o ¹­§iw3ã@sôUddlmZddlmZmZmZddlmZddlm Z ddl m Z er7ddl mZddlmZmZmZmZgd¢Zejd d „e je je je jfDƒŽZd ed <ed ƒZGdd„deeƒZd dd„Zd!dd„Zd!dd„Zd!dd„Zd!dd„ZdS)"é)Ú annotations)Ú TYPE_CHECKINGÚGenericÚTypeVar)ÚwarnN)Úfind_stacklevel)ÚCallable)Ú DataFrameÚExprÚ LazyFrameÚSeries)Úregister_dataframe_namespaceÚregister_expr_namespaceÚregister_lazyframe_namespaceÚregister_series_namespaceccs|]}|jVqdS©N)Ú _accessors)Ú.0Úcls©rúA/home/app/Keep/.python/lib/python3.10/site-packages/polars/api.pyÚ s€rzset[str]Ú_reserved_namespacesÚNSc@s$eZdZdZddd „Zddd„ZdS)Ú NameSpacezHEstablish property-like namespace object for user-defined functionality.ÚnameÚstrÚ namespaceútype[NS]ÚreturnÚNonecCs||_||_dSr)Ú _accessorÚ_ns)ÚselfrrrrrÚ__init__"s zNameSpace.__init__Úinstanceú NS | Nonerú NS | type[NS]cCs*|dur|jS| |¡}t||j|ƒ|Sr)r"Úsetattrr!)r#r%rZ ns_instancerrrÚ__get__&s  zNameSpace.__get__N)rrrrrr )r%r&rrrr')Ú__name__Ú __module__Ú __qualname__Ú__doc__r$r)rrrrrs rrrrú+type[Expr | DataFrame | LazyFrame | Series]rúCallable[[type[NS]], type[NS]]csd‡‡fdd„ }|S)z>Register custom namespace against the underlying Polars class.Úns_classrrcsfˆtvr dˆ›}t|ƒ‚tˆˆƒr"tdˆ›dˆj›dttƒdtˆˆtˆ|ƒƒˆj   ˆ¡|S)Nz#cannot override reserved namespace z%Overriding existing custom namespace z (on ú))Ú stacklevel) rÚAttributeErrorÚhasattrrr*Ú UserWarningrr(rrÚadd)r0Úmsg©rrrrr4s  ý z$_create_namespace..namespaceN)r0rrrr)rrrrr8rÚ_create_namespace/sr9cCó t|tjƒS)uï Decorator for registering custom functionality with a Polars Expr. Parameters ---------- name Name under which the functionality will be accessed. See Also -------- register_dataframe_namespace : Register functionality on a DataFrame. register_lazyframe_namespace : Register functionality on a LazyFrame. register_series_namespace : Register functionality on a Series. Examples -------- >>> @pl.api.register_expr_namespace("pow_n") ... class PowersOfN: ... def __init__(self, expr: pl.Expr) -> None: ... self._expr = expr ... ... def next(self, p: int) -> pl.Expr: ... return (p ** (self._expr.log(p).ceil()).cast(pl.Int64)).cast(pl.Int64) ... ... def previous(self, p: int) -> pl.Expr: ... return (p ** (self._expr.log(p).floor()).cast(pl.Int64)).cast(pl.Int64) ... ... def nearest(self, p: int) -> pl.Expr: ... return (p ** (self._expr.log(p)).round(0).cast(pl.Int64)).cast(pl.Int64) >>> >>> df = pl.DataFrame([1.4, 24.3, 55.0, 64.001], schema=["n"]) >>> df.select( ... pl.col("n"), ... pl.col("n").pow_n.next(p=2).alias("next_pow2"), ... pl.col("n").pow_n.previous(p=2).alias("prev_pow2"), ... pl.col("n").pow_n.nearest(p=2).alias("nearest_pow2"), ... ) shape: (4, 4) ┌────────┬───────────┬───────────┬──────────────┠│ n ┆ next_pow2 ┆ prev_pow2 ┆ nearest_pow2 │ │ --- ┆ --- ┆ --- ┆ --- │ │ f64 ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡ │ 1.4 ┆ 2 ┆ 1 ┆ 1 │ │ 24.3 ┆ 32 ┆ 16 ┆ 32 │ │ 55.0 ┆ 64 ┆ 32 ┆ 64 │ │ 