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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)r rr rCrOrr|r%s rrrsVX-)Y!78-(k\ !!"'  r.r&cyrrnarwhals_objectr&s r to_nativer2rcyrrr0s rr2r2r3rcyrrr0s rr2r2srcyrrr0s rr2r2r 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. r.)nwr2r0s rr2r2s& <<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.r.)ritemsrgetattr__len__ ValueErrorr2)argsrargrvaluevbbackendsrrr)r'funcr&r(s rwrapperz.narwhalify..decorator..wrapper5s  !-) +!-  D &$*<<>  D%k!-) +!-  F 342&--/2 $:DAAAAH !A%w o%4*6*FV,? ?E  sB;C'C)rBrrrrrr)rHrIr)r'r&r(s` r decoratorznarwhalify..decorator4s) t# @# @ # @Jr)rHCallable[..., Any]rrKr)rHr&r'r(r)rJs ```` r narwhalifyrL s&R''R | T?rc<ttjS)z3Instantiate an expression representing all columns.)rr8allrrrrNrNc 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. )rr8colnamess rrQrQhs bffen %%rc8ttj|S)zCreates an expression that excludes columns by their name(s). Arguments: names: Name(s) of the columns to exclude. )rr8excluderRs rrUrUqs 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. )rr8nth)indicess rrWrWzs bffg& ''rc<ttjS)zReturn the number of rows.)rr8lenrrrrZrZrOrc@ttj||S)zReturn an expression representing a literal value. Arguments: value: The value to use as literal. dtype: The data type of the literal value. If not provided, the data type will be inferred by the native library. )rr8lit)rDrs rr\r\s 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. )rr8mincolumnss rr^r^ 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. )rr8maxr_s rrcrcrarc8ttj|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 )rr8meanr_s rreres 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 )rr8medianr_s rrgrgs 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 )rr8sumr_s rririrarc8ttj|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. )rr8sum_horizontalexprss rrkrk 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)rr8all_horizontalrrms rrprp 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)rr8any_horizontalrqs rrtrtrrrc8ttj|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. )rr8mean_horizontalrls rrvrvs 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. )rr8min_horizontalrls rrxrx rnrc8ttj|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. )rr8max_horizontalrls rrzrzrnr 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|)rr8 concat_str)rmr}r more_exprss rrr&s)&  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. )rr8format)f_stringrBs 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. )rr8coalesce)rmrs rrrHs bkk%5*5 66rc2eZdZeddZdfd ZxZS)Whenc&||jSr) _predicate)rwhens r from_whenzWhen.from_whenYs4??##rcHtjt| |Sr)Then from_thenr}thenrrDrs rrz When.then]s~~egl5122r)rz nw_f.Whenrr)rD&IntoExpr | NonNestedLiteral | _1DArrayrr)rrrrrrrrs@rrrXs$$33rrc2eZdZeddZdfd ZxZS)rc ||jSr)r)rrs rrzThen.from_thenbsDKK  rc4tt| |Sr)rr} otherwisers rrzThen.otherwisefs%'+E233r)rz nw_f.Thenrr)rDrrr>)rrrrrrrrs@rrras!!44rrcLtjtj|S)aStart a `when-then-otherwise` expression. Expression similar to an `if-else` statement in Python. Always initiated by a `pl.when().then()`, and optionally followed by a `.otherwise()` can be appended at the end. If not appended, and the condition is not `True`, `None` will be returned. Info: Chaining multiple `.when().then()` statements is currently not supported. See [Narwhals#668](https://github.com/narwhals-dev/narwhals/issues/668). 