+ xȇiD/VRt^RIHt^RIHtHtHt^RIt^RIH t ^RI H t ^RI H t ^RIHt^RIHt^R IHt^R IHtHtHtHt^R IHtHtHtHt^R IH t ^RI!H"uH#t$^R I%H&t&]'d^RI'H(t(H)t)^RIH*t*^RI+H,t,Rt-RRlt.Rt/RRlt0RRlt1RRRllt2RRlt3R#)zH Table Schema builders https://specs.frictionlessdata.io/table-schema/ ) annotations) TYPE_CHECKINGAnycastN)option_context)lib) ujson_loads) timezones)find_stack_level) _registry) is_bool_dtypeis_integer_dtypeis_numeric_dtypeis_string_dtype)CategoricalDtypeDatetimeTZDtypeExtensionDtype PeriodDtype) DataFrame) to_offset)DtypeObjJSONSerializable)Series) MultiIndexz1.4.0c V^8dQhRRRR/#)xrreturnstr)formats"l/Users/max/.openclaw/workspace/postharvest/venv/lib/python3.14/site-packages/pandas/io/json/_table_schema.py __annotate__r"7s++(+s+cR\V4'dR#\V4'dR#\V4'dR#\P!VR4'g\ V\ \34'dR#\P!VR4'dR#\V4'dR#R #) a< Convert a NumPy / pandas type to its corresponding json_table. Parameters ---------- x : np.dtype or ExtensionDtype Returns ------- str the Table Schema data types Notes ----- This table shows the relationship between NumPy / pandas dtypes, and Table Schema dtypes. ============== ================= Pandas type Table Schema type ============== ================= int64 integer float64 number bool boolean datetime64[ns] datetime timedelta64[ns] duration object str categorical any =============== ================= integerbooleannumberMdatetimemdurationstringany) r r rr is_np_dtype isinstancerrr)rs&r!as_json_table_typer07sx< q   !   C Jq?K2P$Q$Q C    r#c.\P!VPP!'dVPPp\ V4^8Xd>VPP R8Xd#\ P!R\4R7V#\ V4^8d[\;QJdRV4F 'gK RM RM !RV44'd!\ P!R\4R7V#VPRR7pVPP^8d<\P!VPP4VPnV#VPP ;'gRVPnV#) z?Sets index names to 'index' for regular, or 'level_x' for Multiindexz-Index name of 'index' is not round-trippable.) stacklevelc3B"TFqPR4xK R#5i)level_N) startswith).0rs& r! $set_default_names..ns!F#Q,,x"8"8#sTFz * *!JJj Lr#cV^8dQhRR/#)rrzstr | CategoricalDtyper)r s"r!r"r"sIBIB0FIBr#cVR,pVR8XdVPRR4#VR8XdVPRR4#VR8XdVPRR4#VR 8XdVPRR 4#VR 8XdR #VR 8XdkVPR4'dRVR, R2#VPR4'd.\VR,4p\V4PpRV R2#R#VR8XdWRV9d*RV9d#\ VR,R,VR,R7#RV9d\ P !VR,4#R#\RV 24h)a Converts a JSON field descriptor into its corresponding NumPy / pandas type Parameters ---------- field A JSON field descriptor Returns ------- dtype Raises ------ ValueError If the type of the provided field is unknown or currently unsupported Examples -------- >>> convert_json_field_to_pandas_type({"name": "an_int", "type": "integer"}) 'int64' >>> convert_json_field_to_pandas_type( ... { ... "name": "a_categorical", ... "type": "any", ... "constraints": {"enum": ["a", "b", "c"]}, ... "ordered": True, ... } ... ) CategoricalDtype(categories=['a', 'b', 'c'], ordered=True, categories_dtype=str) >>> convert_json_field_to_pandas_type({"name": "a_datetime", "type": "datetime"}) 'datetime64[ns]' >>> convert_json_field_to_pandas_type( ... {"name": "a_datetime_with_tz", "type": "datetime", "tz": "US/Central"} ... ) 'datetime64[ns, US/Central]' rLr,rSNr%int64r'float64r&boolr+ timedelta64r)rRzdatetime64[ns, ]rPzperiod[zdatetime64[ns]r-rNrOrM)rUrOobjectz#Unsupported or invalid field type: )getrr_freqstrrregistryfind ValueError)r[typoffsetrPs& r!!convert_json_field_to_pandas_typernsMR -C hyyT** yyW-- yyY// yyV,,   99T??$U4[M3 3 YYv  uV}-Fv&//DTF!