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FJ. 59 .......[.FJ......[.59......[.111X1777X7ZZZXZ X   X  ) ) ) ) ) )((((((48J# JJJJJJJJ47WWWWWXW PPPXP #'<<<<<X<$(44444444333333//////////rrcxeZdZejZeedfd Ze dd Z ddfd Z xZ S)rrwrrxryrzr{cx|jtjusJt||dSr}r~rs rrzLazyFrame.__init__rrtype[DataFrame[Any]]ctSrrrs r _dataframezLazyFrame._dataframerNr+IntoBackend[Polars | Pandas | Arrow] | Nonekwargsrc Tttjdd|i|S)Nrr)rrcollect)rrrrs rrzLazyFrame.collects./%''/DD'DVDDEEErrrzrr)rrrrrzr) rrrrrrr NwLazyFramerrrrrrs@rrrszH[******XFJFFFFFFFFFFFrrceZdZUejZeed&fd Ze d'd Z e d(d)fd Z e d(d*fd Z d+fd Zddd ddd,fd ZeZded!<dd"d-fd%ZxZS).rCseriesrrxryrzr{cx|jtjusJt||dSr}r~)rrrxrs rrzSeries.__init__s='*,,,, u-----rrctSrrrs rrzSeries._dataframerrNrrvaluesrpdtypeIntoDType | Nonerrrcnt||||}td|SNrrrrrrrrrrs rrzSeries.from_numpys5##D&%#IIM6***r Iterable[Any]cnt||||}td|Sr)r from_iterabler rs rrzSeries.from_iterable"s5&&tVUG&LLM6***rrc^ttSr)rrto_framers rrzSeries.to_frame.s!%''**,,---rFsortparallelr normalizerrr str | Nonerchtt||||S)Nr)rr value_counts)rrrrrrs rrzSeries.value_counts1s< GG H49 !     rhist ignore_nullsrrmcvd}t|tt|S)Nz_`Series.any_value` is being called from the stable API although considered an unstable feature.r)rr=r any_value)rrmsgrs rr zSeries.any_valueBs; #  c2333ww  l ;;;r)rrrxryrzr{rr) rrrrprrrrrzr) rrrrrrrrrzr)rzr) rrrrrrrrrzr)rrrzrm)rrrrrrrNwSeriesrrrrrrrrrr__annotations__r rrs@rrCrCs}zH[......X #' + + + + + +[ + #' + + + + + +[ +......           !!D!!!!05<<<<<<<<<<<FrrrrzrWcd}t|t|ttjd|S)Nz]`Expr.any_value` is being called from the stable API although considered an unstable feature.r r)rr= _append_noderr AGGREGATION)rrr s rr zExpr.any_valueLsL #  c2333  X);\ R R R   rc\|ttjdS)zGet the first value.firstrrrORDERABLE_AGGREGATIONrs rrz Expr.firstVs#  (*H'!R!RSSSrc\|ttjdS)zGet the last value.lastrrs rrz Expr.lastZs#  (*H&!Q!QRRRrN)rrrzrW)rzrW)rrrr rrrrrr>r>Ksg05      TTTTSSSSSSrr>ceZdZejZdS)rBN)rrrrrrrrrrBrB_szHHHrrBobjNwDataFrame[IntoDataFrameT]rzDataFrame[IntoDataFrameT]cdSrrrs rrrcORsrNwLazyFrame[IntoLazyFrameT]LazyFrame[IntoLazyFrameT]cdSrrrs rrrerrNwSeries[IntoSeriesT]Series[IntoSeriesT]cdSrrrs rrrgCF3rNwExprcdSrrrs rrris%(SrZNwDataFrame[IntoDataFrameT] | NwLazyFrame[IntoLazyFrameT] | NwSeries[IntoSeriesT] | NwExprRDataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT] | Exprc8t|tr8t|jt j|jSt|tr8t|jt j|jSt|tr8t|j t j|jSt|trt|jSt!|dSr}) isinstancerr_compliant_frame _with_versionrr_levelrrr rC_compliant_seriesr&r>_nodesrrs rrrms #{##[-;;GJGGszZZZZ#{##[-;;GJGGszZZZZ#x  Yc+99'*EESZXXXX#v!SZ  r native_objectrNkwdsUnpack[OnlySeries]c dSrrr1r2s r from_nativer6~sPSPSrUnpack[AllowSeries]c dSrrr5s rr6r6sQTQTrrGUnpack[ExcludeSeries]c dSrrr5s rr6r6s rrMUnpack[AllowLazy]c dSrrr5s rr6r6sUXUXrrHc dSrrr5s rr6r6 !$rrLc dSrrr5s rr6r6 #rc dSrrr5s rr6r6r@rrJc dSrrr5s rr6r6r>rIntoDataFrameT | IntoSeriesT/DataFrame[IntoDataFrameT] | Series[IntoSeriesT]c dSrrr5s rr6r6s 7:cr-IntoDataFrameT | IntoLazyFrameT | IntoSeriesTUnpack[AllowAny]KDataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT]c dSrrr5s rr6r6s SVRUrUnpack[PassThroughUnknown]c dSrrr5s rr6r6sLOCrr pass_throughr eager_only series_only allow_series bool | NonecdSrrr1rLrMrNrOs rr6r6s  #rFrLrMrNrOJIntoLazyFrameT | IntoDataFrameT | IntoSeriesT | IntoFrame | IntoSeries | TOLazyFrame[IntoLazyFrameT] | DataFrame[IntoDataFrameT] | Series[IntoSeriesT] | Tc t|ttfr|s|St|tr|s|r|St |||||dt jS)aConvert `native_object` to Narwhals Dataframe, Lazyframe, or Series. Arguments: native_object: Raw object from user. Depending on the other arguments, input object can be - a Dataframe / Lazyframe / Series supported by Narwhals (pandas, Polars, PyArrow, ...) - an object which implements `__narwhals_dataframe__`, `__narwhals_lazyframe__`, or `__narwhals_series__` pass_through: Determine what happens if the object can't be converted to Narwhals - `False` (default): raise an error - `True`: pass object through as-is eager_only: Whether to only allow eager objects - `False` (default): don't require `native_object` to be eager - `True`: only convert to Narwhals if `native_object` is eager series_only: Whether to only allow Series - `False` (default): don't require `native_object` to be a Series - `True`: only convert to Narwhals if `native_object` is a Series allow_series: Whether to allow Series (default is only Dataframe / Lazyframe) - `False` or `None` (default): don't convert to Narwhals if `native_object` is a Series - `True`: allow `native_object` to be a Series Returns: DataFrame, LazyFrame, Series, or original object, depending on which combination of parameters was passed. F)rLrMrNrOeager_or_interchange_onlyversion)r+rrrCrOrrrRs rr6r6s}X-)Y!788-((k\ !!"'    r.rLnarwhals_objectrcdSrrrZrLs r to_nativer] SrcdSrrr\s rr]r]r^rcdSrrr\s rr]r]s #rcdSrrr\s rr]r]r%r3IntoDataFrameT | IntoLazyFrameT | IntoSeriesT | Anyc.tj||S)a]Convert Narwhals object to native one. Arguments: narwhals_object: Narwhals object. pass_through: Determine what happens if `narwhals_object` isn't a Narwhals class - `False` (default): raise an error - `True`: pass object through as-is Returns: Object of class that user started with. rY)nwr]r\s rr]r]s& <l C C CCrTfuncCallable[..., Any] | NoneCallable[..., Any]c:dfd }||S||S)aDecorate function so it becomes dataframe-agnostic. This will try to convert any dataframe/series-like object into the Narwhals respective DataFrame/Series, while leaving the other parameters as they are. Similarly, if the output of the function is a Narwhals DataFrame or Series, it will be converted back to the original dataframe/series type, while if the output is another type it will be left as is. By setting `pass_through=False`, then every input and every output will be required to be a dataframe/series-like object. Arguments: func: Function to wrap in a `from_native`-`to_native` block. pass_through: Determine what happens if the object can't be converted to Narwhals - `False`: raise an error - `True` (default): pass object through as-is eager_only: Whether to only allow eager objects - `False` (default): don't require `native_object` to be eager - `True`: only convert to Narwhals if `native_object` is eager series_only: Whether to only allow Series - `False` (default): don't require `native_object` to be a Series - `True`: only convert to Narwhals if `native_object` is a Series allow_series: Whether to allow Series (default is only Dataframe / Lazyframe) - `False` or `None`: don't convert to Narwhals if `native_object` is a Series - `True` (default): allow `native_object` to be a Series Returns: Decorated function. rergrzcHtdfd }|S)Nargsrrrzc< fd|D} fd|D}fdg||RD}|dkrd}t| |i|}t | S)Nc 8g|]}t|SrSr6).0argrOrMrLrNs r zBnarwhalify..decorator..wrapper..;sI   !-) +!-    rc @i|]\}}|t|Srmrn)rorvaluerOrMrLrNs r zBnarwhalify..decorator..wrapper..FsO    D%k!-) +!-    rcJh|]}t|ddx S)__native_namespace__N)getattr)rovbs r zAnarwhalify..decorator..wrapper..QsG $:DAAAArz_Found multiple backends. Make sure that all dataframe/series inputs come from the same backend.rY)itemsr__len__ ValueErrorr]) rjrargs_nw kwargs_nwbackendsr rryrOrMrerLrNs @rwrapperz.narwhalify..decorator..wrapper9s           G       $*<<>>   I878Y%5%5%7%788H !!A%%w oo%T70i00FV,??? ?r)rjrrrrzrr)rerrOrMrLrNs` r decoratorznarwhalify..decorator8sX t# @# @# @# @# @# @# @# @# @ # @JrN)rergrzrgr)rerLrMrNrOrs ```` r narwhalifyrsPR'''''''''R | 9T??rcBttjS)z3Instantiate an