U -e+@s>dZddlmZddlmZmZmZddlZddlm Z ddl m Z ddl m Z mZddlmZdd lmZdd lmZmZmZmZmZmZmZmZmZmZdd lm Z dd l!m"Z"ddl#m$m%Z&erdd l!m'Z'ddl(m)Z)dZ*dddddZ+ddZ,ddddZ-ddddZ.d'dd d!d dd"d#d$Z/d%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) MultiIndexz1.4.0rstr)xreturncCsxt|r dSt|rdSt|r$dSt|slsz$set_default_names..zt |rt |j rd|d<q|j j|d<nt|r|j|d <|S) Nvalues)r3typeenum constraintsorderedfreqUTCtzextDtype)dtyper3r&r categoriesr>listrr?ZfreqstrrrZis_utcrAzoner)ZarrrCr3fieldZcatsr>r$r$r%!convert_pandas_type_to_json_field{s*     rHzstr | CategoricalDtypecCs|d}|dkrdS|dkr(|ddS|dkr<|ddS|d krP|dd S|d kr\d S|d kr|dr~d|ddS|drd|ddSdSnJ|dkrd|krd|krt|dd|ddSd|krt|dSdStd|dS)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) >>> 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]' r;r#objectrrBZint64rZfloat64rboolr! timedelta64r rAzdatetime64[ns, ]r?zperiod[zdatetime64[ns]r"r=r>r<)rDr>z#Unsupported or invalid field type: N)getrregistryfind ValueError)rGtypr$r$r%!convert_json_field_to_pandas_types6)      rRTzDataFrame | SeriesrJz bool | None)r8r( primary_keyversionrc Cs"|dkrt|}i}g}|r~|jjdkrntd|j|_t|jj|jjD]"\}}t|}||d<||qHn|t|j|j dkr| D]\} } |t| qn|t|||d<|r|jj r|dkr|jjdkr|jj g|d<n |jj|d<n|dk r||d<|rt |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'rr3fieldsN primaryKeyZpandas_version)r9r(r7rzipZlevelsr1rHappendndimitemsZ is_uniquer3TABLE_SCHEMA_VERSION) r8r(rSrTschemarUlevelr3Z new_fieldcolumnsr$r$r%build_table_schemas48    r`cCst||d}dd|ddD}t|d|d|}dd |ddD}d |kr`td ||}d |dkr||dd }t|jjd kr|jj dkrd|j_ ndd|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_floatcSsg|] }|dqSr3r$r.rGr$r$r% dsz&parse_table_schema..r\rUr8)columnscSsi|]}|dt|qSrb)rRrcr$r$r% gsz&parse_table_schema..rKzs2     0    2K\