使用Pandas可视化自定义Python数组对象中的数据

我正在尝试实现一个具有数组接口的python对象，我想看看是否可以使用pandas来可视化对象中的数据。我基本上是在尝试这个例子。numpy版本是

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))

现在我想做这样的事情

import pandas as pd
import <my_module> as np
a = <array object from my module of size (6,4) with python float values>
df = pd.DataFrame(a, index=dates, columns=list('ABCD'))

因此，从技术上讲，我所尝试的应该是可行的，我希望，如果可以使用np.array创建一个numpy数组对象，pandas应该能够处理该对象，就像它处理传入numpy数组时的情况一样。相反，它似乎试图强制执行数组大小为0的条件。这是熊猫虫还是我在尝试不可行的东西

很抱歉发了这么长的帖子，但这是我唯一能传达完整问题的方法。我真的很感激任何能帮我弄清楚到底发生了什么的回复

编辑

这是我认为需要的改变。有了这个，我似乎得到了我想要的。希望熊猫队的人能回应

    else:
        try:
            arr = np.array(data, dtype=dtype, copy=copy)
        except (ValueError, TypeError) as e:
            exc = TypeError('DataFrame constructor called with '
                            'incompatible data and dtype: %s' % e)
            raise_with_traceback(exc)

        if isinstance(arr, (np.ndarray, Series, Index)):
            if data.dtype.names:
                data_columns = list(data.dtype.names)
                data = dict((k, data[k]) for k in data_columns)
            if columns is None:
                columns = data_columns
                mgr = self._init_dict(data, index, columns, dtype=dtype)
            elif getattr(data, 'name', None):
                mgr = self._init_dict({data.name: data}, index, columns,
                                      dtype=dtype)
            else:
                mgr = self._init_ndarray(data, index, columns, dtype=dtype,
                                         copy=copy)

        elif arr.ndim == 0 and index is not None and columns is not None:
            if isinstance(data, compat.string_types) and dtype is None:
                dtype = np.object_
            if dtype is None:
                dtype, data = _infer_dtype_from_scalar(data)

            values = np.empty((len(index), len(columns)), dtype=dtype)
            values.fill(data)
            mgr = self._init_ndarray(values, index, columns, dtype=dtype,
                                     copy=False)
        else:
            raise PandasError('DataFrame constructor not properly called!')

>>> import numpy as np
>>> import <my_module> as gnp
>>> a = <array object from my_module of size (6,4) with python float values>
>>> anp = np.array(a)
>>> print(anp)
array([[ 1.65596968, -2.39189608, -2.92223678, -1.0312887 ],
       [ 0.41918582,  2.35760521,  3.97045546,  1.06558325],
       [-0.29084453,  1.43759343,  2.94189788,  0.07185253],
       [-1.51164217,  0.9233231 ,  2.05859341,  2.62394528],
       [ 0.12106421, -1.03187945,  2.36255711,  0.86845971],
       [-1.39774121,  5.17955785, -1.05458446, -2.10051246]])

 def __init__(self, data=None, index=None, columns=None, dtype=None,
             copy=False):

     elif isinstance(data, (np.ndarray, Series, Index)):
          ....
     else:
         try:
             arr = np.array(data, dtype=dtype, copy=copy)
         except (ValueError, TypeError) as e:
             exc = TypeError('DataFrame constructor called with '
                            'incompatible data and dtype: %s' % e)
             raise_with_traceback(exc)
         if arr.ndim == 0 and index is not None and columns is not None:
             ....
         else:
             raise PandasError('DataFrame constructor not properly called!')

    else:
        try:
            arr = np.array(data, dtype=dtype, copy=copy)
        except (ValueError, TypeError) as e:
            exc = TypeError('DataFrame constructor called with '
                            'incompatible data and dtype: %s' % e)
            raise_with_traceback(exc)

        if isinstance(arr, (np.ndarray, Series, Index)):
            if data.dtype.names:
                data_columns = list(data.dtype.names)
                data = dict((k, data[k]) for k in data_columns)
            if columns is None:
                columns = data_columns
                mgr = self._init_dict(data, index, columns, dtype=dtype)
            elif getattr(data, 'name', None):
                mgr = self._init_dict({data.name: data}, index, columns,
                                      dtype=dtype)
            else:
                mgr = self._init_ndarray(data, index, columns, dtype=dtype,
                                         copy=copy)

        elif arr.ndim == 0 and index is not None and columns is not None:
            if isinstance(data, compat.string_types) and dtype is None:
                dtype = np.object_
            if dtype is None:
                dtype, data = _infer_dtype_from_scalar(data)

            values = np.empty((len(index), len(columns)), dtype=dtype)
            values.fill(data)
            mgr = self._init_ndarray(values, index, columns, dtype=dtype,
                                     copy=False)
        else:
            raise PandasError('DataFrame constructor not properly called!')