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) predicatess rrrjs* >>$))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)aXInstantiate 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 or dict of {name: type}. 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 DDrr rcDttj|||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}, 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 rrrs4 doodFGD EErc Dttj|fd|i|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`. 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')`. r)rrread_csvsourcerrs rrrs"$ dmmFFGFvF GGrc Dttj|fd|i|S)aRLazily 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`. 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 rrrs"* dmmFFGFvF GGrc 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_parquetrs rrrs$$ 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 rrr3s%F d''JJ6J KKr)Tr!r"r#r$rr%r&r'r(r)r>r*r+r,rr-r.r/r0r1r r2r3rBrCr4r5r6r7r8r9r:r;r<rNrprtrrQr@rrDrErEr rUrrrrrrrrPrrZr\rcrzrrrrrrervrgr^rxrLrrWrrrrrFrFrArirkr2rQr)rzNwDataFrame[IntoDataFrameT]rDataFrame[IntoDataFrameT])rzNwLazyFrame[IntoLazyFrameT]rLazyFrame[IntoLazyFrameT])rzNwSeries[IntoSeriesT]rSeries[IntoSeriesT])rrrr>)rzZNwDataFrame[IntoDataFrameT] | NwLazyFrame[IntoLazyFrameT] | NwSeries[IntoSeriesT] | NwExprrzRDataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT] | Expr)rrNrUnpack[OnlySeries]rrN)rrNrUnpack[AllowSeries]rrN)rrGrUnpack[ExcludeSeries]rrG)rrMrUnpack[AllowLazy]rrM)rrHrrrr)rrLrrrr)rrLrrrr)rrJrrrr)rzIntoDataFrameT | IntoSeriesTrrrz/DataFrame[IntoDataFrameT] | Series[IntoSeriesT])rz-IntoDataFrameT | IntoLazyFrameT | IntoSeriesTrzUnpack[AllowAny]rKDataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT])rrrrzUnpack[PassThroughUnknown]rrr) rrr&rr'rr(rr) bool | Nonerr) rzJIntoLazyFrameT | IntoDataFrameT | IntoSeriesT | IntoFrame | IntoSeries | Tr&rr'rr(rr)rrzOLazyFrame[IntoLazyFrameT] | DataFrame[IntoDataFrameT] | Series[IntoSeriesT] | T)r1rr&rrrH)r1rr&rrrJ)r1rr&rrrL)r1rr&rrr)r1rr&rrz3IntoDataFrameT | IntoLazyFrameT | IntoSeriesT | Anyr) rHzCallable[..., Any] | Noner&rr'rr(rr)rrrK)rr>)rSzstr | Iterable[str]rr>)rXzint | Sequence[int]rr>)rDrlrrrr>)r`rrr>)rmIntoExpr | Iterable[IntoExpr]rr>)rmrrrrr>) rmrrrjr}rrrrr>)rrrBrjrr>)rmrrrjrr>)rrrr) rrrrrrrrrrr)rrrz#Mapping[str, DType] | Schema | Nonerrrrr)rrrrrrrr)rrrzIntoBackend[Backend]rrrr) __future__r functoolsrtypingrrrr r r r narwhalsr8r rrnarwhals._exceptionsrnarwhals._expression_parsingrrnarwhals._typing_compatrrnarwhals._utilsrrrrrrrrrrrnarwhals.dataframerrr rnarwhals.dtypesr!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<narwhals.exceptionsr= narwhals.exprr>rnarwhals.functionsr?r@rAnarwhals.schemarBrnarwhals.seriesrCrnarwhals.stable.v2rDrErFnarwhals.stable.v2.typingrGrHrIrJrKrLrMrNnarwhals.translaterOrPrQcollections.abcrRrSrTtyping_extensionsrUrVrWnarwhals._translaterXrYrZr[r\r]r^narwhals._typingr_r`rarbrcrdrerfrgnarwhals.stable.v2.dtypesrhnarwhals.typingrirjrkrlrmrnrorprqrrrtrurrr2rLrNrQrUrWrZr\r^rcrergrirkrprtrvrxrzrrrrrrrrrrrrrrrr__all__rrrrs5"OOO2.;9    R<8(FF..>>   UT;;99H/    S!A#A A}/ N+}/@ F N+ F C