AHj'UddlmZddlZddlmZddlmZddlmZm Z m Z m Z ddl m Z ddlmZddlmZmZmZdd lmZdd lmZdd lmZdd lmZej8e5dd lmZm Z m!Z!ddder?ddl"Z"ddlm#Z#ddlm$Z$e"jJdk\rddlm&Z&nddl'm&Z&ddl(Z)ddl*m+Z+m,Z,ddl m-Z-nddl.m(Z)ddZ/ee0efZ1eeze zZ2de3d<dgZ4ddZ5ddZ6Gdde1Z7y#1swYxYw)) annotationsN) OrderedDict)Mapping) TYPE_CHECKINGAnyLiteraloverload)PythonDataType)unstable)DataType DataTypeClassis_polars_dtype)parse_into_dtype) unpack_dtypes)DuplicateError) CompatLevel)&init_polars_schema_from_arrow_c_schema'polars_schema_field_from_arrow_c_schemapolars_schema_to_pycapsule)Iterable) TypeAlias) )TypeIs DataFrame LazyFrame)ArrowSchemaExportable)pyarrowc,t|jSN)bool__annotations__)tps E/root/tools/cai/cai_env/lib/python3.12/site-packages/polars/schema.py_required_init_argsr&)s "" ##rSchemaInitDataTypeSchemact|tsB|js|js t |rd|}t ||}|S)Nz%dtypes must be fully-specified, got: ) isinstancer is_nested is_decimalr& TypeError)r$msgs r% _check_dtyper03sG b( # <<>R]]_0CB0G9"@CC. T Ir'ct|dS)N__arrow_c_schema__)hasattr)objs r%_is_arrow_schema_exportabler5=s 3, --r'ceZdZdZ ddd dfdZddZddZ dfd Zedd Z dd Z dd Z edd dd Z e ddZe ddddZddd dZd!dZd"dZd#dZxZS)$r)aA 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 NT) check_dtypesct|rt|ts t||yt|tr|j n|xsd}|D]o}t|r t |n|\}}||vrd|d}t||st|%||Ft|rt|%|t|k|||<qy)Nz7iterable passed to pl.Schema contained duplicate name '') r5r+r)rritemsrrsuper __setitem__rr0) selfschemar7inputvnamer$r/ __class__s r%__init__zSchema.__init__us 'v .z&&7Q 24 @  #-VW"= FLb A/q18: D" t|OPTvUVW$S))#D"- $#D,r*:;T ! r'ct|tsyt|t|k7ryt|j |j dD]#\\}}\}}||k7s|j |r#yy)NFT)strict)r+rlenzipr;is_)r>othernm1tp1nm2tp2s r%__eq__z Schema.__eq__sl%) t9E "&)$**, d&S  "JS# ccz r'c&|j| Sr!)rO)r>rJs r%__ne__z Schema.__ne__s;;u%%%r'cNtt|}t| ||yr!)r0rr<r=)r>rBdtyperCs r%r=zSchema.__setitem__s$-e45 D%(r'cRt|tjjS)z Export a Schema via the Arrow PyCapsule Interface. https://arrow.apache.org/docs/dev/format/CDataInterface/PyCapsuleInterface.html )rrnewest_versionr>s r%r2zSchema.__arrow_c_schema__s *$ 0B0B0D0M0MNNr'c4t|jS)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'] )listkeysrWs r%namesz Schema.namessDIIK  r'c4t|jS)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)] )rYvaluesrWs r%dtypesz Schema.dtypessDKKM""r') compat_levelcGdd}tj|||tjS|S)aI Convert the schema to a pyarrow schema. Parameters ---------- compat_level Use a specific compatibility level when exporting Polars' internal data types. Examples -------- >>> pl.Schema({"x": pl.String}).to_arrow() x: string_view ceZdZddZddZy).Schema.to_arrow..SchemaCapsuleProviderc ||_||_yr!)r?r_)r>r?r_s r%rDz7Schema.to_arrow..SchemaCapsuleProvider.__init__s$ $0!r'cVt|j|jjSr!)rr?r_rVrWs r%r2zASchema.to_arrow..SchemaCapsuleProvider.