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dSrrr s r~r!r!gs "cr.) eager_only series_onlyIntoDataFrameT | IntoSeries pass_throughrr'rr( allow_seriescdSrrrr*r'r(r+s r~r!r!m !$r)r(IntoDataFrameT | IntoSeriesT/DataFrame[IntoDataFrameT] | Series[IntoSeriesT]cdSrrr-s r~r!r!xs 7:cr)r'r(r+rFrscdSrrr-s r~r!r!r.rcdSrrr-s r~r!r!  r)r(r+cdSrrr-s r~r!r!r.rcdSrrr-s r~r!r!r4r)r'r+cdSrrr-s r~r!r! #rr*r'r(r+cdSrrr-s r~r!r!r.rrGcdSrrr-s r~r!r!r.r)r*r(r+cdSrrr-s r~r!r!r.r)r*r'r(IntoFrame | IntoSeries-DataFrame[Any] | LazyFrame[Any] | Series[Any]cdSrrr-s r~r!r!s 58Cr)r*r'r+cdSrrr-s r~r!r!r8rr bool | NonecdSrrr-s r~r!r!s  #rFJIntoDataFrameT | IntoLazyFrameT | IntoFrame | IntoSeriesT | IntoSeries | TOLazyFrame[IntoLazyFrameT] | DataFrame[IntoDataFrameT] | Series[IntoSeriesT] | Tc t|ttfr|s|St|tr|s|r|S|r.dt t |}t |t|||||tj S)aConvert `native_object` to Narwhals Dataframe, Lazyframe, or Series. 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Depending on the other arguments, input object can be - a Dataframe / Lazyframe / Series supported by Narwhals (pandas, Polars, PyArrow, ...) - an object which implements `__narwhals_dataframe__`, `__narwhals_lazyframe__`, or `__narwhals_series__` pass_through: Determine what happens if the object can't be converted to Narwhals - `False` (default): raise an error - `True`: pass object through as-is eager_only: Whether to only allow eager objects - `False` (default): don't require `native_object` to be eager - `True`: only convert to Narwhals if `native_object` is eager series_only: Whether to only allow Series - `False` (default): don't require `native_object` to be a Series - `True`: only convert to Narwhals if `native_object` is a Series allow_series: Whether to allow Series (default is only Dataframe / Lazyframe) - `False` or `None` (default): don't convert to Narwhals if `native_object` is a Series - `True`: allow `native_object` to be a Series Returns: DataFrame, LazyFrame, Series, or original object, depending on which combination of parameters was passed. z1from_native() got an unexpected keyword argument )r*r'r(r+version) rrrr?nextiter TypeErrorrCrrx)rr*r'r(r+rmsgs r~r!r!sZ-)Y!788-((k\ V$tDzzBRBRVVnn !!    rr*narwhals_objectcdSrrrLr*s r~ to_nativerO= SrcdSrrrNs r~rOrOArPrcdSrrrNs r~rOrOEs #rcdSrrrNs r~rOrOIrrKDataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT]3IntoDataFrameT | 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. rK)nwrOrNs r~rOrOMs& <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. rXrZrrcHtdfd }|S)Nargsrrrrc< fd|D} fd|D}fdg||RD}|dkrd}t||i|}t | S)Nc 8g|]}t|Sr9r!).0argr+r'r*r(s r~ zBnarwhalify..decorator..wrapper..sI   !-) +!-    rc @i|]\}}|t|Sr`ra)rbrvaluer+r'r*r(s r~ zBnarwhalify..decorator..wrapper..sO    D%k!-) +!-    rcJh|]}t|ddx S)__native_namespace__N)getattr)rbvbs r~ zAnarwhalify..decorator..wrapper..sG $:DAAAArz_Found multiple backends. Make sure that all dataframe/series inputs come from the same backend.rK)itemsr__len__ ValueErrorrO) r]rbackendsrJrrlr+r'rXr*r(s @r~wrapperz.narwhalify..decorator..wrappers            D       $*<<>>   F242&--//22H !!A%%w oo%T4*6**FV,??? ?r)r]rrrrrrr)rXrsr+r'r*r(s` r~ decoratorznarwhalify..decoratorsX t# @# @# @# @# @# @# @# @# @ # @JrN)rXrZrrrZr)rXr*r'r(r+rts ```` r~ narwhalifyrucsPR'''''''''R | 9T??rcBttjS)z3Instantiate an expression representing all columns.)rrWallrrr~rwrw 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. )rrWcolrys r~r|r|s bfen % %%rc8ttj|S)zCreates an expression that excludes columns by their name(s). Arguments: names: Name(s) of the columns to exclude. )rrWexcluder}s r~rrs 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. )rrWnth)rs r~rrs bfg& ' ''rcBttjS)zReturn the number of rows.)rrWlenrrr~rrrxrrfr]rrcFttj||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. )rrWlit)rfrs r~rrs 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. )rrWminrs r~rr 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. )rrWmaxrs r~rrrrc8ttj|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 )rrWmeanrs r~rrs 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 )rrWmedianrs r~rrs 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 )rrWsumrs r~rr!rrexprsIntoExpr | 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. )rrWsum_horizontalrs r~rr- b'/ 0 00r ignore_nullsc>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)rrWall_horizontalrrs