expression representing all columns.)rrdallrrrrrg bfhh  rnamesstr | Iterable[str]c8ttj|S)zCreates an expression that references one or more columns by their name(s). Arguments: names: Name(s) of the columns to use. )rrdcolrs rrrls bfen % %%rc8ttj|S)zCreates an expression that excludes columns by their name(s). Arguments: names: Name(s) of the columns to exclude. )rrdexcluders rrrus bj%( ) ))rindicesint | Sequence[int]c8ttj|S)a!Creates an expression that references one or more columns by their index(es). Notes: `nth` is not supported for Polars version<1.0.0. Please use [`narwhals.col`][] instead. Arguments: indices: One or more indices representing the columns to retrieve. )rrdnth)rs rrr~s bfg& ' ''rcBttjS)zReturn the number of rows.)rrdlenrrrrrrrrsrlrrcFttj||S)aReturn an expression representing a literal value. Arguments: value: The value to use as literal. Can be a scalar value, list, tuple, or dict. Lists and tuples are converted to `List` dtype, dicts to `Struct` dtype. dtype: The data type of the literal value. If not provided, the data type will be inferred by the native library. For empty lists/dicts, dtype must be specified explicitly. )rrdlit)rsrs rrrs bfUE** + ++rcolumnsrc8ttj|S)zReturn the minimum value. Note: Syntactic sugar for ``nw.col(columns).min()``. Arguments: columns: Name(s) of the columns to use in the aggregation function. )rrdminrs rrr bfg& ' ''rc8ttj|S)zReturn the maximum value. Note: Syntactic sugar for ``nw.col(columns).max()``. Arguments: columns: Name(s) of the columns to use in the aggregation function. )rrdmaxrs rrrrrc8ttj|S)zGet the mean value. Note: Syntactic sugar for ``nw.col(columns).mean()`` Arguments: columns: Name(s) of the columns to use in the aggregation function )rrdmeanrs rrrs bgw' ( ((rc8ttj|S)aLGet the median value. Notes: - Syntactic sugar for ``nw.col(columns).median()`` - Results might slightly differ across backends due to differences in the underlying algorithms used to compute the median. Arguments: columns: Name(s) of the columns to use in the aggregation function )rrdmedianrs rrrs bi) * **rc8ttj|S)zSum all values. Note: Syntactic sugar for ``nw.col(columns).sum()`` Arguments: columns: Name(s) of the columns to use in the aggregation function )rrdsumrs rrrrrexprsIntoExpr | Iterable[IntoExpr]c8ttj|S)a Sum all values horizontally across columns. Warning: Unlike Polars, we support horizontal sum over numeric columns only. Arguments: exprs: Name(s) of the columns to use in the aggregation function. Accepts expression input. )rrdsum_horizontalrs rrr b'/ 0 00rrc>ttj|d|iS)aCompute the bitwise AND horizontally across columns. Arguments: exprs: Name(s) of the columns to use in the aggregation function. Accepts expression input. ignore_nulls: Whether to ignore nulls: - If `True`, null values are ignored. If there are no elements, the result is `True`. - If `False`, Kleene logic is followed. Note that this is not allowed for pandas with classical NumPy dtypes when null values are present. r)rrdall_horizontalrrs rrr" b'J\JJ K KKrc>ttj|d|iS)aCompute the bitwise OR horizontally across columns. Arguments: exprs: Name(s) of the columns to use in the aggregation function. Accepts expression input. ignore_nulls: Whether to ignore nulls: - If `True`, null values are ignored. If there are no elements, the result is `False`. - If `False`, Kleene logic is followed. Note that this is not allowed for pandas with classical NumPy dtypes when null values are present. r)rrdany_horizontalrs rrrrrc8ttj|S)zCompute the mean of all values horizontally across columns. Arguments: exprs: Name(s) of the columns to use in the aggregation function. Accepts expression input. )rrdmean_horizontalrs rrrs b(%0 