json 형식을 DataFrame으로 읽기(.csv로 전환)

import pandas as pd
import json

data = pd.DataFrame(json.loads(open('jsonFile.txt','r+').read()))#   
dataCopy = pd.read_json('jsonFile.txt',typ='frame')  #   

pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False)[source]

    Convert a JSON string to pandas object
    Parameters: 

    path_or_buf : a valid JSON string or file-like, default: None

        The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file://localhost/path/to/table.json

    orient : string,

        Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. The set of possible orients is:

            'split' : dict like {index -> [index], columns -> [columns], data -> [values]}
            'records' : list like [{column -> value}, ... , {column -> value}]
            'index' : dict like {index -> {column -> value}}
            'columns' : dict like {column -> {index -> value}}
            'values' : just the values array

        The allowed and default values depend on the value of the typ parameter.

            when typ == 'series',
                allowed orients are {'split','records','index'}
                default is 'index'
                The Series index must be unique for orient 'index'.
            when typ == 'frame',
                allowed orients are {'split','records','index', 'columns','values'}
                default is 'columns'
                The DataFrame index must be unique for orients 'index' and 'columns'.
                The DataFrame columns must be unique for orients 'index', 'columns', and 'records'.

    typ : type of object to recover (series or frame), default ‘frame’

    dtype : boolean or dict, default True

        If True, infer dtypes, if a dict of column to dtype, then use those, if False, then don’t infer dtypes at all, applies only to the data.

    convert_axes : boolean, default True

        Try to convert the axes to the proper dtypes.

    convert_dates : boolean, default True

        List of columns to parse for dates; If True, then try to parse datelike columns default is True; a column label is datelike if

            it ends with '_at',
            it ends with '_time',
            it begins with 'timestamp',
            it is 'modified', or
            it is 'date'

    keep_default_dates : boolean, default True

        If parsing dates, then parse the default datelike columns

    numpy : boolean, default False

        Direct decoding to numpy arrays. Supports numeric data only, but non-numeric column and index labels are supported. Note also that the JSON ordering MUST be the same for each term if numpy=True.

    precise_float : boolean, default False

        Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionality

    date_unit : string, default None

        The timestamp unit to detect if converting dates. The default behaviour is to try and detect the correct precision, but if this is not desired then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively.

    lines : boolean, default False

        Read the file as a json object per line.

        New in version 0.19.0.

    encoding : str, default is ‘utf-8’

        The encoding to use to decode py3 bytes.

        New in version 0.19.0.