!hJ-:dZddlmZddlmZmZmZddlZddlm Z ddl m Z ddl m Z ddlmZdd lmZdd lmZdd lmZmZmZmZdd lmZmZmZmZdd lm Z ddl!m"cm#Z$ddl%m&Z&erddl'm(Z(m)Z)ddlm*Z*ddl+m,Z,dZ-d)dZ.dZ/d*dZ0d+dZ1 d,d-d%Z2d.d(Z3dS)/zH Table Schema builders https://specs.frictionlessdata.io/table-schema/ ) annotations) TYPE_CHECKINGAnycastN)lib) ujson_loads) timezones)freq_to_period_freqstr)find_stack_level) _registry) is_bool_dtypeis_integer_dtypeis_numeric_dtypeis_string_dtype)CategoricalDtypeDatetimeTZDtypeExtensionDtype PeriodDtype) DataFrame) to_offset)DtypeObjJSONSerializable)Series) MultiIndexz1.4.0xrreturnstrcPt|rdSt|rdSt|rdStj|dst |t tfrdStj|drdSt |trdSt|rd SdS) a Convert a NumPy / pandas type to its corresponding json_table. Parameters ---------- x : np.dtype or ExtensionDtype Returns ------- str the Table Schema data types Notes ----- This table shows the relationship between NumPy / pandas dtypes, and Table Schema dtypes. ============== ================= Pandas type Table Schema type ============== ================= int64 integer float64 number bool boolean datetime64[ns] datetime timedelta64[ns] duration object str categorical any =============== ================= integerbooleannumberMdatetimemdurationanystring) rr rr is_np_dtype isinstancerrrr)rs Vc:\xampp_lite_8_4\www\timesheet\venv\Lib\site-packages\pandas/io/json/_table_schema.pyas_json_table_typer+5s<y q   y !   x C Jq?K2P$Q$Q z C z A~ & &u   xucDtj|jjr|jj}t |dkr3|jjdkr#t jdtnNt |dkr;td|Dr"t jdt|S| }|jj dkr)tj |jj|j_n|jjpd|j_|S)z?Sets index names to 'index' for regular, or 'level_x' for Multiindexz-Index name of 'index' is not round-trippable.) stacklevelc3@K|]}|dVdSlevel_N startswith.0rs r* z$set_default_names..ns.!F!FQ!,,x"8"8!F!F!F!F!F!Fr,z * *'!Jj Lr,str | CategoricalDtypec|d}|dkrdS|dkr|ddS|dkr|ddS|d kr|dd S|d krd S|d krq|dr d|ddS|dr9t|d}|j|j}}t ||}d|dSdS|dkrKd|vr'd|vr#t |dd|dSd|vrt j|dSdStd|)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]' rIr'objectrrPint64r!float64r boolr% timedelta64r#rOzdatetime64[ns, ]rMzperiod[zdatetime64[ns]r&rKrLrJ)rRrLz#Unsupported or invalid field type: ) getrnr=r rregistryfind ValueError)rXtypoffsetfreq_n freq_namerMs r*!convert_json_field_to_pandas_typerlsR -C hx  yyW--- yyY///  yyV,,,  }   99T?? $3U4[333 3 YYv   $uV}--F &&+IF)&)<`__ 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.rr=fieldsN primaryKeypandas_version)rEr/rArziplevelsr;rZappendndimitems is_uniquer=TABLE_SCHEMA_VERSION) rCr/rnrpschemarrlevelr= new_fieldcolumnss r*build_table_schemarsp }} &&F F I :  ! !lDJ77DJ"4:#4dj6FGG ) ) t=eDD $( &! i(((( ) MM;DJGG H H H y1}} @ @IFA MM;A>> ? ? ? ? @  7==>>>F8 +%++*= :  " "$(JO#4F< #':#3F<  *|8#7 Mr, precise_floatrc@t||}d|ddD}t|d||}d|ddD}d|vrtd ||}d |dvr{||dd }t |jjd kr|jj d kr d |j_ n d|jjD|j_|S)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 )rcg|] }|d Sr=r7rXs r* z&parse_table_schema..ksFFF5vFFFr,r|rrrC)columnsc:i|]}|dt|Sr)rlrs r* z&parse_table_schema..ns7  f 8??r,raz.s::; X..5Ar,) rrrHNotImplementedErrorastype set_indexr<r/r;r=)jsonrtable col_orderdfdtypess r*parse_table_schemarFs8H M : : :EFFE(OH,EFFFI 5=) 4 4 4Y ?B8_X.F  ''! J    6  BuX&& \\%/,7 8 8 rx~  ! # #x}'' $ ?Ax~BHN Ir,)rrrr)rrF)rr[)TNT) rCrmr/r`rnrorpr`rrF)rr`rr)4__doc__ __future__rtypingrrrr> pandas._libsrpandas._libs.jsonrpandas._libs.tslibsr pandas._libs.tslibs.dtypesr pandas.util._exceptionsr pandas.core.dtypes.baser repandas.core.dtypes.commonr rrrpandas.core.dtypes.dtypesrrrrpandasrpandas.core.commoncorecommonr9pandas.tseries.frequenciesrpandas._typingrrrpandas.core.indexes.multirr{r+rErZrlrrrr,r*rs #""""" ))))))))))))======444444999999  0000005 444444----`0@JBJBJBJB^# YYYYYx??????r,