1 11rc8ttj|S)aGet the minimum value horizontally across columns. Notes: We support `min_horizontal` over numeric columns only. Arguments: exprs: Name(s) of the columns to use in the aggregation function. Accepts expression input. )rrdmin_horizontalrs rrrrrc8ttj|S)aGet the maximum value horizontally across columns. Notes: We support `max_horizontal` over numeric columns only. Arguments: exprs: Name(s) of the columns to use in the aggregation function. Accepts expression input. )rrdmax_horizontalrs rrrrrpearsonarjrymethodLiteral['pearson', 'spearman']cJttj|||S)zCompute the Pearson's or Spearman rank correlation between two columns. Arguments: a: Column name or Expression b: Column name or Expression method: Correlation method ('pearson' or 'spearman') )r)rrdcorr)rryrs rrr,s# bga6222 3 33rr{ddofrintcJttj|||S)a2Compute the covariance between two columns. Arguments: a: Column name or Expression b: Column name or Expression ddof: "Delta Degrees of Freedom": the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1. r)rrdcov)rryrs rrr9s# bfQ--- . ..r separatorr more_exprsrcHttj|g|R||dS)aHorizontally concatenate columns into a single string column. Arguments: exprs: Columns to concatenate into a single string column. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. Non-`String` columns are cast to `String`. *more_exprs: Additional columns to concatenate into a single string column, specified as positional arguments. separator: String that will be used to separate the values of each column. ignore_nulls: Ignore null values (default is `False`). If set to `False`, null values will be propagated and if the row contains any null values, the output is null. r)rrd concat_str)rrrrs rrrEs5&  eYjYYILYYY  rf_stringrjc@ttj|g|RS)zFormat expressions as a string. Arguments: f_string: A string that with placeholders. args: Expression(s) that fill the placeholders. )rrdformat)rrjs rrr]s$ bi04000 1 11rc@ttj|g|RS)aFolds the columns from left to right, keeping the first non-null value. Arguments: exprs: Columns to coalesce, must be a str, nw.Expr, or nw.Series where strings are parsed as column names and both nw.Expr/nw.Series are passed through as-is. Scalar values must be wrapped in `nw.lit`. *more_exprs: Additional columns to coalesce, specified as positional arguments. Raises: TypeError: If any of the inputs are not a str, nw.Expr, or nw.Series. )rrdcoalesce)rrs rrrgs$ bk%5*555 6 66rc.eZdZed dZd dZd S) Whenwhen nw_f.Whenrzc&||jdS)Nr)chain) _predicate)rrs r from_whenzWhen.from_whenxss4?"----rrs&IntoExpr | NonNestedLiteral | _1DArrayThenc\g|j|j|fR}t|Sr)_chainrr _from_chain)rrs new_chains rthenz When.then|s1c`tt|Sr)rr otherwise)rrrs rrzThen.otherwises#%''++O<<===rrrrzr)rrrzr>)rrrrrrrs@rrrsQ4444>>>>>>>>>>rrrcNttj|S)aStart a `when-then-otherwise` expression. Expression similar to an `if-else` statement in Python. Always initiated by a `nw.when().then()`, and optionally followed by chained `.when().then()` calls. An `.otherwise()` can be appended at the end. If not appended, and the condition is not `True`, `None` will be returned. Arguments: predicates: Condition(s) that must be met in order to apply the subsequent statement. Accepts one or more boolean expressions, which are implicitly combined with `&`. String input is parsed as a column name. Returns: A "When" object, which `.then` can be called on. )rrnw_fr)rs rrrs" >>$)Z0 1 11rrrrrrcBtt||||S)aInstantiate Narwhals Series from iterable (e.g. list or array). Arguments: name: Name of resulting Series. values: Values of make Series from. dtype: (Narwhals) dtype. If not provided, the native library may auto-infer it from `values`. backend: specifies which eager backend instantiate to. