python - 将包含 pandas 对象(系列/数据帧)的 python 元组、列表、字典转换为 json

Question

我知道我可以将 pandas 对象如 Series、DataFrame 转换为 json，如下所示:

series1 = pd.Series(np.random.randn(5), name='something')
jsonSeries1 = series1.to_json() #{"0":0.0548079371,"1":-0.9072821424,"2":1.3865642993,"3":-1.0609052074,"4":-3.3513341839}

但是当那个系列被封装在其他数据结构中时我该怎么办，说字典如下:

seriesmap = {"key1":pd.Series(np.random.randn(5), name='something')}

我如何像这样将上面的 map 转换为 json:

{"key1":{"0":0.0548079371,"1":-0.9072821424,"2":1.3865642993,"3":-1.0609052074,"4":-3.3513341839}}

simplejson 不起作用:

 jsonObj = simplejson.dumps(seriesmap)

给予

Traceback (most recent call last):
  File "C:\..\py2.py", line 86, in <module>
    jsonObj = json.dumps(seriesmap)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\simplejson\__init__.py", line 380, in dumps
    return _default_encoder.encode(obj)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\simplejson\encoder.py", line 275, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\simplejson\encoder.py", line 357, in iterencode
    return _iterencode(o, 0)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\simplejson\encoder.py", line 252, in default
    raise TypeError(repr(o) + " is not JSON serializable")
TypeError: 0   -0.038824
1   -0.047297
2   -0.887672
3   -1.510238
4    0.900217
Name: something, dtype: float64 is not JSON serializable

为了进一步概括这一点，我想将任意对象转换为 json。任意对象可以是简单的 int、string 或复杂类型，例如元组、列表、包含 pandas 对象的字典以及其他类型。在字典中， Pandas 对象可能位于任意深度作为某个键的值。我想安全地将这种结构转换为有效的 json。可能吗？

更新

我只是尝试将 DataFrame 封装为字典键之一的值，并通过封装在另一个 DataFrame 中将该字典转换为 json(如以下答案中所建议)。但似乎它不起作用:

import pandas as pd

d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
    'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)

mapDict = {"key1":df}
print(pd.DataFrame(mapDict).to_json())

这给出了:

Traceback (most recent call last):
  File "C:\Mahesh\repos\JavaPython\JavaPython\bin\py2.py", line 80, in <module>
    print(pd.DataFrame(mapDict).to_json())
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\pandas\core\frame.py", line 224, in __init__
    mgr = self._init_dict(data, index, columns, dtype=dtype)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\pandas\core\frame.py", line 360, in _init_dict
    return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\pandas\core\frame.py", line 5231, in _arrays_to_mgr
    index = extract_index(arrays)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\pandas\core\frame.py", line 5270, in extract_index
    raise ValueError('If using all scalar values, you must pass'
ValueError: If using all scalar values, you must pass an index

Answer 1

在seriesmap 上调用pd.DataFrame 然后使用to_json

pd.DataFrame(seriesmap).to_json()

'{"key1":{"0":0.8513342674,"1":-1.3357052602,"2":0.2102391775,"3":-0.5957492995,"4":0.2356552588}}'