hJ-`SrSSKJr SSKJrJrJr SSKrSSKJ r SSK J r SSK J r SSKJr SS KJr SS KJr SS KJrJrJrJr SS KJrJrJrJr SS KJ r SSK!J"s J#r$ SSK%J&r& \(aSSK'J(r(J)r) SSKJ*r* SSK+J,r, Sr-SSjr.Sr/SSjr0SSjr1SSSjjr2SSjr3g)zH Table Schema builders https://specs.frictionlessdata.io/table-schema/ ) annotations) TYPE_CHECKINGAnycastN)lib) ujson_loads) timezones)freq_to_period_freqstr)find_stack_level) _registry) is_bool_dtypeis_integer_dtypeis_numeric_dtypeis_string_dtype)CategoricalDtypeDatetimeTZDtypeExtensionDtype PeriodDtype) DataFrame) to_offset)DtypeObjJSONSerializable)Series) MultiIndexz1.4.0cb[U5(ag[U5(ag[U5(ag[R"US5(d[ U[ [45(ag[R"US5(ag[ U[5(ag[U5(ag g) 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 =============== ================= integerbooleannumberMdatetimemdurationanystring) rr rr is_np_dtype isinstancerrrr)xs `C:\Users\psant\Downloads\Noiseback\school\venv\Lib\site-packages\pandas/io/json/_table_schema.pyas_json_table_typer)5s< q   !   C Jq?K2P$Q$Q C  A~ & &   c[R"URR6(aURRn[ U5S:Xa9URR S:Xa[ R"S[5S9 U$[ U5S:a4[SU55(a[ R"S[5S9 U$UR5nURRS:a;[R"URR5URlU$URR =(d SURlU$)z?Sets index names to 'index' for regular, or 'level_x' for Multiindexz-Index name of 'index' is not round-trippable.) stacklevelc3B# UHoRS5v M g7f)level_N) startswith).0r's r( $set_default_names..ns!F#Q,,x"8"8#sz * *!JJj Lr*cBUSnUS:XagUS:XaURSS5$US:XaURSS5$US :XaURSS 5$US :Xag US :XalURS5(a SUSS3$URS5(a6[US5nURURpC[ X45nSUS3$gUS:Xa?SU;aSU;a[ USSUSS9$SU;a[ R"US5$g[SU35e)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=object) >>> 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]' rDr$objectrrKint64rfloat64rboolr" timedelta64r rJzdatetime64[ns, ]rHzperiod[zdatetime64[ns]r#rFrGrE)rMrGz#Unsupported or invalid field type: ) getrnr9r rregistryfind ValueError)rStypoffsetfreq_n freq_namerHs r(!convert_json_field_to_pandas_typerfsHR -C h yyW-- yyY// yyV,,   99T??$U4[M3 3 YYv  uV}-F &&++I)&`__ 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'}, {'name': 'C', 'type': 'datetime'}], 'primaryKey': ['idx'], 'pandas_version': '1.4.0'} Tr,rr9fields primaryKeypandas_version)rAr-r=rziplevelsr7rUappendndimitems is_uniquer9TABLE_SCHEMA_VERSION) r?r- primary_keyversionschemarhlevelr9 new_fieldcolumnss r(build_table_schemarys`p } &F F ::   !lDJJ7DJ"4::#4#4djj6F6FG =eD $(&! i( H MM;DJJG H yy1}IF MM;A> ?&  7=>8 %%+*= ::   "$(JJOO#4F< #'::#3#3F<  *|#7 Mr*c[XS9nUSSVs/sHo3SPM nn[USUS9UnUSSVs0sHnUS[U5_M nnSUR5;a [ S5eUR U5nS US;aUR USS 5n[URR5S :Xa-URRS :XaS URl U$URRVs/sHowRS 5(aS OUPM snURl U$s snfs snfs snf)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 ) precise_floatrtrhr9r?)columnsr[zrs # ))=49  0 4-`0@JB^# Y Y YY Y ! Yx?r*