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B �5�g�' � @ s d Z ddlmZ ddlmZmZmZ ddlZddlm m Z ddl m Z m Z mZ ddlmZmZmZmZmZmZmZmZmZ ddlmZ ddlm Z ddl m m Z er�dd l!m"Z" e j#Z#d d d �d d�Z$dd� Z%dd� Z&dd� Z'd dddddd�dd �Z(d d � Z)dS ) zM Table Schema builders https://specs.frictionlessdata.io/json-table-schema/ � )� annotations)� TYPE_CHECKING�Any�castN)�DtypeObj� FrameOrSeries�JSONSerializable) � is_bool_dtype�is_categorical_dtype�is_datetime64_dtype�is_datetime64tz_dtype�is_integer_dtype�is_numeric_dtype�is_period_dtype�is_string_dtype�is_timedelta64_dtype)�CategoricalDtype)� DataFrame)� MultiIndexr �str)�x�returnc C sl t | �r dS t| �rdS t| �r$dS t| �s<t| �s<t| �r@dS t| �rLdS t| �rXdS t| �rddS dS dS ) 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 =============== ================= �integer�boolean�number�datetime�duration�any�stringN) r r r r r r r r r )r � r �R/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/io/json/_table_schema.py�as_json_table_type, s r! c C s� t j| jj� rf| jj}t|�dkr:| jjdkr:t�d� n(t|�dkrbtdd� |D ��rbt�d� | S | � � } | jj dkr�dd� t | jj�D �}|| j_n| jjp�d| j_| S ) z?Sets index names to 'index' for regular, or 'level_x' for Multi� �indexz,Index name of 'index' is not round-trippablec s s | ]}|� d �V qdS )�level_N)� startswith)�.0r r r r � <genexpr>b s z$set_default_names.<locals>.<genexpr>z;Index names beginning with 'level_' are not round-trippablec S s& g | ] \}}|d k r|nd|� ��qS )Nr$ r )r& �i�namer r r � <listcomp>i s z%set_default_names.<locals>.<listcomp>) �comZ all_not_noner# �names�lenr) �warnings�warnr �copy�nlevels� enumerate)�dataZnmsr, r r r �set_default_names\ s r4 c C s� | j }| jd krd}n| j}|t|�d�}t|�rX|j}|j}dt|�i|d< ||d< n*t|�rn|jj |d< nt |�r�|j j |d< |S )N�values)r) �type�enum� constraints�ordered�freq�tz) �dtyper) r! r � categoriesr9 �listr r: Zfreqstrr r; �zone)Zarrr< r) �fieldZcatsr9 r r r �!convert_pandas_type_to_json_fieldr s rA c C s� | d }|dkrdS |dkr dS |dkr,dS |dkr8d S |d krDd S |d krl| � d �rfd| d � d�S dS n4|dkr�d| kr�d| kr�t| d d | d d�S dS td|� ���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]' r6 r �objectr Zint64r Zfloat64r �boolr � timedelta64r r; zdatetime64[ns, �]zdatetime64[ns]r r8 r9 r7 )r= r9 z#Unsupported or invalid field type: N)�getr � ValueError)r@ �typr r r �!convert_json_field_to_pandas_type� s* ) rI Tr rC z bool | Nonezdict[str, JSONSerializable])r3 r# � primary_key�versionr c C s0 |dkrt | �} i }g }|r�| jjdkrrtd| j�| _xJt| jj| jj�D ]"\}}t|�}||d< |�|� qJW n|�t| j�� | j dkr�x2| � � D ]\} } |�t| �� q�W n|�t| �� ||d< |�r | jj �r |dk�r | jjdkr�| jj g|d< n | jj|d< n|dk �r ||d< |�r,d|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 generated the schema. Returns ------- schema : dict Notes ----- See `Table Schema <https://pandas.pydata.org/docs/user_guide/io.html#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 -------- >>> 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': '0.20.0'} Tr" r r) �fieldsN� primaryKeyz0.20.0Zpandas_version) r4 r# r1 r �zipZlevelsr, rA �append�ndim�itemsZ is_uniquer) ) r3 r# rJ rK �schemarL �levelr) Z new_field�column�sr r r �build_table_schema� s4 6 rV c C s� t | |d�}dd� |d d D �}t|d |d�| }dd � |d d D �}d |�� kr`td ��|�|�}d |d kr�|�|d d �}t|jj�d kr�|jj dkr�d|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_floatc S s g | ] }|d �qS )r) r )r& r@ r r r r* M s z&parse_table_schema.<locals>.<listcomp>rR rL r3 )�columnsc S s i | ]}t |�|d �qS )r) )rI )r& r@ r r r � <dictcomp>P s z&parse_table_schema.<locals>.<dictcomp>rD z<table="orient" can not yet read ISO-formatted Timedelta datarM r" r# Nc S s g | ]}|� d �rdn|�qS )r$ N)r% )r& r r r r r* d s ) �loadsr r5 �NotImplementedErrorZastypeZ set_indexr- r# r, r) )�jsonrW �tableZ col_orderZdfZdtypesr r r �parse_table_schema( s $ r^ )TNT)*�__doc__� __future__r �typingr r r r. Zpandas._libs.jsonZ_libsr\ Zpandas._typingr r r Zpandas.core.dtypes.commonr r r r r r r r r Zpandas.core.dtypes.dtypesr Zpandasr Zpandas.core.common�core�commonr+ Zpandas.core.indexes.multir rZ r! r4 rA rI rV r^ r r r r �<module> s( , 0F V
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