-j*SSKJr SSKJrJrJr SSKJrJr SSK J r SSK J r \(aSSK Jr SSKJr SSKJr SS KJr "S S \ 5rSS jrSS jrSSjrSSjrSSjrSSjrSSjrSSjrSSSjjr/SQrg)) annotations) TYPE_CHECKINGAnyNoReturn)ExprKindExprNode)flatten)Expr)Iterable)timezone)DType)TimeUnitcZ\rSrSrS SjrS SjrS SjrS SjrS SjrS Sjr S Sjr S r g )Selectorc&[UR6$N)r _nodes)selfs ڠC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\narwhals/selectors.py_to_exprSelector._to_exprsT[[!!c [U[5(a Sn[U5eUR5R [ [ RSU4SS95$)Nz=unsupported operand type(s) for op: ('Selector' + 'Selector')__add__Texprs str_as_lit) isinstancer TypeErrorr _append_noderr ELEMENTWISE)rothermsgs rrSelector.__add__sN eX & &QCC. }}++ X))9UHQU V  rc [U[5(a+UR[[R SU4SSS95$UR 5R[[R SU4SS95$)N__or__Trrallow_multi_outputrrrr!rrr"rrr#s rr'Selector.__or__ss eX & &$$(( (#'+  }}++ X))8E8PT U  rc [U[5(a+UR[[R SU4SSS95$UR 5R[[R SU4SS95$)N__and__Tr(rr*r+s rr.Selector.__and__,ss eX & &$$(( (#'+  }}++ X))9UHQU V  rc[erNotImplementedErrorr+s r__rsub__Selector.__rsub__;!!rc[err1r+s r__rand__Selector.__rand__>r5rc[err1r+s r__ror__Selector.__ror__Ar5rN)returnr )r#rr=r )r#rr=r) __name__ __module__ __qualname____firstlineno__rrr'r.r3r7r:__static_attributes__r<rrrrs%"     """rrc\[U5n[[[RSUS95$)a&Select columns based on their dtype. Arguments: dtypes: one or data types to select Examples: >>> import pyarrow as pa >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> df_native = pa.table({"a": [1, 2], "b": ["x", "y"], "c": [4.1, 2.3]}) >>> df = nw.from_native(df_native) Let's select int64 and float64 dtypes and multiply each value by 2: >>> df.select(ncs.by_dtype(nw.Int64, nw.Float64) * 2).to_native() pyarrow.Table a: int64 c: double ---- a: [[2,4]] c: [[8.2,4.6]] zselectors.by_dtype)dtypes)r rrrSELECTOR)rD flatteneds rby_dtyperGEs(.I HX..0DYW XXrcF[[[RSUS95$)a7Select all columns that match the given regex pattern. Arguments: pattern: A valid regular expression pattern. Examples: >>> import pandas as pd >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> df_native = pd.DataFrame( ... {"bar": [123, 456], "baz": [2.0, 5.5], "zap": [0, 1]} ... ) >>> df = nw.from_native(df_native) Let's select column names containing an 'a', preceded by a character that is not 'z': >>> df.select(ncs.matches("[^z]a")).to_native() bar baz 0 123 2.0 1 456 5.5 zselectors.matchespatternrrrrErIs rmatchesrL`s, HX..0CWU VVrcH[[[RS55$)u~Select numeric columns. Examples: >>> import polars as pl >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> df_native = pl.DataFrame({"a": [1, 2], "b": ["x", "y"], "c": [4.1, 2.3]}) >>> df = nw.from_native(df_native) Let's select numeric dtypes and multiply each value by 2: >>> df.select(ncs.numeric() * 2).to_native() shape: (2, 2) ┌─────┬─────┐ │ a ┆ c │ │ --- ┆ --- │ │ i64 ┆ f64 │ ╞═════╪═════╡ │ 2 ┆ 8.2 │ │ 4 ┆ 4.6 │ └─────┴─────┘ zselectors.numericrKr<rrnumericrNys. HX..0CD EErcH[[[RS55$)u%Select boolean columns. Examples: >>> import polars as pl >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> df_native = pl.DataFrame({"a": [1, 2], "b": ["x", "y"], "c": [False, True]}) >>> df = nw.from_native(df_native) Let's select boolean dtypes: >>> df.select(ncs.boolean()) ┌──────────────────┐ |Narwhals DataFrame| |------------------| | shape: (2, 1) | | ┌───────┐ | | │ c │ | | │ --- │ | | │ bool │ | | ╞═══════╡ | | │ false │ | | │ true │ | | └───────┘ | └──────────────────┘ zselectors.booleanrKr<rrbooleanrPs6 HX..0CD EErcH[[[RS55$)uSelect string columns. Examples: >>> import polars as pl >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> df_native = pl.DataFrame({"a": [1, 2], "b": ["x", "y"], "c": [False, True]}) >>> df = nw.from_native(df_native) Let's select string dtypes: >>> df.select(ncs.string()).to_native() shape: (2, 1) ┌─────┐ │ b │ │ --- │ │ str │ ╞═════╡ │ x │ │ y │ └─────┘ zselectors.stringrKr<rrstringrRs. HX..0BC DDrcH[[[RS55$)uSelect categorical columns. Examples: >>> import polars as pl >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> df_native = pl.DataFrame({"a": [1, 2], "b": ["x", "y"], "c": [False, True]}) Let's convert column "b" to categorical, and then select categorical dtypes: >>> df = nw.from_native(df_native).with_columns( ... b=nw.col("b").cast(nw.Categorical()) ... ) >>> df.select(ncs.categorical()).to_native() shape: (2, 1) ┌─────┐ │ b │ │ --- │ │ cat │ ╞═════╡ │ x │ │ y │ └─────┘ zselectors.categoricalrKr<rr categoricalrTs2 HX..0GH IIrcH[[[RS55$)uCSelect enum columns. Examples: >>> import polars as pl >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> df_native = pl.DataFrame( ... {"a": [1, 2], "b": ["x", "y"]}, ... schema_overrides={"b": pl.Enum(["x", "y"])}, ... ) >>> df = nw.from_native(df_native) Let's select enum dtypes: >>> df.select(ncs.enum()).to_native() shape: (2, 1) ┌──────┐ │ b │ │ --- │ │ enum │ ╞══════╡ │ x │ │ y │ └──────┘ zselectors.enumrKr<rrenumrVs4 HX..0@A BBrcH[[[RS55$)aSelect all columns. Examples: >>> import pandas as pd >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> df_native = pd.DataFrame({"a": [1, 2], "b": ["x", "y"], "c": [False, True]}) >>> df = nw.from_native(df_native) Let's select all dtypes: >>> df.select(ncs.all()).to_native() a b c 0 1 x False 1 2 y True z selectors.allrKr<rrallrXs" HX..@ AArNc H[[[RSUUS95$)aASelect all datetime columns, optionally filtering by time unit/zone. Arguments: time_unit: One (or more) of the allowed timeunit precision strings, "ms", "us", "ns" and "s". Omit to select columns with any valid timeunit. time_zone: Specify which timezone(s) to select * One or more timezone strings, as defined in zoneinfo (to see valid options run `import zoneinfo; zoneinfo.available_timezones()` for a full list). * Set `None` to select Datetime columns that do not have a timezone. * Set `"*"` to select Datetime columns that have *any* timezone. Examples: >>> from datetime import datetime, timezone >>> import pyarrow as pa >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> >>> utc_tz = timezone.utc >>> data = { ... "tstamp_utc": [ ... datetime(2023, 4, 10, 12, 14, 16, 999000, tzinfo=utc_tz), ... datetime(2025, 8, 25, 14, 18, 22, 666000, tzinfo=utc_tz), ... ], ... "tstamp": [ ... datetime(2000, 11, 20, 18, 12, 16, 600000), ... datetime(2020, 10, 30, 10, 20, 25, 123000), ... ], ... "numeric": [3.14, 6.28], ... } >>> df_native = pa.table(data) >>> df_nw = nw.from_native(df_native) >>> df_nw.select(ncs.datetime()).to_native() pyarrow.Table tstamp_utc: timestamp[us, tz=UTC] tstamp: timestamp[us] ---- tstamp_utc: [[2023-04-10 12:14:16.999000Z,2025-08-25 14:18:22.666000Z]] tstamp: [[2000-11-20 18:12:16.600000,2020-10-30 10:20:25.123000]] Select only datetime columns that have any time_zone specification: >>> df_nw.select(ncs.datetime(time_zone="*")).to_native() pyarrow.Table tstamp_utc: timestamp[us, tz=UTC] ---- tstamp_utc: [[2023-04-10 12:14:16.999000Z,2025-08-25 14:18:22.666000Z]] zselectors.datetime time_unit time_zonerKrZs rdatetimer]s,h        r) rXrPrGrTr]rVrLrNrR)rDz3DType | type[DType] | Iterable[DType | type[DType]]r=r)rJstrr=r)r=r)N)*N)r[z$TimeUnit | Iterable[TimeUnit] | Noner\z7str | timezone | Iterable[str | timezone | None] | Noner=r) __future__rtypingrrrnarwhals._expression_parsingrrnarwhals._utilsr narwhals.exprr collections.abcr r]r narwhals.dtypesr narwhals.typingrrrGrLrNrPrRrTrVrX__all__r<rrris"//;#(!%(1"t1"hY6W2F4F<E4J8C:B*7;IT;3;F;;| r