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B �5�g-B � @ s d dl mZ d dlmZmZ d dlZd dlmZmZm Z d dl Z d dl m Z d dlmZ d dlmZ d dlZd dlmZ d d d �d d �Zd,d d ddd�dd�Zdd dd dd�dd�Zdd dd�dd�Zd-d d d d �d d �Zd.d d"d#d$d$d d dd%d&� d'd(�Zed)ed*d+�ZdS )/� )� annotations)�abc� defaultdictN)�Any� DefaultDict�Iterable)�convert_json_to_lines)�Scalar)� deprecate)� DataFrame�str)�s�returnc C s0 | d dks | d dkr | S | dd� } t | �S )zJ Helper function that converts JSON lists to line delimited JSON. r �[����]� )r )r � r �O/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/io/json/_normalize.py�convert_to_line_delimits s r � �.�intz int | None)�prefix�sep�level� max_levelc C s� d}t | t�r| g} d}g }x�| D ]�}t�|�}x�|�� D ]�\} } t | t�sTt| �} |dkrb| } n || | } t | t�r�|dk r�||kr�|dkr:|�| �} | || < q:q:|�| �} |�t| | ||d |�� q:W |� |� q"W |r�|d S |S )a� A simplified json_normalize Converts a nested dict into a flat dict ("record"), unlike json_normalize, it does not attempt to extract a subset of the data. Parameters ---------- ds : dict or list of dicts prefix: the prefix, optional, default: "" sep : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar level: int, optional, default: 0 The number of levels in the json string. max_level: int, optional, default: None The max depth to normalize. .. versionadded:: 0.25.0 Returns ------- d - dict or list of dicts, matching `ds` Examples -------- >>> nested_to_record( ... dict(flat1=1, dict1=dict(c=1, d=2), nested=dict(e=dict(c=1, d=2), d=2)) ... ) {'flat1': 1, 'dict1.c': 1, 'dict1.d': 2, 'nested.e.c': 1, 'nested.e.d': 2, 'nested.d': 2} FTr Nr ) � isinstance�dict�copy�deepcopy�itemsr �pop�update�nested_to_record�append) �dsr r r r Z singletonZnew_ds�dZnew_d�k�vZnewkeyr r r r$ ' s2 . r$ r zdict[str, Any])�data� key_string�normalized_dict� separatorr c C sr t | t�rfxb| �� D ]L\}}|� |� |� �}t||t|�d |krH|n|t|�d� ||d� qW n| ||< |S )a3 Main recursive function Designed for the most basic use case of pd.json_normalize(data) intended as a performance improvement, see #15621 Parameters ---------- data : Any Type dependent on types contained within nested Json key_string : str New key (with separator(s) in) for data normalized_dict : dict The new normalized/flattened Json dict separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar r N)r* r+ r, r- )r r r! �_normalise_json�len)r* r+ r, r- �key�valueZnew_keyr r r r. z s r. )r* r- r c C s8 dd� | � � D �}tdd� | � � D �di |d�}||�S )aw Order the top level keys and then recursively go to depth Parameters ---------- data : dict or list of dicts separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- dict or list of dicts, matching `normalised_json_object` c S s i | ]\}}t |t�s||�qS r )r r )�.0r( r) r r r � <dictcomp>� s z+_normalise_json_ordered.<locals>.<dictcomp>c S s i | ]\}}t |t�r||�qS r )r r )r2 r( r) r r r r3 � s r )r* r+ r, r- )r! r. )r* r- Z top_dict_Z nested_dict_r r r �_normalise_json_ordered� s r4 zdict | list[dict]zdict | list[dict] | Any)r& r r c s@ i }t | t�r t| � d�}n t | t�r<� fdd�| D �}|S |S )a� A optimized basic json_normalize Converts a nested dict into a flat dict ("record"), unlike json_normalize and nested_to_record it doesn't do anything clever. But for the most basic use cases it enhances performance. E.g. pd.json_normalize(data) Parameters ---------- ds : dict or list of dicts sep : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- frame : DataFrame d - dict or list of dicts, matching `normalised_json_object` Examples -------- >>> _simple_json_normalize( ... { ... "flat1": 1, ... "dict1": {"c": 1, "d": 2}, ... "nested": {"e": {"c": 1, "d": 2}, "d": 2}, ... } ... ) {'flat1': 1, 'dict1.c': 1, 'dict1.d': 2, 'nested.e.c': 1, 'nested.e.d': 2, 'nested.d': 2} )r* r- c s g | ]}t |� d ��qS ))r )�_simple_json_normalize)r2 �row)r r r � <listcomp>� s z*_simple_json_normalize.