h*"SSKJr SSKJr SSKJr SSKJr SSKJr SSKJr SSKJ r SSK J r SS K J r \(aSS KJr SS KJr SS KJr "S S\ 5rSSjrSSjrSSjrSSjrSSjrSSjrSSjrSSSjjr/SQrg)) annotations) TYPE_CHECKING)Any)Iterable)NoReturn) ExprMetadata)combine_metadata)Expr)flatten)timezone)DType)TimeUnitcZ\rSrSrS SjrS SjrS SjrS SjrS SjrS Sjr S Sjr S r g )SelectorcB[URUR5$N)r _to_compliant_expr _metadata)selfs D/var/www/html/env/lib/python3.13/site-packages/narwhals/selectors.py_to_exprSelector._to_exprsD++T^^<<cl[U[5(a Sn[U5eUR5U-$)Nz=unsupported operand type(s) for op: ('Selector' + 'Selector')) isinstancer TypeErrorr)rothermsgs r__add__Selector.__add__s/ eX & &QCC. }}&&rc ^^[T[5(a"TRUU4Sj[TTSSSS95$TR 5T-$)NcJ>TRU5TRU5-$rrplxrrs r!Selector.__or__..!!D33C85;S;STW;XXrFT str_as_litallow_multi_outputto_single_outputrr __class__r rrrs``r__or__Selector.__or__N eX & &>>X $'+%*   }}&&rc ^^[T[5(a"TRUU4Sj[TTSSSS95$TR 5T-$)NcJ>TRU5TRU5-$rr$r%s rr'"Selector.__and__../r)rFTr*r.r0s``r__and__Selector.__and__,r3rc[erNotImplementedErrorr0s r__rsub__Selector.__rsub__:!!rc[err:r0s r__rand__Selector.__rand__=r>rc[err:r0s r__ror__Selector.__ror__@r>rN)returnr )rrrFr )rrrFr) __name__ __module__ __qualname____firstlineno__rr r1r7r<r@rC__static_attributes__rErrrrs%=' ' '"""rrc`^[U5m[U4Sj[R"55$)aFSelect columns based on their dtype. Arguments: dtypes: one or data types to select Returns: A new expression. 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]] c:>URRT5$r) selectorsby_dtype)r& flatteneds rr'by_dtype..`sCMM**95r)r rrselector_multi_unnamed)dtypesrPs @rrOrODs*4I 5++- rcJ^[U4Sj[R"55$)aSelect all columns that match the given regex pattern. Arguments: pattern: A valid regular expression pattern. Returns: A new expression. 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 c:>URRT5$r)rNmatches)r&patterns rr'matches..sCMM))'2rrrrR)rWs`rrVrVes!: 2++- rcB[S[R"55$)uSelect numeric columns. Returns: A new expression. 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 │ └─────┴─────┘ c6URR5$r)rNnumericr&s rr'numeric..CMM))+rrYrErrr\r\s4 +\-P-P-R rcB[S[R"55$)uESelect boolean columns. Returns: A new expression. 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 │ | | └───────┘ | └──────────────────┘ c6URR5$r)rNbooleanr]s rr'boolean..r_rrYrErrrbrbs< +\-P-P-R rcB[S[R"55$)uSelect string columns. Returns: A new expression. 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 │ └─────┘ c6URR5$r)rNstringr]s rr'string..sCMM((*rrYrErrrfrfs4 *L,O,O,Q rcB[S[R"55$)uSelect categorical columns. Returns: A new expression. 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 │ └─────┘ c6URR5$r)rN categoricalr]s rr'categorical..sCMM--/rrYrErrrjrjs 8 /++- rcB[S[R"55$)aSelect all columns. Returns: A new expression. 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 c6URR5$r)rNallr]s rr'all.. sCMM%%'rrYrErrrnrn s( ')L)L)N rNcN^^[UU4Sj[R"55$)abSelect 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. Returns: A new expression. 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]] c8>URRTTS9$)N time_unit time_zone)rNdatetime)r&rsrts rr'datetime..\sCMM**Y)*TrrYrrs``rruru$s"n T++- r)rnrbrOrjrurVr\rf)rSz3DType | type[DType] | Iterable[DType | type[DType]]rFr)rWstrrFr)rFr)N)*N)rsz$TimeUnit | Iterable[TimeUnit] | Nonertz7str | timezone | Iterable[str | timezone | None] | NonerFr) __future__rtypingrrrrnarwhals._expression_parsingrr narwhals.exprr narwhals.utilsr rur narwhals.dtypesr narwhals.typingrrrOrVr\rbrfrjrn__all__rErrrs" 59"!%(-"t-"`B F> F>D47;IT:3:F::z r