Question

我最近更新到pandas 0.20.1并尝试使用to_json的新功能（orient =＆＃39; table＆＃39;）

import pandas as pd
pd.__version__
# '0.20.1'

a = pd.DataFrame({'a':[1,2,3], 'b':[4,5,6]})
a.to_json('a.json', orient='table')

但是如何将此JSON文件读取到DataFrame？

我尝试了pd.read_json('a.json', orient='table')，但提出了ValueError

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-22-7527b25107ef> in <module>()
----> 1 pd.read_json('a.json', orient='table')

C:\Anaconda3\lib\site-packages\pandas\io\json\json.py in read_json(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines)
    352         obj = FrameParser(json, orient, dtype, convert_axes, convert_dates,
    353                           keep_default_dates, numpy, precise_float,
--> 354                           date_unit).parse()
    355 
    356     if typ == 'series' or obj is None:

C:\Anaconda3\lib\site-packages\pandas\io\json\json.py in parse(self)
    420 
    421         else:
--> 422             self._parse_no_numpy()
    423 
    424         if self.obj is None:

C:\Anaconda3\lib\site-packages\pandas\io\json\json.py in _parse_no_numpy(self)
    650         else:
    651             self.obj = DataFrame(
--> 652                 loads(json, precise_float=self.precise_float), dtype=None)
    653 
    654     def _process_converter(self, f, filt=None):

C:\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
    273                                  dtype=dtype, copy=copy)
    274         elif isinstance(data, dict):
--> 275             mgr = self._init_dict(data, index, columns, dtype=dtype)
    276         elif isinstance(data, ma.MaskedArray):
    277             import numpy.ma.mrecords as mrecords

C:\Anaconda3\lib\site-packages\pandas\core\frame.py in _init_dict(self, data, index, columns, dtype)
    409             arrays = [data[k] for k in keys]
    410 
--> 411         return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
    412 
    413     def _init_ndarray(self, values, index, columns, dtype=None, copy=False):

C:\Anaconda3\lib\site-packages\pandas\core\frame.py in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
   5592     # figure out the index, if necessary
   5593     if index is None:
-> 5594         index = extract_index(arrays)
   5595     else:
   5596         index = _ensure_index(index)

C:\Anaconda3\lib\site-packages\pandas\core\frame.py in extract_index(data)
   5643 
   5644             if have_dicts:
-> 5645                 raise ValueError('Mixing dicts with non-Series may lead to '
   5646                                  'ambiguous ordering.')
   5647 

ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.

那么有没有办法可以读取该JSON文件？提前谢谢。

PS：JSON文件如下所示：

{"schema": {"pandas_version":"0.20.0","fields":[{"type":"integer","name":"index"},{"type":"integer","name":"a"},{"type":"integer","name":"b"}],"primaryKey":["index"]}, "data": [{"index":0,"a":1,"b":4},{"index":1,"a":2,"b":5},{"index":2,"a":3,"b":6}]}

Answer 1

显然，新方法会将数据集中的一些元数据输出到json中，例如pandas版本。因此，请考虑使用内置的json模块读取此嵌套对象以提取 data 键的值：

import json
...
with open('a.json', 'r') as f:   
    json_obj = json.loads(f.read())
    df = pd.DataFrame(json_obj['data']).set_index('index')
    df.index.name = None

print(df)          
#    a  b
# 0  1  4
# 1  2  5
# 2  3  6

如果您打算使用 type 和 name ，请在嵌套json中对这些部分运行字典和列表解析。虽然在这里，整数必须切成 int 。无法使用 dtype 参数，因为直到步骤之后才会保存名称：

with open('a.json', 'r') as f:   
    json_obj = json.loads(f.read())
    df = pd.DataFrame(json_obj['data'], columns=[t['name'] 
                        for t in json_obj['schema']['fields']])
    df = df.astype(dtype={t['name']: t['type'][:3] 
                        for t in json_obj['schema']['fields']}).set_index('index')
    df.index.name = None

print(df)
#    a  b
# 0  1  4
# 1  2  5
# 2  3  6

Answer 2

这是我从Parfait回答的一个函数：

def table_to_df(table):
    df = pd.DataFrame(table['data'],
                      columns=[t['name'] for t in table['schema']['fields']])
    for t in table['schema']['fields']:
        if t['type'] == "datetime":
            df[t['name']] = pd.to_datetime(df[t['name']], infer_datetime_format=True)
    df.set_index(table['schema']['primaryKey'], inplace=True)
    return df

有没有办法可以读取dataframe.to_json保存的JSON文件（orient =＆＃39; table＆＃39;）？

2 个答案: