Construct DataFrame from dict of array-like or dicts.

        Creates DataFrame object from dictionary by columns or by index
        allowing dtype specification.

        Parameters
        ----------
        data : dict
            Of the form {field : array-like} or {field : dict}.
        orient : {'columns', 'index'}, default 'columns'
            The "orientation" of the data. If the keys of the passed dict
            should be the columns of the resulting DataFrame, pass 'columns'
            (default). Otherwise if the keys should be rows, pass 'index'.
        dtype : dtype, default None
            Data type to force, otherwise infer.
        columns : list, default None
            Column labels to use when ``orient='index'``. Raises a ValueError
            if used with ``orient='columns'``.

            .. versionadded:: 0.23.0

        Returns
        -------
        DataFrame

        See Also
        --------
        DataFrame.from_records : DataFrame from ndarray (structured
            dtype), list of tuples, dict, or DataFrame.
        DataFrame : DataFrame object creation using constructor.

        Examples
        --------
        By default the keys of the dict become the DataFrame columns:

        >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
        >>> pd.DataFrame.from_dict(data)
           col_1 col_2
        0      3     a
        1      2     b
        2      1     c
        3      0     d

        Specify ``orient='index'`` to create the DataFrame using dictionary
        keys as rows:

        >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
        >>> pd.DataFrame.from_dict(data, orient='index')
               0  1  2  3
        row_1  3  2  1  0
        row_2  a  b  c  d

        When using the 'index' orientation, the column names can be
        specified manually:

        >>> pd.DataFrame.from_dict(data, orient='index',
        ...                        columns=['A', 'B', 'C', 'D'])
               A  B  C  D
        row_1  3  2  1  0
        row_2  a  b  c  d

def from_dict(cls, data, orient='columns', dtype=None, columns=None):
        """
        Construct DataFrame from dict of array-like or dicts.

        Creates DataFrame object from dictionary by columns or by index
        allowing dtype specification.

        Parameters
        ----------
        data : dict
            Of the form {field : array-like} or {field : dict}.
        orient : {'columns', 'index'}, default 'columns'
            The "orientation" of the data. If the keys of the passed dict
            should be the columns of the resulting DataFrame, pass 'columns'
            (default). Otherwise if the keys should be rows, pass 'index'.
        dtype : dtype, default None
            Data type to force, otherwise infer.
        columns : list, default None
            Column labels to use when ``orient='index'``. Raises a ValueError
            if used with ``orient='columns'``.

            .. versionadded:: 0.23.0

        Returns
        -------
        DataFrame

        See Also
        --------
        DataFrame.from_records : DataFrame from ndarray (structured
            dtype), list of tuples, dict, or DataFrame.
        DataFrame : DataFrame object creation using constructor.

        Examples
        --------
        By default the keys of the dict become the DataFrame columns:

        >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
        >>> pd.DataFrame.from_dict(data)
           col_1 col_2
        0      3     a
        1      2     b
        2      1     c
        3      0     d

        Specify ``orient='index'`` to create the DataFrame using dictionary
        keys as rows:

        >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
        >>> pd.DataFrame.from_dict(data, orient='index')
               0  1  2  3
        row_1  3  2  1  0
        row_2  a  b  c  d

        When using the 'index' orientation, the column names can be
        specified manually:

        >>> pd.DataFrame.from_dict(data, orient='index',
        ...                        columns=['A', 'B', 'C', 'D'])
               A  B  C  D
        row_1  3  2  1  0
        row_2  a  b  c  d
        """
        index = None
        orient = orient.lower()
        if orient == 'index':
            if len(data) > 0:
                # TODO speed up Series case
                if isinstance(list(data.values())[0], (Series, dict)):
                    data = _from_nested_dict(data)
                else:
                    data, index = list(data.values()), list(data.keys())
        elif orient == 'columns':
            if columns is not None:
                raise ValueError("cannot use columns parameter with "
                                 "orient='columns'")
        else:  # pragma: no cover
            raise ValueError('only recognize index or columns for orient')

        return cls(data, index=index, columns=columns, dtype=dtype)