d+ dZddlmZddlmZmZmZddlZddlm Z ddl m Z ddl m Z mZddlmZdd lmZdd lmZmZmZmZmZmZmZmZmZmZdd lm Z dd l!m"Z"ddl#m$cm%Z&er dd l!m'Z'ddl(m)Z)dZ*d$dZ+dZ,d%dZ-d&dZ. d'd(d"Z/d#Z0dS))zH Table Schema builders https://specs.frictionlessdata.io/table-schema/ ) annotations) TYPE_CHECKINGAnycastN)loads) timezones)DtypeObjJSONSerializable)find_stack_level) _registry) is_bool_dtypeis_categorical_dtypeis_datetime64_dtypeis_datetime64tz_dtypeis_extension_array_dtypeis_integer_dtypeis_numeric_dtypeis_period_dtypeis_string_dtypeis_timedelta64_dtype)CategoricalDtype) DataFrame)Series) MultiIndexz1.4.0xr returnstrcRt|rdSt|rdSt|rdSt|st |st |rdSt |rdSt|rdSt|rdSt|rdSdS)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 =============== ================= integerbooleannumberdatetimedurationanystring) rr rrrrrrrr)rs X/var/www/html/t/fyr/venv311/lib/python3.11/site-packages/pandas/io/json/_table_schema.pyas_json_table_typer'1s<y q  y !   x Q   #8#;#; q?Q?Q z a z a u !! $ $u   xucDtj|jjr|jj}t |dkr3|jjdkr#t jdtnNt |dkr;td|Dr"t jdt|S| }|jj dkr)tj |jj|j_n|jjpd|j_|S)z?Sets index names to 'index' for regular, or 'level_x' for Multiindexz-Index name of 'index' is not round-trippable.) stacklevelc3@K|]}|dVdSlevel_N startswith.0rs r& z$set_default_names..ls.!F!FQ!,,x"8"8!F!F!F!F!F!Fr(z>> 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) >>> 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]' rEr%objectrrLint64r!float64r boolr# timedelta64r"rKzdatetime64[ns, ]rIzperiod[zdatetime64[ns]r$rGrHrF)rNrHz#Unsupported or invalid field type: )getrregistryfind ValueError)rTtyps r&!convert_json_field_to_pandas_typerdsoR -C hx  yyW--- yyY///  yyV,,,  }   99T?? $3U4[333 3 YYv   $-U6]--- -##  E ! !i5&8&8# /7yAQ 5 =z!233 38 @3@@ A AAr(Tr?DataFrame | Seriesr+r\ primary_key bool | Noneversionc|durt|}i}g}|r|jjdkrntd|j|_t |jj|jjD].\}}t|}||d<||/n'|t|j|j dkr=| D]'\} } |t| (n"|t|||d<|r?|jj r3|1|jjdkr|jj g|d<n|jj|d<n|||d<|r t|d<|S) a Create a Table schema from ``data``. Parameters ---------- data : Series, DataFrame 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 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'}, {'name': 'C', 'type': 'datetime'}], 'primaryKey': ['idx'], 'pandas_version': '1.4.0'} Tr*rr9fieldsN primaryKeypandas_version)rAr+r=rziplevelsr7rVappendndimitems is_uniquer9TABLE_SCHEMA_VERSION) r?r+rfrhschemarjlevelr9 new_fieldcolumnss r&build_table_schemarysp }} &&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(c@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 ) precise_floatcg|] }|d Sr9r3rTs r& z&parse_table_schema..dsFFF5vFFFr(rtrjr?)columnsc:i|]}|dt|Sr})rdrs r& z&parse_table_schema..gs7  f 8??r(r]z.zs::; X..5Ar() rrrDNotImplementedErrorastype set_indexr8r+r7r9)jsonr{table col_orderdfdtypess r&parse_table_schemar?s8H $m 4 4 4EFFE(OH,EFFFI 5=) 4 4 4Y ?B8_X.F  ''! J    6  BuX&& \\%/,7 8 8 rx~  ! # #x}'' $ ?Ax~BHN Ir()rr rr)rrB)rrW)TNT) r?rer+r\rfrgrhr\rrB)1__doc__ __future__rtypingrrrr:pandas._libs.jsonrpandas._libs.tslibsrpandas._typingr r pandas.util._exceptionsr pandas.core.dtypes.baser r`pandas.core.dtypes.commonr rrrrrrrrrpandas.core.dtypes.dtypesrpandasrpandas.core.commoncorecommonr5rpandas.core.indexes.multirrsr'rArVrdryrr~r(r&rsW #""""" ######))))))544444999999                        766666 5444444////d0>FBFBFBFBV# YYYYYx?????r(