64.001 ┆ 128 ┆ 64 ┆ 64 │ └────────┴───────────┴───────────┴──────────────┘ )r9Úplr ©rrrrrFó 2rcCr:)uO Decorator for registering custom functionality with a Polars DataFrame. Parameters ---------- name Name under which the functionality will be accessed. See Also -------- register_expr_namespace : Register functionality on an Expr. register_lazyframe_namespace : Register functionality on a LazyFrame. register_series_namespace : Register functionality on a Series. Examples -------- >>> @pl.api.register_dataframe_namespace("split") ... class SplitFrame: ... def __init__(self, df: pl.DataFrame) -> None: ... self._df = df ... ... def by_first_letter_of_column_names(self) -> list[pl.DataFrame]: ... return [ ... self._df.select([col for col in self._df.columns if col[0] == f]) ... for f in dict.fromkeys(col[0] for col in self._df.columns) ... ] ... ... def by_first_letter_of_column_values(self, col: str) -> list[pl.DataFrame]: ... return [ ... self._df.filter(pl.col(col).str.starts_with(c)) ... for c in sorted( ... set(df.select(pl.col(col).str.slice(0, 1)).to_series()) ... ) ... ] >>> >>> df = pl.DataFrame( ... data=[["xx", 2, 3, 4], ["xy", 4, 5, 6], ["yy", 5, 6, 7], ["yz", 6, 7, 8]], ... schema=["a1", "a2", "b1", "b2"], ... orient="row", ... ) >>> df shape: (4, 4) ┌─────┬─────┬─────┬─────┠│ a1 ┆ a2 ┆ b1 ┆ b2 │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ xx ┆ 2 ┆ 3 ┆ 4 │ │ xy ┆ 4 ┆ 5 ┆ 6 │ │ yy ┆ 5 ┆ 6 ┆ 7 │ │ yz ┆ 6 ┆ 7 ┆ 8 │ └─────┴─────┴─────┴─────┘ >>> df.split.by_first_letter_of_column_names() [shape: (4, 2) ┌─────┬─────┠│ a1 ┆ a2 │ │ --- ┆ --- │ │ str ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ xx ┆ 2 │ │ xy ┆ 4 │ │ yy ┆ 5 │ │ yz ┆ 6 │ └─────┴─────┘, shape: (4, 2) ┌─────┬─────┠│ b1 ┆ b2 │ │ --- ┆ --- │ │ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 3 ┆ 4 │ │ 5 ┆ 6 │ │ 6 ┆ 7 │ │ 7 ┆ 8 │ └─────┴─────┘] >>> df.split.by_first_letter_of_column_values("a1") [shape: (2, 4) ┌─────┬─────┬─────┬─────┠│ a1 ┆ a2 ┆ b1 ┆ b2 │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ xx ┆ 2 ┆ 3 ┆ 4 │ │ xy ┆ 4 ┆ 5 ┆ 6 │ └─────┴─────┴─────┴─────┘, shape: (2, 4) ┌─────┬─────┬─────┬─────┠│ a1 ┆ a2 ┆ b1 ┆ b2 │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ yy ┆ 5 ┆ 6 ┆ 7 │ │ yz ┆ 6 ┆ 7 ┆ 8 │ └─────┴─────┴─────┴─────┘] )r9r;r r<rrrr {s _r cCr:)uµ Decorator for registering custom functionality with a Polars LazyFrame. Parameters ---------- name Name under which the functionality will be accessed. See Also -------- register_expr_namespace : Register functionality on an Expr. register_dataframe_namespace : Register functionality on a DataFrame. register_series_namespace : Register functionality on a Series. Examples -------- >>> @pl.api.register_lazyframe_namespace("types") ... class DTypeOperations: ... def __init__(self, lf: pl.LazyFrame) -> None: ... self._lf = lf ... ... def