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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)rrrreager_or_interchange_onlyversion)rrrrBrNrr~r rrrrs,.rnarwhals_objectrcCrrrr'rrrr to_nativerr)cCrrrr(rrrr)rcCrrrr(rrrr)rcCrrrr(rrrr)r3IntoDataFrameT | IntoLazyFrameT | IntoSeriesT | AnycCstj||dS)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(rrrr)sTfuncCallable[..., Any] | NoneCallable[..., Any]cs(dfdd }|dur|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. r,r.rxcs"tdfdd }|S)Nargsrrrxcsfdd|D}fdd|D}fddg||RD}|dkr6d}t||i|}t|d S) Nc sg|] }t|dqSr!r).0argrrrrrr 7szBnarwhalify..decorator..wrapper..c s$i|]\}}|t|dqSr0r1)r2rvaluer4rr BszBnarwhalify..decorator..wrapper..cs"h|] }t|ddrqS)__native_namespace__N)getattr)r2vbrr Ms zAnarwhalify..decorator..wrapper..z_Found multiple backends. Make sure that all dataframe/series inputs come from the same backend.r&)itemsr__len__ ValueErrorr))r/rbackendsrr)rrr,rrr;rwrapper5s    z.narwhalify..decorator..wrapper)r/rrrrxrr)r,rCr4)r,r decorator4s%znarwhalify..decoratorN)r,r.rxr.r)r,rrrrrDrr4r narwhalify s))rEcC ttS)z3Instantiate an expression representing all columns.)rr+allrrrrrGc rGnamesstr | Iterable[str]cGttj|S)zCreates an expression that references one or more columns by their name(s). Arguments: names: Name(s) of the columns to use. )rr+colrIrrrrLhrLcGrK)zCreates an expression that excludes columns by their name(s). Arguments: names: Name(s) of the columns to exclude. )rr+excluderMrrrrOqrNrOindicesint | Sequence[int]cGrK)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)rPrrrrRz rRcCrF)zReturn the number of rows.)rr+lenrrrrrTrHrTr6rkrrcCstt||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. )rr+lit)r6rrrrrUsrUcolumnsrcGrK)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+minrVrrrrW rWcGrK)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+maxrXrrrrZrYrZcGrK)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+meanrXrrrr[rYr[cGrK)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+medianrXrrrr\s r\cGrK)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+sumrXrrrr]rYr]exprsIntoExpr | Iterable[IntoExpr]cGrK)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_horizontalr^rrrr`rSr`rcGttj|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_horizontalrr^rrrrc rccGrb)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_horizontalrdrrrrfrerfcGrK)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_horizontalrarrrrgsrgcGrK)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_horizontalrarrrrh rSrhcGrK)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_horizontalrarrrrirSri separatorr more_exprsrirlcGsttj|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. rk)rr+ concat_str)r^rlrrmrrrrn&srnf_stringr/cGttj|g|RS)zFormat expressions as a string. Arguments: f_string: A string that with placeholders. args: Expression(s) that fill the placeholders. )rr+format)ror/rrrrq>srqcGrp)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)r^rmrrrrrHs rrc,eZdZed ddZd fd d ZZS) Whenwhen nw_f.WhenrxcCs ||jSr) _predicate)rrurrr from_whenYrzWhen.from_whenr6&IntoExpr | NonNestedLiteral | _1DArrayThencstt|Sr)rz from_thenrthenrr6rrrr|]sz When.then)rurvrxrt)r6ryrxrz)rrrrrxr|rrrrrrtX rtcrs) rzr| nw_f.ThenrxcCs ||jSr)r )rr|rrrr{brzThen.from_thenr6ryr=cstt|Sr)rr otherwiser}rrrrfszThen.otherwise)r|rrxrz)r6ryrxr=)rrrrr{rrrrrrrzar~rz predicatescGsttj|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. )rtrxnw_fru)rrrrrujsrurrrrrcCstt||||dS)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>)rrrrrrr new_seriessrrr[rcCsttj||dS)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)rrrrrrsrrrrr#Mapping[str, DType] | Schema | NonercCttj|||dS)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)rrrrrrrrrrsrrrrprcCr)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)rrrrrrrrsrsourcercKttj|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_csvrrrrrrrrIntoBackend[Backend]rcKr)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_csvrrrrrsrcKr)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_parquetrrrrrrrcKr)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. 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