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN` or `CUDF`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"` or `"cudf"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin` or `cudf`. r)rr?)rrrrs r new_seriesrs$. &tVUGLLL M MMrrr]rcHttj||S)aLConstruct a DataFrame from an object which supports the PyCapsule Interface. Arguments: native_frame: Object which implements `__arrow_c_stream__`. backend: specifies which eager backend instantiate to. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN` or `CUDF`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"` or `"cudf"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin` or `cudf`. r)rrr)rrs rrrs! dolGDDD E EErrrrrrrcJttj|||S)aInstantiate DataFrame from dictionary. Indexes (if present, for pandas-like backends) are aligned following the [left-hand-rule](../concepts/pandas_index.md/). Notes: For pandas-like dataframes, conversion to schema is applied after dataframe creation. Arguments: data: Dictionary to create DataFrame from. schema: The DataFrame schema as Schema, dict of {name: type}, or a iterable of (name, type) tuples. If not specified, the schema will be inferred by the native library. If any `dtype` is `None`, the data type for that column will be inferred by the native library. backend: specifies which eager backend instantiate to. Only necessary if inputs are not Narwhals Series. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN` or `CUDF`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"` or `"cudf"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin` or `cudf`. r)rrrrrrs rrrs$@ dnT67CCC D DDrrrrqrcJttj|||S)a^Construct a DataFrame from a NumPy ndarray. Notes: Only row orientation is currently supported. For pandas-like dataframes, conversion to schema is applied after dataframe creation. Arguments: data: Two-dimensional data represented as a NumPy ndarray. schema: The DataFrame schema as Schema, dict of {name: type}, an iterable of (name, type) tuples, or a sequence of str. backend: specifies which eager backend instantiate to. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN` or `CUDF`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"` or `"cudf"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin` or `cudf`. r)rrrrs rrrs#6 dodFGDDD E EEr,)rsourcerc Fttj|f||d|S)aRead a CSV file into a DataFrame. Arguments: source: Path to a file. backend: The eager backend for DataFrame creation. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN` or `CUDF`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"` or `"cudf"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin` or `cudf`. separator: Single byte character to use as separator in the file. kwargs: Extra keyword arguments which are passed to the native CSV reader. For example, you could use `nw.read_csv('file.csv', backend='pandas', engine='pyarrow')`. rr)rrread_csvrrrrs rrrs2.  fMgMMfMM  rIntoBackend[Backend]rc Fttj|f||d|S)aLazily read from a CSV file. For the libraries that do not support lazy dataframes, the function reads a csv file eagerly and then converts the resulting dataframe to a lazyframe. Arguments: source: Path to a file. backend: The eager backend for DataFrame creation. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN` or `CUDF`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"` or `"cudf"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin` or `cudf`. separator: Single byte character to use as separator in the file. kwargs: Extra keyword arguments which are passed to the native CSV reader. For example, you could use `nw.scan_csv('file.csv', backend=pd, engine='pyarrow')`. r)rrscan_csvrs rrr*s2,  fMgMMfMM  rc Dttj|fd|i|S)aRead into a DataFrame from a parquet file. Arguments: source: Path to a file. backend: The eager backend for DataFrame creation. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN` or `CUDF`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"` or `"cudf"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin` or `cudf`. kwargs: Extra keyword arguments which are passed to the native parquet reader. For example, you could use `nw.read_parquet('file.parquet', backend=pd, engine='pyarrow')`. r)rr read_parquetrrrs rrrEs*$ d'JJJ6JJ K KKrc Dttj|fd|i|S)aLazily read from a parquet file. For the libraries that do not support lazy dataframes, the function reads a parquet file eagerly and then converts the resulting dataframe to a lazyframe. Note: Spark like backends require a session object to be passed in `kwargs`. For instance: ```py import narwhals as nw from sqlframe.duckdb import DuckDBSession nw.scan_parquet(source, backend="sqlframe", session=DuckDBSession()) ``` Arguments: source: Path to a file. backend: The eager backend for DataFrame creation. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN`, `CUDF`, `PYSPARK` or `SQLFRAME`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"`, `"cudf"`, `"pyspark"` or `"sqlframe"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin`, `cudf`, `pyspark.sql` or `sqlframe`. kwargs: Extra keyword arguments which are passed to the native parquet reader. For example, you could use `nw.scan_parquet('file.parquet', backend=pd, engine='pyarrow')`. r)rr scan_parquetrs rrrZs+F d'JJJ6JJ K KKrIntoExpr | Sequence[IntoExpr] named_exprsc>ttj|i|S)aCollect columns into a struct column. Arguments: *exprs: Column(s) to collect into a struct column, specified as positional arguments. Accepts only expression input. Strings are parsed as column names, other non-expression inputs are not allowed. **named_exprs: Additional columns to collect into the struct column, specified as keyword arguments. The columns will be renamed to the keyword used. )rrstruct)rrs rrrs! dk58K88 9 99r)Xr r!r"r#rr$r%r&r'r(r>r)r*r+r,rr-r.r/r0r1rr2r3rBrCr4r5r6r7r8r9r:r;r<rrrrrr@rrrrDrErEr rrrrrr6rrrPrrrrrrrrrrrrrrrrrrrrrrrFrFrArrrr]rQr)rrrzr)rrrzr )rr"rzr#)rr&rzr>)rr(rzr))r1rNr2r3rzrN)r1rNr2r7rzrN)r1rGr2r9rzrG)r1rMr2r;rzrM)r1rHr2r9rzr)r1rLr2r3rzr#)r1rLr2r7rzr#)r1rJr2r;rzr )r1rCr2r7rzrD)r1rFr2rGrzrH)r1rrr2rJrzrr) r1rrLrrMrrNrrOrPrzr) r1rTrLrrMrrNrrOrPrzrU)rZrrLrrzrH)rZr rLrrzrJ)rZr#rLrrzrL)rZrrLrrzr)rZrHrLrrzrbr) rerfrLrrMrrNrrOrPrzrg)rzr>)rrrzr>)rrrzr>)rsrlrrrzr>)rrrzr>)rrrzr>)rrrrrzr>)r)rrjryrjrrrzr>)rrjryrjrrrzr>) rrrrjrrrrrzr>)rrrjrjrzr>)rrrrjrzr>r) rrrrrrrrrzrrrr) rrrrrrrrrzr) rrrrrrrrrzr)rrrrrrrzr)rrrrrrrzr)rrrrjrzr>) __future__r functoolsrtypingrrrr r r narwhalsrdr r rnarwhals._exceptionsrnarwhals._expression_parsingrrnarwhals._typing_compatrrnarwhals._utilsrrrrrrrrrrrnarwhals.dataframerrrrnarwhals.dtypesr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<narwhals.exceptionsr= narwhals.exprr>r&narwhals.functionsr?r@rAnarwhals.schemarBNwSchemanarwhals.seriesrCr narwhals.stable.v2rDrErFnarwhals.stable.v2.typingrGrHrIrJrKrLrMrNnarwhals.translaterOrPrQcollections.abcrRrSrTrUtyping_extensionsrVrWrXnarwhals._translaterYrZr[r\r]r^r_narwhals._typingr`rarbrcrdrerfrgrhnarwhals.typingrirjrkrlrmrnrorprqrrrtrurr6r]rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr rrrrrr__all__rrrrs """""""EEEEEEEEEEEEEEEE22222222......;;;;;;;;99999999                          RQQQQQQQ>877777((((((FFFFFFFFFF............>>>>>>>>>>                    UTTTTTTTTT&EEEEEEEEEEEE9999999999HGGGGGGG                       S!!!A #A A}/}/}/}/}/ N+}/}/}/@FFFFF N+FFF$C<C<C<C<C>>>>49d>>>2222.#NNNNN4FFFF*BF E15 E E E E E EF( (((( 15FFFFFD :EH6LLLL*#L#L#L#LL : : : :Y Y Y r