split_by_column_dtypes(self) -> list[pl.LazyFrame]: ... return [ ... self._lf.select(pl.col(tp)) ... for tp in dict.fromkeys(self._lf.collect_schema().dtypes()) ... ] ... ... def upcast_integer_types(self) -> pl.LazyFrame: ... return self._lf.with_columns( ... pl.col(tp).cast(pl.Int64) for tp in (pl.Int8, pl.Int16, pl.Int32) ... ) >>> >>> lf = pl.LazyFrame( ... data={"a": [1, 2], "b": [3, 4], "c": [5.6, 6.7]}, ... schema=[("a", pl.Int16), ("b", pl.Int32), ("c", pl.Float32)], ... ) >>> lf.collect() shape: (2, 3) ┌─────┬─────┬─────┠│ a ┆ b ┆ c │ │ --- ┆ --- ┆ --- │ │ i16 ┆ i32 ┆ f32 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 3 ┆ 5.6 │ │ 2 ┆ 4 ┆ 6.7 │ └─────┴─────┴─────┘ >>> lf.types.upcast_integer_types().collect() shape: (2, 3) ┌─────┬─────┬─────┠│ a ┆ b ┆ c │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ f32 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 3 ┆ 5.6 │ │ 2 ┆ 4 ┆ 6.7 │ └─────┴─────┴─────┘ >>> lf = pl.LazyFrame( ... data=[["xx", 2, 3, 4], ["xy", 4, 5, 6], ["yy", 5, 6, 7], ["yz", 6, 7, 8]], ... schema=["a1", "a2", "b1", "b2"], ... orient="row", ... ) >>> lf.collect() shape: (4, 4) ┌─────┬─────┬─────┬─────┠│ a1 ┆ a2 ┆ b1 ┆ b2 │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ xx ┆ 2 ┆ 3 ┆ 4 │ │ xy ┆ 4 ┆ 5 ┆ 6 │ │ yy ┆ 5 ┆ 6 ┆ 7 │ │ yz ┆ 6 ┆ 7 ┆ 8 │ └─────┴─────┴─────┴─────┘ >>> pl.collect_all(lf.types.split_by_column_dtypes()) [shape: (4, 1) ┌─────┠│ a1 │ │ --- │ │ str │ â•žâ•â•â•â•â•â•¡ │ xx │ │ xy │ │ yy │ │ yz │ └─────┘, shape: (4, 3) ┌─────┬─────┬─────┠│ a2 ┆ b1 ┆ b2 │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 2 ┆ 3 ┆ 4 │ │ 4 ┆ 5 ┆ 6 │ │ 5 ┆ 6 ┆ 7 │ │ 6 ┆ 7 ┆ 8 │ └─────┴─────┴─────┘] )r9r;r r<rrrrÝs brcCr:)a§ Decorator for registering custom functionality with a polars Series. Parameters ---------- name Name under which the functionality will be accessed. See Also -------- register_expr_namespace : Register functionality on an Expr. register_dataframe_namespace : Register functionality on a DataFrame. register_lazyframe_namespace : Register functionality on a LazyFrame. Examples -------- >>> @pl.api.register_series_namespace("math") ... class MathShortcuts: ... def __init__(self, s: pl.Series) -> None: ... self._s = s ... ... def square(self) -> pl.Series: ... return self._s * self._s ... ... def cube(self) -> pl.Series: ... return self._s * self._s * self._s >>> >>> s = pl.Series("n", [1.5, 31.0, 42.0, 64.5]) >>> s.math.square().alias("s^2") shape: (4,) Series: 's^2' [f64] [ 2.25 961.0 1764.0 4160.25 ] >>> s = pl.Series("n", [1, 2, 3, 4, 5]) >>> s.math.cube().alias("s^3") shape: (5,) Series: 's^3' [i64] [ 1 8 27 64 125 ] )r9r;r r<rrrrBr=r)rrrr.rr/)rrrr/) Ú __future__rÚtypingrrrÚwarningsrZpolars._reexportZ _reexportr;Zpolars._utils.variousrÚcollections.abcrZpolarsr r r r Ú__all__ÚsetÚunionrÚ__annotations__rrr9rr rrrrrrÚs&     ÿ   5 be