j%UddlmZddlZddlmZddlmZddlmZm Z m Z ddl m Z ddl mZddlmZmZmZdd lmZdd lmZdd lmZdd lmZeje5dd lmZmZm Z dddn #1swxYwYerddlm!Z!ddlm"Z"ddl#Z$ddl%m&Z&m'Z'ddl m(Z(nddl)m#Z$ddZ*ee+efZ,eeze zZ-de.d<dgZ/d dZ0Gdde,Z1dS)!) annotationsN) OrderedDict)Mapping) TYPE_CHECKINGLiteraloverload)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 DataFrame LazyFrame)ArrowSchemaExportable)pyarrowtpr returnboolc*t|jSN)r__annotations__)rs R/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/polars/schema.py_required_init_argsr##s " # ##rSchemaInitDataTypeSchemaDataType | DataTypeClassr ct|tsU|s#|st |rd|}t ||}|S)Nz%dtypes must be fully-specified, got: ) isinstancer is_nested is_decimalr# TypeError)rmsgs r" _check_dtyper.-sl b( # # <<>> !R]]__ !0CB0G0G !@"@@CC.. RTT Ir$c eZdZdZ d1ddd2fd Zd3dZd3dZd4fd Zed5dZ d6dZ d7dZ eddd8dZ e d9d"Ze d#d$d:d'Zdd$d;d)Zdd0ZxZS)?r&aA Ordered mapping of column names to their data type. Parameters ---------- schema The schema definition given by column names and their associated Polars data type. Accepts a mapping, or an iterable of tuples, or any object implementing the `__arrow_c_schema__` PyCapsule interface (e.g. pyarrow schemas). Examples -------- Define a schema by passing instantiated data types. >>> schema = pl.Schema( ... { ... "foo": pl.String(), ... "bar": pl.Duration("us"), ... "baz": pl.Array(pl.Int8, 4), ... } ... ) >>> schema Schema({'foo': String, 'bar': Duration(time_unit='us'), 'baz': Array(Int8, shape=(4,))}) Access the data type associated with a specific column name. >>> schema["baz"] Array(Int8, shape=(4,)) Access various schema properties using the `names`, `dtypes`, and `len` methods. >>> schema.names() ['foo', 'bar', 'baz'] >>> schema.dtypes() [String, Duration(time_unit='us'), Array(Int8, shape=(4,))] >>> schema.len() 3 Import a pyarrow schema. >>> import pyarrow as pa >>> pl.Schema(pa.schema([pa.field("x", pa.int32())])) Schema({'x': Int32}) Export a schema to pyarrow. >>> pa.schema(pl.Schema({"x": pl.Int32})) x: int32 NT) check_dtypesschemaMapping[str, SchemaInitDataType] | Iterable[tuple[str, SchemaInitDataType] | ArrowSchemaExportable] | ArrowSchemaExportable | Noner0rrNonecPt|dr't|tst||dSt|tr|n|pd}|D]}t|dr$t|t st|n|\}}||vrd|d}t||s#t ||yt|r0t |t||||<dS)N__arrow_c_schema__z7iterable passed to pl.Schema contained duplicate name '') hasattrr)r&rritemsr rrsuper __setitem__r r.) selfr1r0inputvnamerr- __class__s r"__init__zSchema.__init__ksK 6/ 0 0 FF9S9S  24 @ @ @ F",VW"="=Q FLb  A1233>> s = pl.Schema({"x": pl.Float64(), "y": pl.Datetime(time_zone="UTC")}) >>> s.names() ['x', 'y'] )listkeysrWs r"namesz Schema.namessDIIKK   r$list[DataType]cDt|S)z Get the data types of the schema. Examples -------- >>> s = pl.Schema({"x": pl.UInt8(), "y": pl.List(pl.UInt8)}) >>> s.dtypes() [UInt8, List(UInt8)] )rZvaluesrWs r"dtypesz Schema.dtypessDKKMM"""r$) compat_levelraCompatLevel | None pa.SchemacGdd}tj|||tjn|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 ceZdZd dZd d Zd S) .Schema.to_arrow..SchemaCapsuleProviderr1r&rarrr3c"||_||_dSr )r1ra)r<r1ras r"rAz7Schema.to_arrow..SchemaCapsuleProvider.__init__s$ $0!!!r$rCc@t|j|jjSr )rr1rarVrWs r"r5zASchema.to_arrow..SchemaCapsuleProvider.__arrow_c_schema__s!1K!2!;r$N)r1r&rarrr3rrC)__name__ __module__ __qualname__rAr5r6r$r"SchemaCapsuleProviderrfs< 1 1 1 1      r$rm)par1rrU)r<rarms r"to_arrowzSchema.to_arrowsh"        y ! !l.Bk(***      r$eagerLiteral[False]rcdSr r6r<rps r"to_framezSchema.to_frames?Bsr$.)rp Literal[True]rcdSr r6rss r"rtzSchema.to_framesDGCr$DataFrame | LazyFramecFddlm}m}|r ||n ||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)r1)polarsrr)r<rprrs r"rtzSchema.to_framesF. 0///////).Jyy%%%%IIT4J4J4JJr$intc t|S)z Get the number of schema entries. Examples -------- >>> s = pl.Schema({"x": pl.Int32(), "y": pl.List(pl.String)}) >>> s.len() 2 >>> len(s) 2 )rFrWs r"rFz Schema.lens4yyr$dict[str, type]c>d|DS)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': } c>i|]\}}||Sr6) to_python).0r?rs r" z$Schema.to_python..!s&BBBrbllnnBBBr$)r9rWs r"rzSchema.to_pythons! CBTZZ\\BBBBr$r recursivec|s-tfd|DSt|vS)av Check if the schema contains the given data type. Parameters ---------- dtype The data type to search for. recursive If False, only check top-level column dtypes. If True, also search within nested types (List, Array, Struct). Examples -------- >>> s = pl.Schema({"x": pl.Int64(), "y": pl.List(pl.Float64)}) >>> s.contains_dtype(pl.Int64, recursive=False) True >>> s.contains_dtype(pl.Float64, recursive=False) False >>> s.contains_dtype(pl.Float64, recursive=True) True c3$K|] }|kV dSr r6)rdtrQs r" z(Schema.contains_dtype..:s';;rrU{;;;;;;r$)anyr_r)r<rQrs ` r"contains_dtypezSchema.contains_dtype#sP, :;;;;T[[]];;;;; ;M4;;==99 9r$r )r1r2r0rrr3)rBrCrr)r?rPrQrRrr3ri)rrX)rr])rarbrrc)rprqrr)rprurr)rprrrw)rrz)rr|)rQr rrrr)rjrkrl__doc__rArMrOr;r r5r\r`rorrtrFrr __classcell__)r@s@r"r&r&7s11t   "         D&&&&)))))) XZZOOOZO ! ! ! ! # # # #XZZ=A     Z @BBBXB 14GGGGGXG(,KKKKKK6    CCCC$::::::::r$)rr rr)rr'rr )2 __future__r contextlib collectionsrcollections.abcrtypingrrrpolars._typingr polars._utils.unstabler polars.datatypesr r r polars.datatypes._parserpolars.datatypes.convertrpolars.exceptionsrpolars.interchange.protocolrsuppress ImportError polars._plrrrrrrrrnryrrrpolars._dependenciesr#rP BaseSchemar%r!__all__r.r&r6r$r"rs"""""""############3333333333))))))++++++EEEEEEEEEE444444222222,,,,,,333333Z%% 3((((((      ++++++++4444444222222$$$$h ' (= 8> IIIII *E:E:E:E:E:ZE:E:E:E:E:s A77A;>A;