def to_records(self, index=True, convert_datetime64=None,
                   column_dtypes=None, index_dtypes=None):
        """
        Convert DataFrame to a NumPy record array.

        Index will be included as the first field of the record array if
        requested.

        Parameters
        ----------
        index : bool, default True
            Include index in resulting record array, stored in 'index'
            field or using the index label, if set.
        convert_datetime64 : bool, default None
            .. deprecated:: 0.23.0

            Whether to convert the index to datetime.datetime if it is a
            DatetimeIndex.
        column_dtypes : str, type, dict, default None
            .. versionadded:: 0.24.0

            If a string or type, the data type to store all columns. If
            a dictionary, a mapping of column names and indices (zero-indexed)
            to specific data types.
        index_dtypes : str, type, dict, default None
            .. versionadded:: 0.24.0

            If a string or type, the data type to store all index levels. If
            a dictionary, a mapping of index level names and indices
            (zero-indexed) to specific data types.

            This mapping is applied only if `index=True`.

        Returns
        -------
        numpy.recarray
            NumPy ndarray with the DataFrame labels as fields and each row
            of the DataFrame as entries.

        See Also
        --------
        DataFrame.from_records: Convert structured or record ndarray
            to DataFrame.
        numpy.recarray: An ndarray that allows field access using
            attributes, analogous to typed columns in a
            spreadsheet.

        Examples
        --------
        >>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
        ...                   index=['a', 'b'])
        >>> df
           A     B
        a  1  0.50
        b  2  0.75
        >>> df.to_records()
        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                  dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

        If the DataFrame index has no label then the recarray field name
        is set to 'index'. If the index has a label then this is used as the
        field name:

        >>> df.index = df.index.rename("I")
        >>> df.to_records()
        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                  dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

        The index can be excluded from the record array:

        >>> df.to_records(index=False)
        rec.array([(1, 0.5 ), (2, 0.75)],
                  dtype=[('A', '<i8'), ('B', '<f8')])

        Data types can be specified for the columns:

        >>> df.to_records(column_dtypes={"A": "int32"})
        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                  dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

        As well as for the index:

        >>> df.to_records(index_dtypes="<S2")
        rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
                  dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])

        >>> index_dtypes = "<S{}".format(df.index.str.len().max())
        >>> df.to_records(index_dtypes=index_dtypes)
        rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
                  dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
        """

        if convert_datetime64 is not None:
            warnings.warn("The 'convert_datetime64' parameter is "
                          "deprecated and will be removed in a future "
                          "version",
                          FutureWarning, stacklevel=2)

        if index:
            if is_datetime64_any_dtype(self.index) and convert_datetime64:
                ix_vals = [self.index.to_pydatetime()]
            else:
                if isinstance(self.index, MultiIndex):
                    # array of tuples to numpy cols. copy copy copy
                    ix_vals = lmap(np.array, zip(*self.index.values))
                else:
                    ix_vals = [self.index.values]

            arrays = ix_vals + [self[c].get_values() for c in self.columns]

            count = 0
            index_names = list(self.index.names)

            if isinstance(self.index, MultiIndex):
                for i, n in enumerate(index_names):
                    if n is None:
                        index_names[i] = 'level_%d' % count
                        count += 1
            elif index_names[0] is None:
                index_names = ['index']

            names = lmap(str, index_names) + lmap(str, self.columns)
        else:
            arrays = [self[c].get_values() for c in self.columns]
            names = lmap(str, self.columns)
            index_names = []

        index_len = len(index_names)
        formats = []

        for i, v in enumerate(arrays):
            index = i

            # When the names and arrays are collected, we
            # first collect those in the DataFrame's index,
            # followed by those in its columns.
            #
            # Thus, the total length of the array is:
            # len(index_names) + len(DataFrame.columns).
            #
            # This check allows us to see whether we are
            # handling a name / array in the index or column.
            if index < index_len:
                dtype_mapping = index_dtypes
                name = index_names[index]
            else:
                index -= index_len
                dtype_mapping = column_dtypes
                name = self.columns[index]

            # We have a dictionary, so we get the data type
            # associated with the index or column (which can
            # be denoted by its name in the DataFrame or its
            # position in DataFrame's array of indices or
            # columns, whichever is applicable.
            if is_dict_like(dtype_mapping):
                if name in dtype_mapping:
                    dtype_mapping = dtype_mapping[name]
                elif index in dtype_mapping:
                    dtype_mapping = dtype_mapping[index]
                else:
                    dtype_mapping = None

            # If no mapping can be found, use the array's
            # dtype attribute for formatting.
            #
            # A valid dtype must either be a type or
            # string naming a type.
            if dtype_mapping is None:
                formats.append(v.dtype)
            elif isinstance(dtype_mapping, (type, np.dtype, str)):
                formats.append(dtype_mapping)
            else:
                element = "row" if i < index_len else "column"
                msg = ("Invalid dtype {dtype} specified for "
                       "{element} {name}").format(dtype=dtype_mapping,
                                                  element=element, name=name)
                raise ValueError(msg)

        return np.rec.fromarrays(
            arrays,
            dtype={'names': names, 'formats': formats}
        )
Convert DataFrame to a NumPy record array.

        Index will be included as the first field of the record array if
        requested.

        Parameters
        ----------
        index : bool, default True
            Include index in resulting record array, stored in 'index'
            field or using the index label, if set.
        convert_datetime64 : bool, default None
            .. deprecated:: 0.23.0

            Whether to convert the index to datetime.datetime if it is a
            DatetimeIndex.
        column_dtypes : str, type, dict, default None
            .. versionadded:: 0.24.0

            If a string or type, the data type to store all columns. If
            a dictionary, a mapping of column names and indices (zero-indexed)
            to specific data types.
        index_dtypes : str, type, dict, default None
            .. versionadded:: 0.24.0

            If a string or type, the data type to store all index levels. If
            a dictionary, a mapping of index level names and indices
            (zero-indexed) to specific data types.

            This mapping is applied only if `index=True`.

        Returns
        -------
        numpy.recarray
            NumPy ndarray with the DataFrame labels as fields and each row
            of the DataFrame as entries.

        See Also
        --------
        DataFrame.from_records: Convert structured or record ndarray
            to DataFrame.
        numpy.recarray: An ndarray that allows field access using
            attributes, analogous to typed columns in a
            spreadsheet.

        Examples
        --------
        >>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
        ...                   index=['a', 'b'])
        >>> df
           A     B
        a  1  0.50
        b  2  0.75
        >>> df.to_records()
        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                  dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

        If the DataFrame index has no label then the recarray field name
        is set to 'index'. If the index has a label then this is used as the
        field name:

        >>> df.index = df.index.rename("I")
        >>> df.to_records()
        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                  dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

        The index can be excluded from the record array:

        >>> df.to_records(index=False)
        rec.array([(1, 0.5 ), (2, 0.75)],
                  dtype=[('A', '<i8'), ('B', '<f8')])

        Data types can be specified for the columns:

        >>> df.to_records(column_dtypes={"A": "int32"})
        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                  dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

        As well as for the index:

        >>> df.to_records(index_dtypes="<S2")
        rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
                  dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])

        >>> index_dtypes = "<S{}".format(df.index.str.len().max())
        >>> df.to_records(index_dtypes=index_dtypes)
        rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
                  dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])