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Arguments: n: Number of rows to return. Returns: A new expression. cDj|jSr)rheadrrrs rzExpr.head..b//499!<rr _metadatawith_kind_and_closeable_windowr FILTRATIONrrs``rr z Expr.headX1~~ < NN 9 9(:M:M N  rcjfdjjtjS)zGet the last `n` rows. Arguments: n: Number of rows to return. Returns: A new expression. cDj|jSr)rrrs rrzExpr.tail..prrrrs``rrz Expr.tailfrrcjfdjjtjS)zTake every nth value in the Series and return as new Series. Arguments: n: Gather every *n*-th row. offset: Starting index. Returns: A new expression. cHj|jS)Nr)rr)rrrrs rrz#Expr.gather_every..s"//4AAAfAUrrrs```rrzExpr.gather_everyts1~~ U NN 9 9(:M:M N  rN)maintain_orderc|d}t|ttjfdjj t jS)aTReturn unique values of this expression. Arguments: maintain_order: Keep the same order as the original expression. This is deprecated and will be removed in a future version, but will still be kept around in `narwhals.stable.v1`. 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Returns: A new expression. cLj|jS)Nr+)rsample)rr,rr.rr-s rrzExpr.sample..s,//4;;H7Gd<r)rrr!rr)rrr,r-r.s`````rr1z Expr.samples5(~~  NN $ $X%8%8 9   rr) rrr)r bool | Nonerr^)r#rr$rrr^r) rrr,z float | Noner-rr.rrr^) rrrrr rrrrr*r1rrs@rrrTs|'      7; ,*/5 "   "&!&           rrcXeZdZejZee d dfd ZxZ S)r'c$t||yrr)rschemars rrzSchema.__init__s  rr)r6z8Mapping[str, DType] | Iterable[tuple[str, DType]] | Nonerr) rrrrMV1_versionrRNwSchemarrrs@rr'r's6zzHQU!N! !!rr'cyrrobjs r _stableifyr=GJrcyrrr;s rr=r=r>rcyrrr;s rr=r=CFrcyrrr;s rr=r=s%(rcyrrr;s rr=r=s!$rc:t|tr>t|jj t j |jSt|tr>t|jj t j |jSt|tr>t|jj t j |jSt|tr t|j|j S|Sr})rrrr _with_versionrMr7_levelrrrr)_compliant_seriesNwExprrrrr;s rr=r=s#{#--;;GJJGszzZZ#{#--;;GJJGszzZZ#x c++99'**ESZZXX#vC**CMM:: Jrc yrr native_objectkwdss r from_nativerMsADrc yrrrJs rrMrMr>rc yrrrJs rrMrMr>rc yrrrJs rrMrMs"r.) eager_only series_onlycyrrrKstrictrQeager_or_interchange_onlyrR allow_seriess rrMrM 7:r)rVrRcyrrrTs rrMrMrXr)rQrRrWcyrrrTs rrMrM#!$rcyrrrTs rrMrM/ r)rVrRrWcyrrrTs rrMrM;r[rcyrrrTs rrMrMGr]r)rQrVrRcyrrrTs rrMrMS KNr)rQrVrWcyrrrTs rrMrM_r)rQrVrRrWcyrrrTs rrMrMk58rcyrrrTs rrMrMwr]r)rUrQrRrWcyrrrTs rrMrMr[r)rUrVrRrWcyrrrTs rrMrMr[r)rUrQrVrRcyrrrTs rrMrMrer)rUrQrVrWcyrrrTs rrMrMrcr)rUrQrVrRrWcyrrrTs rrMrMr[rcyrrrTs rrMrMrercyrrrK pass_throughrQrVrRrWs rrMrMr[rcyrrrns rrMrMrXrcyrrrns rrMrMr[rcyrrrns rrMrMr]rcyrrrns rrMrMr[rcyrrrns rrMrMr]rcyrrrns rrMrMrarcyrrrns rrMrM rcrcyrrrns rrMrM,rercyrrrns rrMrM8r]r)rorQrRrWcyrrrns rrMrMDr[r)rorVrRrWcyrrrns rrMrMPr[r)rorQrVrRcyrrrns rrMrM\rer)rorQrVrWcyrrrns rrMrMhrcrrorQrVrRrWcyrrrns rrMrMtrerF)rVcyrrrns rrMrMs r)rUrorQrVrRrWc t|ttfr|s|St|tr|s|r|St ||dd}|r"dt t |}t|t||||||tj} t| S)a Convert `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__` strict: Determine what happens if the object can't be converted to Narwhals: - `True` or `None` (default): raise an error - `False`: pass object through as-is **Deprecated** (v1.13.0): Please use `pass_through` instead. Note that `strict` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). pass_through: Determine what happens if the object can't be converted to Narwhals: - `False` or `None` (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 eager_or_interchange_only: Whether to only allow eager objects or objects which have interchange-level support in Narwhals: - `False` (default): don't require `native_object` to either be eager or to have interchange-level support in Narwhals - `True`: only convert to Narwhals if `native_object` is eager or has interchange-level support in Narwhals See [interchange-only support](../extending.md/#interchange-only-support) for more details. 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. Fpass_through_defaultemit_deprecation_warningz1from_native() got an unexpected keyword argument )rorQrVrRrWversion) rrrr)rZnextiterrrGrMr7r=) rKrUrorQrVrRrWrLrresults rrMrMsz-)Y!78-(k\2 5SXL A$tDzBRAUVn !";! F f r)rUcyrrnarwhals_objectrUs r to_nativerrcyrrrs rrrrcyrrrs rrrrcyrrrs rrrs=@rrocyrrrros rrrrrcyrrrs rrrrrcyrrrs rrrrrcyrrrs rrrrAr)rUrocddlm}ddlm}ddlm}|||dd}t ||r|jjSt ||r|jjS|sdt|d}t||S) a<Convert Narwhals object to native one. Arguments: narwhals_object: Narwhals object. strict: Determine what happens if `narwhals_object` isn't a Narwhals class: - `True` (default): raise an error - `False`: pass object through as-is **Deprecated** (v1.13.0): Please use `pass_through` instead. Note that `strict` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). 