@staticmethod
def from_records(data: Union[(np.ndarray, List[tuple], dict, pd.DataFrame)], index: Union[(str, list, np.ndarray)]=None, exclude: list=None, columns: list=None, coerce_float: bool=False, nrows: int=None) -> 'DataFrame':
    "\n        Convert structured or record ndarray to DataFrame.\n\n        Parameters\n        ----------\n        data : ndarray (structured dtype), list of tuples, dict, or DataFrame\n        index : string, list of fields, array-like\n            Field of array to use as the index, alternately a specific set of input labels to use\n        exclude : sequence, default None\n            Columns or fields to exclude\n        columns : sequence, default None\n            Column names to use. If the passed data do not have names associated with them, this\n            argument provides names for the columns. Otherwise this argument indicates the order of\n            the columns in the result (any names not found in the data will become all-NA columns)\n        coerce_float : boolean, default False\n            Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to\n            floating point, useful for SQL result sets\n        nrows : int, default None\n            Number of rows to read if data is an iterator\n\n        Returns\n        -------\n        df : DataFrame\n\n        Examples\n        --------\n        Use dict as input\n\n        >>> ps.DataFrame.from_records({'A': [1, 2, 3]})\n           A\n        0  1\n        1  2\n        2  3\n\n        Use list of tuples as input\n\n        >>> ps.DataFrame.from_records([(1, 2), (3, 4)])\n           0  1\n        0  1  2\n        1  3  4\n\n        Use NumPy array as input\n\n        >>> ps.DataFrame.from_records(np.eye(3))\n             0    1    2\n        0  1.0  0.0  0.0\n        1  0.0  1.0  0.0\n        2  0.0  0.0  1.0\n        "
    return DataFrame(pd.DataFrame.from_records(data, index, exclude, columns, coerce_float, nrows))
Convert structured or record ndarray to DataFrame.

Parameters
----------
data : ndarray (structured dtype), list of tuples, dict, or DataFrame
index : string, list of fields, array-like
    Field of array to use as the index, alternately a specific set of input labels to use
exclude : sequence, default None
    Columns or fields to exclude
columns : sequence, default None
    Column names to use. If the passed data do not have names associated with them, this
    argument provides names for the columns. Otherwise this argument indicates the order of
    the columns in the result (any names not found in the data will become all-NA columns)
coerce_float : boolean, default False
    Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to
    floating point, useful for SQL result sets
nrows : int, default None
    Number of rows to read if data is an iterator

Returns
-------
df : DataFrame

Examples
--------
Use dict as input

>>> ps.DataFrame.from_records({'A': [1, 2, 3]})
   A
0  1
1  2
2  3

Use list of tuples as input

>>> ps.DataFrame.from_records([(1, 2), (3, 4)])
   0  1
0  1  2
1  3  4

Use NumPy array as input

>>> ps.DataFrame.from_records(np.eye(3))
     0    1    2
0  1.0  0.0  0.0
1  0.0  1.0  0.0
2  0.0  0.0  1.0
@staticmethod
def from_records(data: Union[(np.ndarray, List[tuple], dict, pd.DataFrame)], index: Union[(str, list, np.ndarray)]=None, exclude: list=None, columns: list=None, coerce_float: bool=False, nrows: int=None) -> 'DataFrame':
    "\n        Convert structured or record ndarray to DataFrame.\n\n        Parameters\n        ----------\n        data : ndarray (structured dtype), list of tuples, dict, or DataFrame\n        index : string, list of fields, array-like\n            Field of array to use as the index, alternately a specific set of input labels to use\n        exclude : sequence, default None\n            Columns or fields to exclude\n        columns : sequence, default None\n            Column names to use. If the passed data do not have names associated with them, this\n            argument provides names for the columns. Otherwise this argument indicates the order of\n            the columns in the result (any names not found in the data will become all-NA columns)\n        coerce_float : boolean, default False\n            Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to\n            floating point, useful for SQL result sets\n        nrows : int, default None\n            Number of rows to read if data is an iterator\n\n        Returns\n        -------\n        df : DataFrame\n\n        Examples\n        --------\n        Use dict as input\n\n        >>> ps.DataFrame.from_records({'A': [1, 2, 3]})\n           A\n        0  1\n        1  2\n        2  3\n\n        Use list of tuples as input\n\n        >>> ps.DataFrame.from_records([(1, 2), (3, 4)])\n           0  1\n        0  1  2\n        1  3  4\n\n        Use NumPy array as input\n\n        >>> ps.DataFrame.from_records(np.eye(3))\n             0    1    2\n        0  1.0  0.0  0.0\n        1  0.0  1.0  0.0\n        2  0.0  0.0  1.0\n        "
    return DataFrame(pd.DataFrame.from_records(data, index, exclude, columns, coerce_float, nrows))