r~rr:" 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)rrWany_horizontalrs r~rrJrrc8ttj|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. )rrWmean_horizontalrs r~rrZs 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. )rrWmin_horizontalrs r~rrdrrc8ttj|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. )rrWmax_horizontalrs r~rrqrr separatorr more_exprsrYrcHttj|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)rrW concat_str)rrrrs r~rr~s5&  eYjYYILYYY  rf_stringr]c@ttj|g|RS)zFormat expressions as a string. Arguments: f_string: A string that with placeholders. args: Expression(s) that fill the placeholders. )rrWformat)rr]s r~rrs$ 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. )rrWcoalesce)rrs r~rrs$ bk%5*555 6 66rc8eZdZed dZd fd ZxZS) Whenwhen nw_f.Whenrrc"||jSr) _predicate)rrs r~ from_whenzWhen.from_whenss4?###rrf&IntoExpr | NonNestedLiteral | _1DArrayThencvtt|Sr)r from_thenrythenr|rfr}s r~rz When.thens%~~eggll511222r)rrrrr)rfrrrr)rrrrrrrrs@r~rrs[$$$[$3333333333rrc8eZdZed dZd fd ZxZS) rr nw_f.Thenrrc.||j|jSr)rr)rrs r~rzThen.from_thenss4*DN;;;rrfrr:c`tt|Sr)rry otherwisers r~rzThen.otherwises#%''++E22333r)rrrrr)rfrrrr:)rrrrrrrrs@r~rrs[<<<[<4444444444rr predicatescNttj|S)aStart a `when-then-otherwise` expression. Expression similar to an `if-else` statement in Python. Always initiated by a `pl.when().then()`, and optionally followed by a `.otherwise()` can be appended at the end. If not appended, and the condition is not `True`, `None` will be returned. Info: Chaining multiple `.when().then()` statements is currently not supported. See [Narwhals#668](https://github.com/narwhals-dev/narwhals/issues/668). Arguments: predicates: Condition(s) that must be met in order to apply the subsequent statement. Accepts one or more boolean expressions, which are implicitly combined with `&`. String input is parsed as a column name. Returns: A "when" object, which `.then` can be called on. )rrnw_fr)rs r~rrs* >>$)Z0 1 11rrrrrcBtt||||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 MMrrrMcHttj||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 r~rrs! 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 or dict of {name: type}. If not specified, the schema 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 r~rrs#: dnT67CCC D DDrr rrarcJttj|||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 r~rr*s#4 dodFGDDD E EErsourcerc 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_csvrrrs r~rrGs)$ dmFFFGFvFF G GGrIntoBackend[Backend]c 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 r~rr\s)* dmFFFGFvFF G 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 r~rrts*$ 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 r~rrs+F d'JJJ6JJ K KKr)Trrr r!rr"r#r$r%r&r:r'r(r)rr*r+r,r-r.rr/r0r>r?r1r2r3r4r5r6r7r8r9rwrrrr|r<rr@rArAr rrrrrr!rrrDrrrrrrrrrrrrrrrrurrrrrrrBrBr=rrrOrEr)rrrrr)rr rrr )rr rrr)rrrrr:)rrrrr)rrgrrrrrg)rrbrrrrrb)rrerrrrre)rr%rrrrr%) rr)r*rr'rr(rr+rrrr) rr/r*rr'rr(rr+rrrr0) rrFr*rr'rr(rr+rsrrr) rrir*rr'rr(rr+rsrrri) rrFr*rr'rr(rr+rsrrr) rrir*rr'rr(rr+rsrrri) rrmr*rr'rr(rr+rsrrr) rrFr*rr'rr(rr+rsrrr) rrGr*rr'rr(rr+rsrrr ) rrFr*rr'rr(rr+rsrrr) rr=r*rr'rr(rr+rrrr>) rrmr*rr'rr(rr+rsrrr) rrr*rr'rr(rr+rArrr)rrCr*rr'rr(rr+rArrrrrD)rLrr*rrrrF)rLr r*rrrrG)rLrr*rrrrm)rLrr*rrrr)rLrTr*rrrrUr) rXrYr*rr'rr(rr+rArrrZ)rrr:)ryrzrrr:)rrrrr:)rfr]rrrrr:)rrrrr:)rrrrr:)rrrrrrr:) rrrrYrrrrrrr:)rrr]rYrrr:)rrrrYrrr:)rrrrr) rrrrrrrrrrrhrrr)rrrrrrrrrc)rrrrrrrrrf) __future__r functoolsrtypingrrrr r r r narwhalsrWr rrnarwhals._typing_compatrrnarwhals._utilsrrrrrrrrrrrnarwhals.dataframerrrrnarwhals.dtypesrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9 narwhals.exprr:rnarwhals.functionsr;r<r=narwhals.schemar>rnarwhals.seriesr?rnarwhals.stable.v2r@rArBnarwhals.translaterCrDrEnarwhals.typingrFrGcollections.abcrHrIrJtyping_extensionsrKrLnarwhals._translaterMnarwhals._typingrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrergrirkrlrmrr!rOrurwr|rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr__all__rrr~rs """""""OOOOOOOOOOOOOOOOOO2222222299999999                          RQQQQQQQ<)(((((FFFFFFFFFF............>>>>>>>>>>TTTTTTTTTT::::::::#;;;;;;;;;;11111111222222HGGGGGGG%%%%%%                        -=>>>J-=>>>Jgi}555GS!!!A #A Agm<EEE }/}/}/}/}/ N+}/}/}/@ F F F F F N+ F F F ;";";";";"Xk ";";";"|6!!!!!X!!! 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