<locals>.<listcomp>)r r r4 �list)r& r Znormalised_json_objectZnormalised_json_listr )r r r5 � s + r5 �raisezstr | list | Nonez"str | list[str | list[str]] | Nonez str | Noner ) r* � record_path�meta� meta_prefix� record_prefix�errorsr r r c s� dddd�dd��dddd��fdd � �t | t�r<| s<t� S t | t�rN| g} n$t | tj�rnt | t�snt| �} nt�|d kr�|d kr�|d kr�� d kr��d kr�tt| � d ��S |d kr�t d d � | D ��r�t | � �d�} t| �S t |t�s�|g}|d kr�g }nt |t��s |g}dd� |D �� g � g �t t��� fdd�� D ��d � ��������� � f dd� ��| |i dd� t� �}� d k �r�|j � fdd�d�}xZ�� � D ]N\} } |d k �r�|| } | |k�r�td| � d���tj| td����|| < �q�W |S ) a� Normalize semi-structured JSON data into a flat table. Parameters ---------- data : dict or list of dicts Unserialized JSON objects. record_path : str or list of str, default None Path in each object to list of records. If not passed, data will be assumed to be an array of records. meta : list of paths (str or list of str), default None Fields to use as metadata for each record in resulting table. meta_prefix : str, default None If True, prefix records with dotted (?) path, e.g. foo.bar.field if meta is ['foo', 'bar']. record_prefix : str, default None If True, prefix records with dotted (?) path, e.g. foo.bar.field if path to records is ['foo', 'bar']. errors : {'raise', 'ignore'}, default 'raise' Configures error handling. * 'ignore' : will ignore KeyError if keys listed in meta are not always present. * 'raise' : will raise KeyError if keys listed in meta are not always present. sep : str, default '.' Nested records will generate names separated by sep. e.g., for sep='.', {'foo': {'bar': 0}} -> foo.bar. max_level : int, default None Max number of levels(depth of dict) to normalize. if None, normalizes all levels. .. versionadded:: 0.25.0 Returns ------- frame : DataFrame Normalize semi-structured JSON data into a flat table. Examples -------- >>> data = [ ... {"id": 1, "name": {"first": "Coleen", "last": "Volk"}}, ... {"name": {"given": "Mark", "family": "Regner"}}, ... {"id": 2, "name": "Faye Raker"}, ... ] >>> pd.json_normalize(data) id name.first name.last name.given name.family name 0 1.0 Coleen Volk NaN NaN NaN 1 NaN NaN NaN Mark Regner NaN 2 2.0 NaN NaN NaN NaN Faye Raker >>> data = [ ... { ... "id": 1, ... "name": "Cole Volk", ... "fitness": {"height": 130, "weight": 60}, ... }, ... {"name": "Mark Reg", "fitness": {"height": 130, "weight": 60}}, ... { ... "id": 2, ... "name": "Faye Raker", ... "fitness": {"height": 130, "weight": 60}, ... }, ... ] >>> pd.json_normalize(data, max_level=0) id name fitness 0 1.0 Cole Volk {'height': 130, 'weight': 60} 1 NaN Mark Reg {'height': 130, 'weight': 60} 2 2.0 Faye Raker {'height': 130, 'weight': 60} Normalizes nested data up to level 1. >>> data = [ ... { ... "id": 1, ... "name": "Cole Volk", ... "fitness": {"height": 130, "weight": 60}, ... }, ... {"name": "Mark Reg", "fitness": {"height": 130, "weight": 60}}, ... { ... "id": 2, ... "name": "Faye Raker", ... "fitness": {"height": 130, "weight": 60}, ... }, ... ] >>> pd.json_normalize(data, max_level=1) id