h%SSKJr SSKrSSKrSSKJr SSKJr SSKJ r J r J r J r SSK Jr SSKJr SSKJrJrJr SS KJr SS KJr SS KJr \R6"\5 SS KJrJrJ r SSS5 \ (a1SS KJ!r! SSK"J#r#J$r$ SSK J%r% \RLS:aSSKJ'r' OSSK(J'r' \RLS:aSSjr)OSSjr)\\*\4r+\ \\\4r,S\-S'S/r.SSjr/"SS\+5r0g!,(df  N=f)) annotationsN) OrderedDict)Mapping) TYPE_CHECKINGLiteralUnionoverload)PythonDataType)unstable)DataType DataTypeClassis_polars_dtype)parse_into_dtype)DuplicateError) CompatLevel)&init_polars_schema_from_arrow_c_schema'polars_schema_field_from_arrow_c_schemapolars_schema_to_pycapsule)Iterable DataFrame LazyFrame)ArrowSchemaExportable) ) TypeAliasc,[UR5$N)bool__annotations__tps G/home/james-whalen/.local/lib/python3.13/site-packages/polars/schema.py_required_init_argsr$$sB&&''c SUR;$)N__init__)__dict__r!s r#r$r$*sR[[((r%rSchemaInitDataTypeSchemac[U[5(dRUR5(d%UR5(d[ U5(aSU<3n[ U5eU"5nU$)Nz%dtypes must be fully-specified, got: ) isinstancer is_nested is_decimalr$ TypeError)r"msgs r# _check_dtyper16sQ b( # # <<>>R]]__0CB0G0G9"@CC. T Ir%c^\rSrSrSrSSS.SU4SjjjjrSSjrSSjrSU4Sjjr\ "5SS j5r SS jr SS jr \ SS j5r\ S S.SSjj5rSS.SSjjrSSjrSSjrSrU=r$) r*@a Ordered mapping of column names to their data type. Parameters ---------- schema The schema definition given by column names and their associated Polars data type. Accepts a mapping, or an iterable of tuples, or any object implementing the `__arrow_c_schema__` PyCapsule interface (e.g. pyarrow schemas). Examples -------- Define a schema by passing instantiated data types. >>> schema = pl.Schema( ... { ... "foo": pl.String(), ... "bar": pl.Duration("us"), ... "baz": pl.Array(pl.Int8, 4), ... } ... ) >>> schema Schema({'foo': String, 'bar': Duration(time_unit='us'), 'baz': Array(Int8, shape=(4,))}) Access the data type associated with a specific column name. >>> schema["baz"] Array(Int8, shape=(4,)) Access various schema properties using the `names`, `dtypes`, and `len` methods. >>> schema.names() ['foo', 'bar', 'baz'] >>> schema.dtypes() [String, Duration(time_unit='us'), Array(Int8, shape=(4,))] >>> schema.len() 3 Import a pyarrow schema. >>> import pyarrow as pa >>> pl.Schema(pa.schema([pa.field("x", pa.int32())])) Schema({'x': Int32}) Export a schema to pyarrow. >>> pa.schema(pl.Schema({"x": pl.Int32})) x: int32 T) check_dtypesc>[US5(a![U[5(d [X5 g[U[5(aUR 5O U=(d SnUHn[US5(a [U[ 5(d [U5OUupVXP;aSUS3n[U5eU(d[TU])XV5 Mf[U5(a[TU])U[U55 MX`U'M g)N__arrow_c_schema__z7iterable passed to pl.Schema contained duplicate name '') hasattrr,r*rritemsr rrsuper __setitem__rr1) selfschemar4inputvnamer"r0 __class__s r#r'Schema.__init__ts 6/ 0 0FF9S9S 24 @ ",VW"="= FLbA1233Jq([[U55n[TU] X5 gr)r1rr;r<)r=rAdtyperBs r#r<Schema.__setitem__s"-e45 D(r%cT[U[R"5R5$)z Export a Schema via the Arrow PyCapsule Interface. https://arrow.apache.org/docs/dev/format/CDataInterface/PyCapsuleInterface.html )rrnewest_versionr=s r#r6Schema.__arrow_c_schema__s *$ 0B0B0D0M0MNNr%c4[UR55$)z Get the column names of the schema. Examples -------- >>> s = pl.Schema({"x": pl.Float64(), "y": pl.Datetime(time_zone="UTC")}) >>> s.names() ['x', 'y'] )listkeysrXs r#names Schema.namessDIIK  r%c4[UR55$)z Get the data types of the schema. Examples -------- >>> s = pl.Schema({"x": pl.UInt8(), "y": pl.List(pl.UInt8)}) >>> s.dtypes() [UInt8, List(UInt8)] )r[valuesrXs r#dtypes Schema.dtypessDKKM""r%cgrr7r=eagers r#to_frameSchema.to_frames?Br%.)recgrr7rds r#rfrgsDGr%c8SSKJnJn U(aU"US9$U"US9$)u Create an empty DataFrame (or LazyFrame) from this Schema. Parameters ---------- eager If True, create a DataFrame; otherwise, create a LazyFrame. Examples -------- >>> s = pl.Schema({"x": pl.Int32(), "y": pl.String()}) >>> s.to_frame() shape: (0, 2) ┌─────┬─────┐ │ x ┆ y │ │ --- ┆ --- │ │ i32 ┆ str │ ╞═════╪═════╡ └─────┴─────┘ >>> s.to_frame(eager=False) # doctest: +IGNORE_RESULT rr)r>)polarsrr)r=rerrs r#rfrgs. 0).y%JIT4JJr%c[U5$)z Get the number of schema entries. Examples -------- >>> s = pl.Schema({"x": pl.Int32(), "y": pl.List(pl.String)}) >>> s.len() 2 >>> len(s) 2 )rErXs r#rE Schema.lens4yr%ctUR5VVs0sHupXR5_M snn$s snnf)a4 Return a dictionary of column names and Python types. Examples -------- >>> s = pl.Schema( ... { ... "x": pl.Int8(), ... "y": pl.String(), ... "z": pl.Duration("us"), ... } ... ) >>> s.to_python() {'x': , 'y': , 'z': } )r: to_python)r=rAr"s r#rnSchema.to_pythons- 6:ZZ\B\lln$\BBBs4r7r)r>zMapping[str, SchemaInitDataType] | Iterable[tuple[str, SchemaInitDataType] | ArrowSchemaExportable] | ArrowSchemaExportable | Noner4rreturnNone)rHobjectrpr)rAstrrSz)DataType | DataTypeClass | PythonDataTyperprq)rprr)rpz list[str])rpzlist[DataType])rezLiteral[False]rpr)rez Literal[True]rpr)rerrpzDataFrame | LazyFrame)rpint)rpzdict[str, type])__name__ __module__ __qualname____firstlineno____doc__r'rMrPr<r r6r]rar rfrErn__static_attributes__ __classcell__)rBs@r#r*r*@s1t   "           D&)) I) ) ZOO ! #BB 14GG(,K6 CCr%)r"r rpr)r"zDataType | DataTypeClassrpr )1 __future__r contextlibsys collectionsrcollections.abcrtypingrrrr polars._typingr polars._utils.unstabler polars.datatypesr r rpolars.datatypes._parserpolars.exceptionsrpolars.interchange.protocolrsuppress ImportError polars._plrrrrrrjrrr version_infortyping_extensionsr$rs BaseSchemar)r __all__r1r*r7r%r#rs" ##::)+EE4,3%&(+4 7"$/w( ) h ' %h ~&M NIN *ICZICa&%s " C00 C>