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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. F)rNrOrPrQeager_or_interchange_onlyversion)r-rr rCrOrrrTs rr8r8s}X-)Y!788-((k\ !!"'    r.rNnarwhals_objectrcdSrrr\rNs 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. r[)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. rgrirzcHtdfd }|S)Nargsrrrzc< fd|D} fd|D}fdg||RD}|dkrd}t| |i|}t | S)Nc 8g|]}t|SrUr8).0argrQrOrNrPs r zBnarwhalify..decorator..wrapper..AsI   !-) +!-    rc @i|]\}}|t|Srorp)rqrvaluerQrOrNrPs r zBnarwhalify..decorator..wrapper..LsO    D%k!-) +!-    rcJh|]}t|ddx S)__native_namespace__N)getattr)rqvbs r zAnarwhalify..decorator..wrapper..WsG $:DAAAArz_Found multiple backends. Make sure that all dataframe/series inputs come from the same backend.r[)itemsr__len__ ValueErrorr_) rlrargs_nw kwargs_nwbackendsr rr{rQrOrgrNrPs @rwrapperz.narwhalify..decorator..wrapper?s           G       $*<<>>   I878Y%5%5%7%788H !!A%%w oo%T70i00FV,??? ?r)rlrrrrzrr)rgrrQrOrNrPs` r decoratorznarwhalify..decorator>sX t# @# @# @# @# @# @# @# @# @ # @JrN)rgrirzrir)rgrNrOrPrQrs ```` r narwhalifyrsPR'''''''''R | 9T??rcBttjS)z3Instantiate an expression representing all columns.)rrfallrrrrrm 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. )rrfcolrs rrrrs bfen % %%rc8ttj|S)zCreates an expression that excludes columns by their name(s). Arguments: names: Name(s) of the columns to exclude. )rrfexcluders rrr{s 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. )rrfnth)rs rrrs bfg& ' ''rcBttjS)zReturn the number of rows.)rrflenrrrrrrrrurlrrcFttj||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. )rrflit)rurs 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. )rrfminrs 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. )rrfmaxrs 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 )rrfmeanrs 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 )rrfmedianrs 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 )rrfsumrs 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. )rrfsum_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)rrfall_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)rrfany_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. )rrfmean_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. )rrfmin_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. )rrfmax_horizontalrs rrr%rrpearsonarjr{methodLiteral['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)rrfcorr)rr{rs rrr2s# bga6222 3 33r 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)rrf concat_str)rrrrs rrr?s5&  eYjYYILYYY  rf_stringrlc@ttj|g|RS)zFormat expressions as a string. Arguments: f_string: A string that with placeholders. args: Expression(s) that fill the placeholders. )rrfformat)rrls rrrWs$ 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. )rrfcoalesce)rrs rrras$ bk%5*555 6 66rc.eZdZed dZd dZd S) Whenwhen nw_f.Whenrzc&||jdS)Nr)chain) _predicate)rrs r from_whenzWhen.from_whenrss4?"----rru&IntoExpr | NonNestedLiteral | _1DArrayThenc\g|j|j|fR}t|Sr)_chainrr _from_chain)rru new_chains rthenz When.thenvs1).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 rrr~s" >>$)Z0 1 11rrrrrrcBtt||||S)aInstantiate Narwhals Series from iterable (e.g. list or array). Arguments: name: Name of resulting Series. values: Values of make Series from. dtype: (Narwhals) dtype. If not provided, the native library may auto-infer it from `values`. backend: specifies which eager backend instantiate to. `backend` can be specified in various ways - As `Implementation.` with `BACKEND` being `PANDAS`, `PYARROW`, `POLARS`, `MODIN` or `CUDF`. - As a string: `"pandas"`, `"pyarrow"`, `"polars"`, `"modin"` or `"cudf"`. - Directly as a module `pandas`, `pyarrow`, `polars`, `modin` or `cudf`. r)rr?)rrrrs r new_seriesrs$. &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 EErrrrr#Mapping[str, DType] | Schema | NonercJttj|||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#> dnT67CCC D DDrr rrqrcJttj|||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 rrrs#4 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 rrrs2,  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 rrr8s*$ 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 rrrMs+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 rrrss! dk58K88 9 99r)Vr!r"r#r$rr%r&r'r(r)r>r*r+r,rr-r.r/r0r1r r2r3rBrCr4r5r6r7r8r9r:r;r<rrrrrr@rrrDrErEr rrrrrr8rrrPrrrrrrrrrrrrrrrrrrrrrrrFrFrArrrr_rQr)rrrzr)rr!rzr")rr$rzr%)rr(rzr>)rr*rzr+)r3rNr4r5rzrN)r3rNr4r9rzrN)r3rGr4r;rzrG)r3rMr4r=rzrM)r3rHr4r;rzr)r3rLr4r5rzr%)r3rLr4r9rzr%)r3rJr4r=rzr")r3rEr4r9rzrF)r3rHr4rIrzrJ)r3rrr4rLrzrr) r3rrNrrOrrPrrQrRrzr) r3rVrNrrOrrPrrQrRrzrW)r\rrNrrzrH)r\r"rNrrzrJ)r\r%rNrrzrL)r\rrNrrzr)r\rJrNrrzrdr) rgrhrNrrOrrPrrQrRrzri)rzr>)rrrzr>)rrrzr>)rurlrrrzr>)rrrzr>)rrrzr>)rrrrrzr>)r)rrjr{rjrrrzr>) rrrrjrrrrrzr>)rrrlrjrzr>)rrrrjrzr>)rrrzr) rrrrrrrrrzrr)rrrrrrrzrr) rrrrrrrrrzr) rrrrrrrrrzr)rrrrrrrzr)rrrrrrrzr)rrrrjrzr>) __future__r functoolsrtypingrrrr r r r narwhalsrfr rrnarwhals._exceptionsrnarwhals._expression_parsingrrnarwhals._typing_compatrrnarwhals._utilsrrrrrrrrrrrnarwhals.dataframerrr rnarwhals.dtypesr!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<narwhals.exceptionsr= narwhals.exprr>r(narwhals.functionsr?r@rAnarwhals.schemarBrnarwhals.seriesrCr narwhals.stable.v2rDrErFnarwhals.stable.v2.typingrGrHrIrJrKrLrMrNnarwhals.translaterOrPrQcollections.abcrRrSrTtyping_extensionsrUrVrWnarwhals._translaterXrYrZr[r\r]r^narwhals._typingr_r`rarbrcrdrerfrgnarwhals.stable.v2.dtypesrhnarwhals.typingrirjrkrlrmrnrorprqrrrtrurr8r_rrrrrrrrrrrrrrrrrrrrrrrrrrrrrr rrrrrr__all__rrrrs """""""OOOOOOOOOOOOOOOOOO22222222......;;;;;;;;99999999                          RQQQQQQQ<877777((((((FFFFFFFFFF............>>>>>>>>>>                    UTTTTTTTTT';;;;;;;;;;9999999999HGGGGGGG//////                       S!!!A #A A}/}/}/}/}/ N+}/}/}/@FFFFF N+FFF$C<C<C<C<C