o $&]iQ0@sdZddlmZddlmZddlmZddlmZddl m Z m Z ddl m Z mZmZmZddlmZmZmZmZmZmZmZe rpdd lmZdd l mZmZdd lZdd lZdd l m!Z!dd l"m#Z#ddl$m%Z%m&Z&m'Z'm(Z(dgZ)Gdddee*dfZ+d S)zxSchema. Adapted from Polars implementation at: https://github.com/pola-rs/polars/blob/main/py-polars/polars/schema.py. ) annotations) OrderedDict)Mapping)partial) TYPE_CHECKINGcast)ImplementationVersionqualified_type_name zip_strict)get_cudf is_cudf_dtypeis_pandas_like_dtypeis_polars_data_typeis_polars_schemais_pyarrow_data_typeis_pyarrow_schema)Iterable)AnyClassVarN)Self)DType) DTypeBackendIntoArrowSchemaIntoPandasSchemaIntoPolarsSchemaSchemacseZdZUdZejZded< d4d5fd d Zd6d d Z d7ddZ d8ddZ e d9ddZ e d:ddZe d;ddZe dd'd(Zd?d*d+Ze d@d.d/Ze dAd2d3ZZS)Bra<Ordered mapping of column names to their data type. Arguments: schema: 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]_versionNschema8Mapping[str, DType] | Iterable[tuple[str, DType]] | NonereturnNonecs|pi}t|dSN)super__init__)selfr __class__M/var/www/html/IGF-ODF-V3/venv/lib/python3.10/site-packages/narwhals/schema.pyr$MszSchema.__init__ list[str]cC t|S)z#Get the column names of the schema.)listkeysr%r(r(r)namesS z Schema.names list[DType]cCr+)z!Get the data types of the schema.)r,valuesr.r(r(r)dtypesWr0z Schema.dtypesintcCst|S)z(Get the number of columns in the schema.)lenr.r(r(r)r5[sz Schema.lenrrcsJt|tr|s Sddl}||}ddlmfdd|DS)aConstruct a Schema from a pyarrow Schema. Arguments: schema: A pyarrow Schema or mapping of column names to pyarrow data types. Examples: >>> import pyarrow as pa >>> import narwhals as nw >>> >>> mapping = { ... "a": pa.timestamp("us", "UTC"), ... "b": pa.date32(), ... "c": pa.string(), ... "d": pa.uint8(), ... } >>> native = pa.schema(mapping) >>> >>> nw.Schema.from_arrow(native) Schema({'a': Datetime(time_unit='us', time_zone='UTC'), 'b': Date, 'c': String, 'd': UInt8}) >>> nw.Schema.from_arrow(mapping) == nw.Schema.from_arrow(native) True rNnative_to_narwhals_dtypec3s$|] }|j|jjfVqdSr")nametyper).0fieldclsr7r(r) s  z$Schema.from_arrow..) isinstancerpyarrowrnarwhals._arrow.utilsr7)r=rpar(r<r) from_arrow_s   zSchema.from_arrowrcCs>|s|Strtdd|Drtjntj}|||S)a3Construct a Schema from a pandas-like schema representation. Arguments: schema: A mapping of column names to pandas-like data types. Examples: >>> import numpy as np >>> import pandas as pd >>> import pyarrow as pa >>> import narwhals as nw >>> >>> data = {"a": [1], "b": ["a"], "c": [False], "d": [9.2]} >>> native = pd.DataFrame(data).convert_dtypes().dtypes.to_dict() >>> >>> nw.Schema.from_pandas_like(native) Schema({'a': Int64, 'b': String, 'c': Boolean, 'd': Float64}) >>> >>> mapping = { ... "a": pd.DatetimeTZDtype("us", "UTC"), ... "b": pd.ArrowDtype(pa.date32()), ... "c": pd.StringDtype("python"), ... "d": np.dtype("uint8"), ... } >>> >>> nw.Schema.from_pandas_like(mapping) Schema({'a': Datetime(time_unit='us', time_zone='UTC'), 'b': Date, 'c': String, 'd': UInt8}) css|]}t|VqdSr")r )r:dtyper(r(r)r>sz*Schema.from_pandas_like..)r anyr2rCUDFPANDAS_from_pandas_like)r=rimplr(r(r)from_pandas_likes zSchema.from_pandas_like5IntoArrowSchema | IntoPolarsSchema | IntoPandasSchemacCs^t|r ||St|r||St|tr!