name fitness.height fitness.weight 0 1.0 Cole Volk 130 60 1 NaN Mark Reg 130 60 2 2.0 Faye Raker 130 60 >>> data = [ ... { ... "state": "Florida", ... "shortname": "FL", ... "info": {"governor": "Rick Scott"}, ... "counties": [ ... {"name": "Dade", "population": 12345}, ... {"name": "Broward", "population": 40000}, ... {"name": "Palm Beach", "population": 60000}, ... ], ... }, ... { ... "state": "Ohio", ... "shortname": "OH", ... "info": {"governor": "John Kasich"}, ... "counties": [ ... {"name": "Summit", "population": 1234}, ... {"name": "Cuyahoga", "population": 1337}, ... ], ... }, ... ] >>> result = pd.json_normalize( ... data, "counties", ["state", "shortname", ["info", "governor"]] ... ) >>> result name population state shortname info.governor 0 Dade 12345 Florida FL Rick Scott 1 Broward 40000 Florida FL Rick Scott 2 Palm Beach 60000 Florida FL Rick Scott 3 Summit 1234 Ohio OH John Kasich 4 Cuyahoga 1337 Ohio OH John Kasich >>> data = {"A": [1, 2]} >>> pd.json_normalize(data, "A", record_prefix="Prefix.") Prefix.0 0 1 1 2 Returns normalized data with columns prefixed with the given string. zdict[str, Any]z list | strzScalar | Iterable)�js�specr c S s2 | }t |t�r&x |D ] }|| }qW n|| }|S )z Internal function to pull field)r r8 )r? r@ �result�fieldr r r � _pull_field s z$_json_normalize.<locals>._pull_fieldr8 c sB � | |�}t |t�s>t�|�r$g }nt| � d|� d|� d���|S )z� Internal function to pull field for records, and similar to _pull_field, but require to return list. And will raise error if has non iterable value. z has non list value z for path z. Must be list or null.)r r8 �pdZisnull� TypeError)r? r@ rA )rC r r � _pull_records� s z&_json_normalize.<locals>._pull_recordsN)r c s s | ]}d d� |� � D �V qdS )c S s g | ]}t |t��qS r )r r )r2 �xr r r r7 � s z-_json_normalize.<locals>.<genexpr>.<listcomp>N)�values)r2 �yr r r � <genexpr>� s z"_json_normalize.<locals>.<genexpr>)r r c S s g | ]}t |t�r|n|g�qS r )r r8 )r2 �mr r r r7 � s z#_json_normalize.<locals>.<listcomp>c s g | ]}� � |��qS r )�join)r2 �val)r r r r7 � s r c sv t | t�r| g} t|�dkr�xj| D ]b}x8t� ��D ]*\}}|d t|�kr2�||d �||< q2W �||d |dd � ||d d� q"W n�x�| D ]�}�||d �}�� fdd�|D �}��t|�� x�t� ��D ]�\}}|d t|�kr�|| }n`y�|||d � �}W nH tk �rP } z(�dk�r.tj}ntd| � d ��| �W d d } ~ X Y nX �| �|� q�W � �|� q�W d S ) Nr r r )r c s( g | ] }t |t�r t|�� d �n|�qS ))r r )r r r$ )r2 �r)r r r r r7 � s z?_json_normalize.<locals>._recursive_extract.<locals>.<listcomp>�ignorez(Try running with errors='ignore' as key z is not always present) r r r/ �zipr% �KeyError�np�nan�extend) r* �pathZ seen_metar �objrM r0 ZrecsZmeta_val�e) �_metarC rF �_recursive_extractr> �lengthsr � meta_keys� meta_vals�recordsr r r rY � s4 * z+_json_normalize.<locals>._recursive_extract)r c s � � | � �S )Nr )rG )r= r r �<lambda>� � z!_json_normalize.<locals>.<lambda>)�columnszConflicting metadata name z , need distinguishing prefix )Zdtype)r )r r8 r r r r r �NotImplementedErrorr5 �anyr$ r �renamer! � ValueErrorrR �array�object�repeat) r* r: r; r< r= r> r r rA r( r) r ) rX rC rF rY r> rZ r r[ r\ r= r] r r �_json_normalize� sX "' rh z pandas.io.json.json_normalizez1.0.0zpandas.json_normalize)r r r N)r )NNNNr9 r N) � __future__r � collectionsr r r �typingr r r �numpyrR Zpandas._libs.writersr Zpandas._typingr Zpandas.util._decoratorsr ZpandasrD r r r$ r. r4 r5 rh Zjson_normalizer r r r �<module> s8 N(5
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