$ $#  E !i5&8# /7yAQ 5 ==z!23 3 :3%@ AAr#c ,V^8dQhRRRRRRRRRR /#) rrEzDataFrame | Seriesr2rc primary_keyz bool | NoneversionrrIr)r s"r!r"r"sAff f ff f ! fr#cVRJd \V4p/p.pV'dVPP^8d|\RVP4Vn\ VPP VPP RR7F%wrg\V4pWxR&VPV4K' M$VP\VP44VP^8d5VP4FwrVP\V 44K! MVP\V44WTR&V'dqVPP'dUVfQVPP^8XdVPP.VR&M"VPP VR&MVeW$R&V'd \VR&V#)a  Create a Table schema from ``data``. This method is a utility to generate a JSON-serializable schema representation of a pandas Series or DataFrame, compatible with the Table Schema specification. It enables structured data to be shared and validated in various applications, ensuring consistency and interoperability. Parameters ---------- data : Series or DataFrame The input data for which the table schema is to be created. index : bool, default True Whether to include ``data.index`` in the schema. primary_key : bool or None, default True Column names to designate as the primary key. The default `None` will set `'primaryKey'` to the index level or levels if the index is unique. version : bool, default True Whether to include a field `pandas_version` with the version of pandas that last revised the table schema. This version can be different from the installed pandas version. Returns ------- dict A dictionary representing the Table schema. See Also -------- DataFrame.to_json : Convert the object to a JSON string. read_json : Convert a JSON string to pandas object. Notes ----- See `Table Schema `__ for conversion types. Timedeltas as converted to ISO8601 duration format with 9 decimal places after the seconds field for nanosecond precision. Categoricals are converted to the `any` dtype, and use the `enum` field constraint to list the allowed values. The `ordered` attribute is included in an `ordered` field. Examples -------- >>> from pandas.io.json._table_schema import build_table_schema >>> df = pd.DataFrame( ... {'A': [1, 2, 3], ... 'B': ['a', 'b', 'c'], ... 'C': pd.date_range('2016-01-01', freq='D', periods=3), ... }, index=pd.Index(range(3), name='idx')) >>> build_table_schema(df) {'fields': [{'name': 'idx', 'type': 'integer'}, {'name': 'A', 'type': 'integer'}, {'name': 'B', 'type': 'string', 'extDtype': 'str'}, {'name': 'C', 'type': 'datetime'}], 'primaryKey': ['idx'], 'pandas_version': '1.4.0'} Tr)strictr?fields primaryKeypandas_version)rGr2rCrziplevelsr=r^appendndimitems is_uniquer?TABLE_SCHEMA_VERSION) rEr2rprqschemartlevelr? new_fieldcolumnss &&&& r!build_table_schemarseJ } &F F ::   !lDJJ7DJ"4::#4#4djj6F6FtT =eD $(&! i( U MM;DJJG H yy1}IF MM;A> ?&  7=>8 %%%+*= ::   "$(JJOO#4F< #'::#3#3F<  *|#7 Mr#c V^8dQhRRRR/#)r precise_floatrcrrr)r s"r!r"r"Rs@@D@Y@r#cv\WR7pVR,R,Uu.uF q3R,NK pp\VR,VR7V,pVR,R,Uu/uFpVR,\V4bK ppRVP49d \ R4h\ R R 4;_uu_4VP V4pR R R 4R VR,9dVPVR,R ,4p\VPP4^8Xd/VPPR 8XdR VPn V#VPPUu.uFqwPR4'dR MTNK upVPn V#uupiuupi +'giL;iuupi)a Builds a DataFrame from a given schema Parameters ---------- json : A JSON table schema precise_float : bool Flag controlling precision when decoding string to double values, as dictated by ``read_json`` Returns ------- df : DataFrame Raises ------ NotImplementedError If the JSON table schema contains either timezone or timedelta data Notes ----- Because :func:`DataFrame.to_json` uses the string 'index' to denote a name-less :class:`Index`, this function sets the name of the returned :class:`DataFrame` to ``None`` when said string is encountered with a normal :class:`Index`. For a :class:`MultiIndex`, the same limitation applies to any strings beginning with 'level_'. Therefore, an :class:`Index` name of 'index' and :class:`MultiIndex` names starting with 'level_' are not supported. See Also -------- build_table_schema : Inverse function. pandas.read_json )rr~rtr?rE)columnsrdzr2r=r?r6)jsonrtabler[ col_orderdfdtypesrs&& r!parse_table_schemarRs}H  :E,1(OH,EF,E5v,EIF 5=) 4Y ?B8_X..E f 8??.   '! J   7 ? ? YYv  @uX& \\%/,7 8 rxx~~ ! #xx}}' $ I@Bxx~~?M! X..A5~BHHN I7G @ ?sFF1F#"#F6# F3 )TNT)4__doc__ __future__rtypingrrrr@pandas._configr pandas._libsrpandas._libs.jsonrpandas._libs.tslibsr pandas.util._exceptionsr pandas.core.dtypes.baser ripandas.core.dtypes.commonr r rrpandas.core.dtypes.dtypesrrrrpandasrpandas.core.commoncorecommonr;pandas.tseries.frequenciesrpandas._typingrrrpandas.core.indexes.multirr}r0rGr^rnrrrr#r!rs # )))49  0 4+\0@IBXfR@r#