__arrow_c_schema__s$1KK!2!2!;!;r'N)r?r)r_rreturnNonereobject)__name__ __module__ __qualname__rDr2r9r'r%SchemaCapsuleProviderrbs  1 r'rl)par?rrU)r>r_rls r%to_arrowzSchema.to_arrowsL"  yy !l.Bk((*   HT   r'cyr!r9r>eagers r%to_framezSchema.to_frames?Br'.)rqcyr!r9rps r%rrzSchema.to_framesDGr'c:ddlm}m}|r ||S||S)u Create an empty DataFrame (or LazyFrame) from this Schema. Parameters ---------- eager If True, create a DataFrame; otherwise, create a LazyFrame. Examples -------- >>> s = pl.Schema({"x": pl.Int32(), "y": pl.String()}) >>> s.to_frame() shape: (0, 2) ┌─────┬─────┐ │ x ┆ y │ │ --- ┆ --- │ │ i32 ┆ str │ ╞═════╪═════╡ └─────┴─────┘ >>> s.to_frame(eager=False) # doctest: +IGNORE_RESULT rr)r?)polarsrr)r>rqrrs r%rrzSchema.to_frames. 0).y%JIT4JJr'ct|S)z Get the number of schema entries. Examples -------- >>> s = pl.Schema({"x": pl.Int32(), "y": pl.List(pl.String)}) >>> s.len() 2 >>> len(s) 2 )rGrWs r%rGz Schema.lens4yr'cr|jDcic]\}}||jc}}Scc}}w)a Return a dictionary of column names and Python types. Examples -------- >>> s = pl.Schema( ... { ... "x": pl.Int8(), ... "y": pl.String(), ... "z": pl.Duration("us"), ... } ... ) >>> s.to_python() {'x': , 'y': , 'z': } )r; to_python)r>rBr$s r%rxzSchema.to_pythons- 6:ZZ\Brblln$BBBs3c||s"tfd|jDSt|jvS)av Check if the schema contains the given data type. Parameters ---------- dtype The data type to search for. recursive If False, only check top-level column dtypes. If True, also search within nested types (List, Array, Struct). Examples -------- >>> s = pl.Schema({"x": pl.Int64(), "y": pl.List(pl.Float64)}) >>> s.contains_dtype(pl.Int64, recursive=False) True >>> s.contains_dtype(pl.Float64, recursive=False) False >>> s.contains_dtype(pl.Float64, recursive=True) True c3(K|] }|k( ywr!r9).0dtrSs r% z(Schema.contains_dtype..Gs;rrU{;s)anyr]r)r>rS recursives ` r%contains_dtypezSchema.contains_dtype0s5,;T[[];; ;M4;;=99 9r'r!)r?zMapping[str, SchemaInitDataType] | Iterable[tuple[str, SchemaInitDataType] | ArrowSchemaExportable] | ArrowSchemaExportable | Noner7r"rerf)rJrhrer")rBstrrSz)DataType | DataTypeClass | PythonDataTypererfrg)rez list[str])rezlist[DataType])r_zCompatLevel | Nonerez pa.Schema)rqzLiteral[False]rer)rqz Literal[True]rer)rqr"rezDataFrame | LazyFrame)reint)rezdict[str, type])rSr rr"rer")rirjrk__doc__rDrOrQr=r r2r[r^rnr rrrGrxr __classcell__)rCs@r%r)r)As1t # "#  # #  # J&)) I) ) ZOO ! #Z=A  @BB 14GG(,K6 C$:r')r$r rer")r$zDataType | DataTypeClassrer )r4rrezTypeIs[ArrowSchemaExportable])8 __future__r contextlib collectionsrcollections.abcrtypingrrrr polars._typingr polars._utils.unstabler polars.datatypesr r rpolars.datatypes._parserpolars.datatypes.convertrpolars.exceptionsrpolars.interchange.protocolrsuppress ImportError polars._plrrrrsysrr version_infortyping_extensionsrrmrurrrpolars._dependenciesr&r BaseSchemar(r#__all__r0r5r)r9r'r%rs"##88)+EE42,3Z%(  7"!,+42$h ' (= 8> III *.H:ZH:cs $ C--C6