^jUddlmZddlmZddlmZmZmZmZm Z m Z ddl Z ddl m Z mZddlmZddlmZmZddlmZmZdd lmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#dd l$m%Z&m'Z(dd l)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@mAZAmBZBmCZCmDZDmEZEmFZFdd lGmHZHdd lImJZKddlLmMZMmNZNmOZOddlPmQZRddlSmTZUddlVmWZWmXZXmYZYddlZm[Z[m\Z\m]Z]m^Z^m_Z_m`Z`maZambZbddlcmdZdmeZemfZferrddlgmhZhmiZimjZjmkZkddllmmZmmnZnmoZoddlpmqZqmrZrmsZsmtZtmuZumvZvmwZwddlxmyZymzZzm{Z{m|Z|m}Z}m~Z~mZddl$mZmZddlmZmZmZmZmZmZmZmZmZedeZemdZedZGdde&e\Z%Gd d!e(e^Z'Gd"d#eUe`ZTGd$d%eKZJGd&d'eRZQe dsd(Ze dtd)Ze dud*Ze dvd+Z dwd,Ze dxd-Ze dyd.Ze dzd/Ze d{d0Ze d|d1Ze d}d2Ze d~d3Ze dd4Ze dd5Ze dd6Ze dd7Ze dd8Zd9d9d9dd: dd;Ze dZe dt|||}td|SNbackendDataFrame[Any])r} from_arrowr )cls native_framerresultrs rrzDataFrame.from_arrow~s'#L'#B$f--rNrc@t||||}td|Sr)r} from_dictr rdataschemarrrs rrzDataFrame.from_dicts)"4"A$f--rc@t||||}td|Sr)r} from_dictsr rs rrzDataFrame.from_dicts)#D&'#B$f--rc@t||||}td|Srr} from_numpyr rs rrzDataFrame.from_numpyrrc"tdtS)Ntype[Series[Any]])r rCrs r_serieszDataFrame._seriess'00rc"tdtS)Ntype[LazyFrame[Any]])r rrs r _lazyframezDataFrame._lazyframes*I66rcyNritems r __getitem__zDataFrame.__getitem__sWZrcyrrrs rrzDataFrame.__getitem__srcyrrrs rrzDataFrame.__getitem__src"t||Sr)r}r)rrrs rrzDataFrame.__getitem__sw"4((rc"t||Sr)r} get_column)rnamers rrzDataFrame.get_columnsw!$''r)sessionc8tt| ||S)N)rr) _stableifyr}lazy)rrrrs rrzDataFrame.lazys %',w,HIIr. as_seriescyrrrrs rto_dictzDataFrame.to_dictsTWrcyrrrs rrzDataFrame.to_dictsMPrTcyrrrs rrzDataFrame.to_dicts9Frc|d}t|t|jttj d|S)Nz]`Expr.any_value` is being called from the stable API although considered an unstable feature.rr)rr= _append_noderr AGGREGATION)rrrs rrzExpr.any_valueLs> #  c23  X));\ R  rcT|jttjdS)zGet the first value.firstrrrORDERABLE_AGGREGATIONrs rrz Expr.firstVs   (*H*H'!RSSrcT|jttjdS)zGet the last value.lastrrs rrz Expr.lastZs   (*H*H&!QRRrN)rrrrW)rrW)rrrrrrrrrr>r>Ks05 TSrr>c$eZdZejZy)rBN)rrrrr|r{rrrrBrB_s zzHrrBcyrrobjs rrrcORrcyrrrs rrrer rcyrrrs rrrgCFrcyrrrs rrris%(rc2t|tr>t|jj t j |jSt|tr>t|jj t j |jSt|tr>t|jj t j |jSt|trt|jSt!|yrx) isinstancerr_compliant_frame _with_versionrr|_levelrrrrC_compliant_seriesNwExprr>_nodesrrs rrrms #{#--;;GJJGszzZZ#{#--;;GJJGszzZZ#x c++99'**ESZZXX#vSZZ  rc yrr native_objectkwdss r from_nativer~sPSrc yrrrs rrrsQTrc yrrrs rrrsrc yrrrs rrrsUXrc yrrrs rrr!$rc yrrrs rrrrc yrrrs rrrr!rc yrrrs rrrrrc yrrrs rrrs7:rc yrrrs rrrs SVrc yrrrs rrrsLOrcyrrr pass_through eager_only series_only allow_seriess rrrs rFr)r*r+r,c t|ttfr|s|St|tr|s|r|St |||||dt j S)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)r)r*r+r,eager_or_interchange_onlyversion)rrrrCrOrr|r(s rrrsVX-)Y!78-(k\ !!"'  r.r)cyrrnarwhals_objectr)s r to_nativer5rcyrrr3s rr5r5r6rcyrrr3s rr5r5srcyrrr3s rr5r5r rc0tj||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. r1)nwr5r3s rr5r5s& <<l CCrTc4dfd }||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. c:tdfd }|S)Nc |Dcgc]}t|  }}|jDcic]\}}|t|  }}}g||jDchc]}t|ddx}r|} }| j dkDr d} t | |i|} t | Scc}wcc}}wcc}w)Nr-__native_namespace__z_Found multiple backends. Make sure that all dataframe/series inputs come from the same backend.r1)ritemsrgetattr__len__ ValueErrorr5)argsrargargs_nwrvalue kwargs_nwvbbackendsrrr,r*funcr)r+s rwrapperz.narwhalify..decorator..wrapper9s  !