@staticmethod
def from_dict(data: Dict[(Name, Sequence[Any])], orient: str='columns', dtype: Union[(str, Dtype)]=None, columns: Optional[List[Name]]=None) -> 'DataFrame':
    '\n        Construct DataFrame from dict of array-like or dicts.\n\n        Creates DataFrame object from dictionary by columns or by index\n        allowing dtype specification.\n\n        Parameters\n        ----------\n        data : dict\n            Of the form {field : array-like} or {field : dict}.\n        orient : {\'columns\', \'index\'}, default \'columns\'\n            The "orientation" of the data. If the keys of the passed dict\n            should be the columns of the resulting DataFrame, pass \'columns\'\n            (default). Otherwise if the keys should be rows, pass \'index\'.\n        dtype : dtype, default None\n            Data type to force, otherwise infer.\n        columns : list, default None\n            Column labels to use when ``orient=\'index\'``. Raises a ValueError\n            if used with ``orient=\'columns\'``.\n\n        Returns\n        -------\n        DataFrame\n\n        See Also\n        --------\n        DataFrame.from_records : DataFrame from structured ndarray, sequence\n            of tuples or dicts, or DataFrame.\n        DataFrame : DataFrame object creation using constructor.\n\n        Examples\n        --------\n        By default the keys of the dict become the DataFrame columns:\n\n        >>> data = {\'col_1\': [3, 2, 1, 0], \'col_2\': [10, 20, 30, 40]}\n        >>> ps.DataFrame.from_dict(data)\n           col_1  col_2\n        0      3     10\n        1      2     20\n        2      1     30\n        3      0     40\n\n        Specify ``orient=\'index\'`` to create the DataFrame using dictionary\n        keys as rows:\n\n        >>> data = {\'row_1\': [3, 2, 1, 0], \'row_2\': [10, 20, 30, 40]}\n        >>> ps.DataFrame.from_dict(data, orient=\'index\').sort_index()\n                0   1   2   3\n        row_1   3   2   1   0\n        row_2  10  20  30  40\n\n        When using the \'index\' orientation, the column names can be\n        specified manually:\n\n        >>> ps.DataFrame.from_dict(data, orient=\'index\',\n        ...                        columns=[\'A\', \'B\', \'C\', \'D\']).sort_index()\n                A   B   C   D\n        row_1   3   2   1   0\n        row_2  10  20  30  40\n        '
    return DataFrame(pd.DataFrame.from_dict(data, orient=orient, dtype=dtype, columns=columns))
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'``.

Returns
-------
DataFrame

See Also
--------
DataFrame.from_records : DataFrame from structured ndarray, sequence
    of tuples or dicts, 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': [10, 20, 30, 40]}
>>> ps.DataFrame.from_dict(data)
   col_1  col_2
0      3     10
1      2     20
2      1     30
3      0     40

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

>>> data = {'row_1': [3, 2, 1, 0], 'row_2': [10, 20, 30, 40]}
>>> ps.DataFrame.from_dict(data, orient='index').sort_index()
        0   1   2   3
row_1   3   2   1   0
row_2  10  20  30  40

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

>>> ps.DataFrame.from_dict(data, orient='index',
...                        columns=['A', 'B', 'C', 'D']).sort_index()
        A   B   C   D
row_1   3   2   1   0
row_2  10  20  30  40
@staticmethod
def from_dict(data: Dict[(Name, Sequence[Any])], orient: str='columns', dtype: Union[(str, Dtype)]=None, columns: Optional[List[Name]]=None) -> 'DataFrame':
    '\n        Construct DataFrame from dict of array-like or dicts.\n\n        Creates DataFrame object from dictionary by columns or by index\n        allowing dtype specification.\n\n        Parameters\n        ----------\n        data : dict\n            Of the form {field : array-like} or {field : dict}.\n        orient : {\'columns\', \'index\'}, default \'columns\'\n            The "orientation" of the data. If the keys of the passed dict\n            should be the columns of the resulting DataFrame, pass \'columns\'\n            (default). Otherwise if the keys should be rows, pass \'index\'.\n        dtype : dtype, default None\n            Data type to force, otherwise infer.\n        columns : list, default None\n            Column labels to use when ``orient=\'index\'``. Raises a ValueError\n            if used with ``orient=\'columns\'``.\n\n        Returns\n        -------\n        DataFrame\n\n        See Also\n        --------\n        DataFrame.from_records : DataFrame from structured ndarray, sequence\n            of tuples or dicts, or DataFrame.\n        DataFrame : DataFrame object creation using constructor.\n\n        Examples\n        --------\n        By default the keys of the dict become the DataFrame columns:\n\n        >>> data = {\'col_1\': [3, 2, 1, 0], \'col_2\': [10, 20, 30, 40]}\n        >>> ps.DataFrame.from_dict(data)\n           col_1  col_2\n        0      3     10\n        1      2     20\n        2      1     30\n        3      0     40\n\n        Specify ``orient=\'index\'`` to create the DataFrame using dictionary\n        keys as rows:\n\n        >>> data = {\'row_1\': [3, 2, 1, 0], \'row_2\': [10, 20, 30, 40]}\n        >>> ps.DataFrame.from_dict(data, orient=\'index\').sort_index()\n                0   1   2   3\n        row_1   3   2   1   0\n        row_2  10  20  30  40\n\n        When using the \'index\' orientation, the column names can be\n        specified manually:\n\n        >>> ps.DataFrame.from_dict(data, orient=\'index\',\n        ...                        columns=[\'A\', \'B\', \'C\', \'D\']).sort_index()\n                A   B   C   D\n        row_1   3   2   1   0\n        row_2  10  20  30  40\n        '
    return DataFrame(pd.DataFrame.from_dict(data, orient=orient, dtype=dtype, columns=columns))