|r||S|Sdt|d|}t|)aoConstruct a Schema from a native schema representation. Arguments: schema: A native schema object, or mapping of column names to *instantiated* native data types. Examples: >>> import datetime as dt >>> import pyarrow as pa >>> import narwhals as nw >>> >>> data = {"a": [1], "b": ["a"], "c": [dt.time(1, 2, 3)], "d": [[2]]} >>> native = pa.table(data).schema >>> >>> nw.Schema.from_native(native) Schema({'a': Int64, 'b': String, 'c': Time, 'd': List(Int64)}) z5Expected an arrow, polars, or pandas schema, but got z ) rrCr from_polarsr?r_from_native_mappingr TypeError)r=rmsgr(r(r) from_natives   zSchema.from_nativercs2|sSddlmfdd|DS)a/Construct a Schema from a polars Schema. Arguments: schema: A polars Schema or mapping of column names to *instantiated* polars data types. Examples: >>> import polars as pl >>> import narwhals as nw >>> >>> mapping = { ... "a": pl.Datetime(time_zone="UTC"), ... "b": pl.Date(), ... "c": pl.String(), ... "d": pl.UInt8(), ... } >>> native = pl.Schema(mapping) >>> >>> nw.Schema.from_polars(native) Schema({'a': Datetime(time_unit='us', time_zone='UTC'), 'b': Date, 'c': String, 'd': UInt8}) >>> nw.Schema.from_polars(mapping) == nw.Schema.from_polars(native) True rr6c3s$|] \}}||jfVqdSr"rr:r8rDr<r(r)r>  z%Schema.from_polars..)narwhals._polars.utilsr7items)r=rr(r<r)rLs  zSchema.from_polars pa.Schemacs2ddl}ddlm|fddDS)aConvert Schema to 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_dtypec3$|] \}}||jfVqdSr"rQrRrXr%r(r)r>rSz"Schema.to_arrow..)r@rArXrrU)r%rBr(rZr)to_arrows  zSchema.to_arrow dtype_backend%DTypeBackend | Iterable[DTypeBackend]dict[str, Any]c sddlm}t|tj|jddusttr$fdd|DSt }t |t |krjddl m }m }m}t |t |}}t |||||||} d|d |d |d |dd | d } t| fddt|||DS)amConvert 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. 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]'} rrW)implementationversionNcsi|] \}}||dqS)rDr\r(rRr\to_native_dtyper(r) s z$Schema.to_pandas..)chainislicerepeatz Provided z) `dtype_backend`(s), but schema contains z1 field(s). Hint: instead of schema.to_pandas(z+) you may want to use schema.to_pandas(z) or schema.to_pandas()cs i|] \}}}|||dqSrar()r:r8rDbackend)rcr(r)rd2s )narwhals._pandas_like.utilsrXrrrGrr?strrUtupler5 itertoolsrerfrg from_iterable ValueErrorr r-r2) r%r\rXbackendsrerfrgn_usern_actual suggestionrOr(rbr) to_pandass:   zSchema.to_pandas pl.SchemacsVddl}ddlmtj}fddD}|dkr$||Stdt |S)a%Convert Schema to a polars Schema. 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)}) rNrWc3rYr"rQrRrZr(r)r>ErSz#Schema.to_polars..)rrru) polarsrTrXrPOLARS_backend_versionrUrrdict)r%pl pl_versionrr(rZr) to_polars7s    zSchema.to_polarsnativeHMapping[str, pa.DataType] | Mapping[str, pl.DataType] | IntoPandasSchemacCstt|}|\}}t|r|td|St|r$|td|St|r0| td|Sd|dt |d|}t |)Nrrrz7Expected an arrow, polars, or pandas dtype, but found `z: z` ) nextiterrUrrLrrrJrrCr rN)r=r~ first_item first_key first_dtyperOr(r(r)rMOs"zSchema._from_native_mappingr_rcs.ddlm|fdd|DS)Nrr6c3s*|]\}}||jddfVqdS)T) allow_objectNrQrRr=rIr7r(r)r>js  z+Schema._from_pandas_like..)rjr7rU)r=rr_r(rr)rHcs zSchema._from_pandas_liker")rrr r!)r r*)r r1)r r4)rrr r)rrr r)rrKr r)rrr r)r rV)r\r]r r^)r ru)r~rr r)rrr_rr r)__name__ __module__ __qualname____doc__r MAINr__annotations__r$r/r3r5 classmethodrCrJrPrLr[rtr}rMrH __classcell__r(r(r&r)r-s0     % %   " 5 r),r __future__r collectionsrcollections.abcr functoolsrtypingrrnarwhals._utilsrr r r narwhals.dependenciesr r rrrrrrrrrwr{r@rBtyping_extensionsrnarwhals.dtypesrnarwhals.typingrrrr__all__rkrr(r(r(r)s$    $