zhQdZddlmZddlmZddlmZddlmZddlm Z ddlm Z ddlm Z dd l m Z dd l mZdd l mZer(dd lmZdd lmZddlZddlZddlmZddlmZeeefZneZdgZGddeZy)zxSchema. Adapted from Polars implementation at: https://github.com/pola-rs/polars/blob/main/py-polars/polars/schema.py. ) annotations) OrderedDict)partial) TYPE_CHECKING)Iterable)Mapping)cast)Implementation)Version) parse_version)Any)ClassVarN)DType) DTypeBackendSchemaceZdZUdZej Zded< d d fd Zd dZ ddZ ddZ ddZ d dd Z dd ZxZS)raOrdered mapping of column names to their data type. Arguments: schema: Mapping[str, DType] | Iterable[tuple[str, DType]] | None The schema definition given by column names and their associated. *instantiated* Narwhals data type. Accepts a mapping or an iterable of tuples. Examples: Define a schema by passing *instantiated* data types. >>> import narwhals as nw >>> schema = nw.Schema({"foo": nw.Int8(), "bar": nw.String()}) >>> schema Schema({'foo': Int8, 'bar': String}) Access the data type associated with a specific column name. >>> schema["foo"] Int8 Access various schema properties using the `names`, `dtypes`, and `len` methods. >>> schema.names() ['foo', 'bar'] >>> schema.dtypes() [Int8, String] >>> schema.len() 2 zClassVar[Version]_versionc0|xsi}t||yN)super__init__)selfschema __class__s M/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/narwhals/schema.pyrzSchema.__init__Gs2  c4t|jS)zXGet the column names of the schema. Returns: Column names. )listkeysrs rnamesz Schema.namesNs DIIK  rc4t|jS)z^Get the data types of the schema. Returns: Data types of schema. )rvaluesr s rdtypesz Schema.dtypesVs DKKM""rct|S)zbGet the number of columns in the schema. Returns: Number of columns. )lenr s rr&z Schema.len^s 4yrcnddl}ddlm|jfdj DS)a7Convert Schema to a pyarrow Schema. Returns: A pyarrow Schema. Examples: >>> import narwhals as nw >>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")}) >>> schema.to_arrow() a: int64 b: timestamp[ns] rNnarwhals_to_native_dtypec3NK|]\}}||jfywrr).0namedtyper)rs r z"Schema.to_arrow..ws. e+E4==A B s"%)pyarrownarwhals._arrow.utilsr)ritems)rpar)s` @rto_arrowzSchema.to_arrowfs1 Bryy #zz|   rc ddl}ddlm}t|tj t ||j}|t|tr,|jDcic]\}}||||c}}St|}t|t|k7rvddl m}ddl m} ddl m} t|t|} } t| |j#| | || | } d | d | d |d |dd | d }t%|t'|j)|j+|Dcic]\}}}||||c}}}Scc}}wcc}}}w)aConvert Schema to an ordered mapping of column names to their pandas data type. Arguments: dtype_backend: Backend(s) used for the native types. When providing more than one, the length of the iterable must be equal to the length of the schema. Returns: An ordered mapping of column names to their pandas data type. Examples: >>> import narwhals as nw >>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")}) >>> schema.to_pandas() {'a': 'int64', 'b': 'datetime64[ns]'} >>> schema.to_pandas("pyarrow") {'a': 'Int64[pyarrow]', 'b': 'timestamp[ns][pyarrow]'} rNr()implementationbackend_versionversion)r. dtype_backend)chain)islice)repeatz Provided z) `dtype_backend`(s), but schema contains z1 field(s). Hint: instead of schema.to_pandas(z+) you may want to use schema.to_pandas(z) or schema.to_pandas())pandasnarwhals._pandas_like.utilsr)rr PANDASr r isinstancestrr2tupler& itertoolsr:r;r< from_iterable ValueErrorziprr#)rr9pdr)to_native_dtyper-r.backendsr:r;r<n_usern_actual suggestionmsgbackends r to_pandaszSchema.to_pandas|sx* H! $)00)"-MM    J}c$B$(::<D%oEOO  ]+H8}D )+,,#&x=#d)"++F6(3CX,NOQY  z)RS[R^_,,4:6,,4QK=9,,6Ecddl}ddlmt|fdj D}dk\r|j |St dt|S)axConvert Schema to a polars Schema. Returns: A polars Schema or plain dict (prior to polars 1.0). Examples: >>> import narwhals as nw >>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")}) >>> schema.to_polars() Schema({'a': Int64, 'b': Datetime(time_unit='ns', time_zone=None)}) rNr(c3RK|]\}}||jf yw))r7Nr+)r,r-r.r) pl_versionrs rr/z#Schema.to_polars..s7 e (4==*  s$')rr pl.Schema)polarsnarwhals._polars.utilsr)r r2rr dict)rplrr)rSs` @@r to_polarszSchema.to_polarss` C"2&   $zz| Y& BIIf  k4<0 rr)rz8Mapping[str, DType] | Iterable[tuple[str, DType]] | NonereturnNone)r[z list[str])r[z list[DType])r[int)r[z pa.Schema)r9z%DTypeBackend | Iterable[DTypeBackend]r[zdict[str, Any])r[rU)__name__ __module__ __qualname____doc__r MAINr__annotations__rr!r$r&r4rPrZ __classcell__)rs@rrr&sm<#*,,H.LP!H! !!# .FJ>B> >@ r)ra __future__r collectionsr functoolsrtypingrrrr narwhals.utilsr r r r rrVrYr0r3narwhals.dtypesrnarwhals.typingrrB BaseSchema__all__rrrrosi ## )"(%,S%Z(JJ *t Zt r