |zd t |zz}|d ||d |}t |y ) aValidate that an object is an instance of one or more specified types. 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Provides access to a compliant object: obj: Narwhals[NativeT_co]] compliant: Compliant[NativeT_co] = obj._compliant Which itself exposes: implementation: Implementation = compliant.implementation native: NativeT_co = compliant.native This interface is used for revealing which `Implementation` member is associated with **either**: - One or more [nominal] native type(s) - One or more [structural] type(s) - where the true native type(s) are [assignable to] *at least* one of them These relationships are defined in the `@overload`s of `_Implementation.__get__(...)`. [nominal]: https://typing.python.org/en/latest/spec/glossary.html#term-nominal [structural]: https://typing.python.org/en/latest/spec/glossary.html#term-structural [assignable to]: https://typing.python.org/en/latest/spec/glossary.html#term-assignable cyrrrs rrzNarwhals._compliants36rN)rzCompliant[NativeT_co])rrrrrrrrrr<r<s066rr<cjeZdZdZddZeddZeddZeddZeddZe ddZeddZe dd Zedd Ze dd Zedd Zedd Ze d dZed!dZe d"dZed#dZd$dZy)%_ImplementationzDescriptor for matching an opaque `Implementation` on a generic class. 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