-) +!-  G &$*<<>  D%k!-) +!-  I 978Y%5%5%78 $:DAAAAH !A%w o%70i0FV,? ?E  sB;C'C)rErrrrrr)rMrNr,r*r)r+s` r decoratorznarwhalify..decorator8s) t# @# @ # @Jr)rMCallable[..., Any]rrPr)rMr)r*r+r,rOs ```` r narwhalifyrQs&R''R | T?rc<ttjS)z3Instantiate an expression representing all columns.)rr;allrrrrSrSg bffh rc8ttj|S)zCreates an expression that references one or more columns by their name(s). Arguments: names: Name(s) of the columns to use. )rr;colnamess rrVrVls bffen %%rc8ttj|S)zCreates an expression that excludes columns by their name(s). Arguments: names: Name(s) of the columns to exclude. )rr;excluderWs rrZrZus bjj%( ))rc8ttj|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. )rr;nth)indicess rr\r\~s bffg& ''rc<ttjS)zReturn the number of rows.)rr;lenrrrr_r_rTrc@ttj||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. )rr;lit)rHrs rraras bffUE* ++rc8ttj|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. )rr;mincolumnss rrcrc bffg& ''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. )rr;maxrds rrhrhrfrc8ttj|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 )rr;meanrds rrjrjs bggw' ((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 )rr;medianrds rrlrls bii) **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 )rr;sumrds rrnrnrfrc8ttj|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. )rr;sum_horizontalexprss rrprp b''/ 00rc>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)rr;all_horizontalrrrs rruru b''J\J 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)rr;any_horizontalrvs rryryrwrc8ttj|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. )rr;mean_horizontalrqs rr{r{s b((%0 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. )rr;min_horizontalrqs rr}r}rsrc8ttj|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. )rr;max_horizontalrqs rrrrsrcDttj|||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') )method)rr;corr)arKrs rrr,s bgga62 33rr@ddofcDttj|||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)rr;cov)rrKrs rrr9s bffQ- ..r separatorrcHttj|g|||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)rr; concat_str)rrrr more_exprss rrrEs)&  eYjYILY rc@ttj|g|S)zFormat expressions as a string. Arguments: f_string: A string that with placeholders. args: Expression(s) that fill the placeholders. )rr;format)f_stringrEs rrr]s bii040 11rc@ttj|g|S)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. )rr;coalesce)rrrs rrrgs bkk%5*5 66rc&eZdZeddZddZy)Whenc*||jdS)Nr)chain) _predicate)rwhens r from_whenzWhen.from_whenxs4??"