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. rrr(rYFrzExpected Narwhals object, got .) rrrr)rrZrr _native_framerGnativerr)rrUrorr)rZrs rrrs:-&>2 5SXL/9-//===/6*00777 .tO/D.EQGn rTcVt|dddfd }||S||S)a Decorate 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. strict: **Deprecated** (v1.13.0): Please use `pass_through` instead. Note that `strict` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). Determine what happens if the object can't be converted to Narwhals: - `True` or `None` (default): raise an error - `False`: pass object through as-is pass_through: Determine what happens if the object can't be converted to Narwhals: - `False` or `None` (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 eager_or_interchange_only: Whether to only allow eager objects or objects which have interchange-level support in Narwhals: - `False` (default): don't require `native_object` to either be eager or to have interchange-level support in Narwhals - `True`: only convert to Narwhals if `native_object` is eager or has interchange-level support in Narwhals See [interchange-only support](../extending.md/#interchange-only-support) for more details. 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. TFrc<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)rMitemsvaluesgetattr__len__ ValueErrorr)argsrrrvaluevbbackendsrrrWrQrVfuncrorRs rwrapperz.narwhalify..decorator..wrapperrs  !-).G +!-  D *$*<<>  D%k!-).G +!-  F 342&--/2 $:DAAAAH !A%w o%4*6*FV,? ?I  sB=C)C)rrrrrrr)rrrWrQrVrorRs` r decoratorznarwhalify..decoratorqs) t% @% @ % @Nr)rCallable[..., Any]rrrY)rrUrorQrVrRrWrs ````` r narwhalifyr0s>z3 4RWL))V |rc<ttjS)z`Instantiate an expression representing all columns. Returns: A new expression. )r=nwrrrrrr 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. Returns: A new expression. )r=rrnamess rrrs bffen %%rc8ttj|S)zCreates an expression that excludes columns by their name(s). Arguments: names: Name(s) of the columns to exclude. Returns: A new expression. )r=rexcluders rrrs bjj%( ))rc8ttj|S)aICreates 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. Returns: A new expression. )r=rnth)indicess rrrs bffg& ''rc<ttjS)zGReturn the number of rows. Returns: A new expression. )r=rlenrrrrrrrc@ttj||S)a!Return 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. Returns: A new expression. )r=rlit)rdtypes rrrs 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. Returns: A new expression. )r=rmincolumnss rrr 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. Returns: A new expression. )r=rmaxrs 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 Returns: A new expression. )r=rmeanrs rrr s bggw' ((rc8ttj|S)atGet 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 Returns: A new expression. )r=rmedianrs rrrs 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 Returns: A new expression. )r=rsumrs rrr)rrc8ttj|S)a2Sum 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. Returns: A new expression. )r=rsum_horizontalexprss rrr8 b''/ 00rc8ttj|S)zCompute the bitwise AND horizontally across columns. Arguments: exprs: Name(s) of the columns to use in the aggregation function. Accepts expression input. Returns: A new expression. )r=rall_horizontalrs rrrH b''/ 00rc8ttj|S)zCompute the bitwise OR horizontally across columns. Arguments: exprs: Name(s) of the columns to use in the aggregation function. Accepts expression input. Returns: A new expression. )r=rany_horizontalrs rrrUrrc8ttj|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. Returns: A new expression. )r=rmean_horizontalrs rrrbs b((%0 11rc8ttj|S)a*Get 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. Returns: A new expression. )r=rmin_horizontalrs rrrorrc8ttj|S)a*Get 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. Returns: A new expression. )r=rmax_horizontalrs rrrrrverticalhowc Vtdttj||S)aConcatenate multiple DataFrames, LazyFrames into a single entity. Arguments: items: DataFrames, LazyFrames to concatenate. how: concatenating strategy: - vertical: Concatenate vertically. Column names must match. - horizontal: Concatenate horizontally. If lengths don't match, then missing rows are filled with null values. This is only supported when all inputs are (eager) DataFrames. - diagonal: Finds a union between the column schemas and fills missing column values with null. Returns: A new DataFrame or LazyFrame resulting from the concatenation. Raises: TypeError: The items to concatenate should either all be eager, or all lazy rnr)r r=rconcat)rrs rrrs!