--rcfg|j|j|f}tj|Sr)_chainrThen _from_chain)rrH new_chains rthenz When.then|s/)rrrrrrrs@rrrs4>>rrcLtjtj|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 11rc2tt||||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. &tVUGL MMrcBttj||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 doolGD EErrcDttj|||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@ dnnT67C DDrrrcDttj|||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 rrrs6 doodFGD EEr,)rc 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_csvsourcerrrs rrrs).  fMgMfM 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*s),  fMgMfM 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''JJ6J 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''JJ6J KKrc>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)rr named_exprss rrrs dkk58K8 99r)Xr r!r"r#rr$r%r&r'r(r>r)r*r+r,rr-r.r/r0r1rr2r3rBrCr4r5r6r7r8r9r:r;r<rSruryrrVr@rrrrDrErEr rZrrrrrrrrPrr_rarhrrrrrrrjr{rlrcr}rQrr\rrrrrFrFrArrnrpr5rQr)rzNwDataFrame[IntoDataFrameT]rDataFrame[IntoDataFrameT])rzNwLazyFrame[IntoLazyFrameT]rLazyFrame[IntoLazyFrameT])rzNwSeries[IntoSeriesT]rSeries[IntoSeriesT])rrrr>)rzZNwDataFrame[IntoDataFrameT] | NwLazyFrame[IntoLazyFrameT] | NwSeries[IntoSeriesT] | NwExprrzRDataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT] | Expr)rrNrUnpack[OnlySeries]rrN)rrNrUnpack[AllowSeries]rrN)rrGrUnpack[ExcludeSeries]rrG)rrMrUnpack[AllowLazy]rrM)rrHrrrr)rrLrrrr)rrLrrrr)rrJrrrr)rzIntoDataFrameT | IntoSeriesTrrrz/DataFrame[IntoDataFrameT] | Series[IntoSeriesT])rz-IntoDataFrameT | IntoLazyFrameT | IntoSeriesTrzUnpack[AllowAny]rKDataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT])rrrrzUnpack[PassThroughUnknown]rrr) rrr)rr*rr+rr, bool | Nonerr) rzJIntoLazyFrameT | IntoDataFrameT | IntoSeriesT | IntoFrame | IntoSeries | Tr)rr*rr+rr,rrzOLazyFrame[IntoLazyFrameT] | DataFrame[IntoDataFrameT] | Series[IntoSeriesT] | T)r4rr)rrrH)r4rr)rrrJ)r4rr)rrrL)r4rr)rrr)r4rr)rrz3IntoDataFrameT | IntoLazyFrameT | IntoSeriesT | Anyr) rMzCallable[..., Any] | Noner)rr*rr+rr,rrrP)rr>)rXzstr | Iterable[str]rr>)r]zint | Sequence[int]rr>)rHrlrrrr>)rerrr>)rrrrr>)rrrrrrr>)pearson)rrjrKrjrzLiteral['pearson', 'spearman']rr>)rrjrKrjrintrr>) rrrrrjrrrrrr>)rrrErjrr>)rrrrrjrr>r) rrrrrrrrrrrrr) rrrrrrrrrr) rrrIntoBackend[Backend]rrrrrr)rrrrrrrr)rrrrrrrr)rrzIntoExpr | Sequence[IntoExpr]rrjrr>) __future__r functoolsrtypingrrrr r r narwhalsr;r r rnarwhals._exceptionsrnarwhals._expression_parsingrrnarwhals._typing_compatrrnarwhals._utilsrrrrrrrrrrrnarwhals.dataframerrrrnarwhals.dtypesr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<narwhals.exceptionsr= narwhals.exprr>rnarwhals.functionsr?r@rAnarwhals.schemarBNwSchemanarwhals.seriesrCrnarwhals.stable.v2rDrErFnarwhals.stable.v2.typingrGrHrIrJrKrLrMrNnarwhals.translaterOrPrQcollections.abcrRrSrTrUtyping_extensionsrVrWrXnarwhals._translaterYrZr[r\r]r^r_narwhals._typingr`rarbrcrdrerfrgrhnarwhals.typingrirjrkrlrmrnrorprqrrrtrurrr5rQrSrVrZr\r_rarcrhrjrlrnrpruryr{r}rrrrrrrrrrrrrrrrrrrr__all__rrrrs"EE2.;9    R>8(FF..>>   UTEE99H    S!A#A A}/ N+}/@F N+F$C499d>2.#N N N N ' N  N4F F.GFF*BF E15 E  E > E. E  EF(( E( 15F F -F' F  FD  '     :EH 1>ASV6L L6LBELL*#L #L1#L=@#L#LL :Y r