( *RYYu#%>? @@r separator ignore_nullscHttj|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. Returns: A new expression. r)r=r concat_str)rrr more_exprss rrrs),  eYjYILY rc2eZdZeddZdfd ZxZS)rc&||jSr) _predicate)clsr&s r from_whenzWhen.from_whens4??##rcHtjt| |Sr)r from_thenrthenrrrs rrz When.thens~~egl5122r)r&NwWhenrr)r&IntoExpr | NonNestedLiteral | _1DArrayrr)rrr classmethodrrrrs@rrrs$$33rrc2eZdZeddZdfd ZxZS)rc<||j|jSr)rr)rrs rrzThen.from_thens4**DNN;;rc4tt| |Sr)r=r otherwisers rrzThen.otherwises%'+E233r)rNwThenrr)rrrr)rrrrrrrrs@rrrs<<44rrc8tjt|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_when) predicatess rr&r&s, >>':. //r)required)rnative_namespacec htd|}tt||||tjS)a_Instantiate 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`. native_namespace: The native library to use for DataFrame creation. **Deprecated** (v1.31.0): Please use `backend` instead. Note that `native_namespace` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). Returns: A new Series !ModuleType | Implementation | strrr)r r=rrMr7)rrrrrs r new_seriesrs3D6@G vugwzzR rcdtd|}tt||tjS)aConstruct 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`. native_namespace: The native library to use for DataFrame creation. **Deprecated** (v1.31.0): Please use `backend` instead. Note that `native_namespace` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). Returns: A new DataFrame. rr)r r=rrMr7) native_framerrs r from_arrowrs.:6@G w K rcNtt|||tjS)aInstantiate DataFrame from dictionary. Indexes (if present, for pandas-like backends) are aligned following the [left-hand-rule](../pandas_like_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`. native_namespace: The native library to use for DataFrame creation. **Deprecated** (v1.26.0): Please use `backend` instead. Note that `native_namespace` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). Returns: A new DataFrame. r)r=rrMr7datar6rrs r from_dictr 7s$P fgwzzJ rcftd|}tt|||tjS)aConstruct 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`. native_namespace: The native library to use for DataFrame creation. **Deprecated** (v1.31.0): Please use `backend` instead. Note that `native_namespace` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). Returns: A new DataFrame. rr)r r=rrMr7rs r from_numpyr ds-J6@G &tVWgjjY ZZrc Htd|}tt|fd|i|S)aYRead 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`. native_namespace: The native library to use for DataFrame creation. **Deprecated** (v1.27.2): Please use `backend` instead. Note that `native_namespace` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). 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')`. Returns: DataFrame. rr)r r=r sourcerrrs rread_csvrs1@6@G v9w9&9 rc Htd|}tt|fd|i|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`. native_namespace: The native library to use for DataFrame creation. **Deprecated** (v1.31.0): Please use `backend` instead. Note that `native_namespace` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). 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')`. Returns: LazyFrame. rr)r r=r"r s rscan_csvrs1F6@G v9w9&9 rc Htd|}tt|fd|i|S)ahRead 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`. native_namespace: The native library to use for DataFrame creation. **Deprecated** (v1.31.0): Please use `backend` instead. Note that `native_namespace` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). 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')`. Returns: DataFrame. rr)r r=r!r s r read_parquetrs1@6@G 6=7=f= rc Htd|}tt|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`. native_namespace: The native library to use for DataFrame creation. **Deprecated** (v1.31.0): Please use `backend` instead. Note that `native_namespace` is still available (and won't emit a deprecation warning) if you use `narwhals.stable.v1`, see [perfect backwards compatibility policy](../backcompat.md/). 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')`. 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