Method to_numpy

pandas/core/frame.py:2020–2085

Convert the DataFrame to a NumPy array. By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are ``float16`` and ``float32``, the results dtype will be ``float32``. This may requ

(
        self,
        dtype: npt.DTypeLike | None = None,
        copy: bool = False,
        na_value: object = lib.no_default,
    )

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

    def to_numpy(
        self,
        dtype: npt.DTypeLike | None = None,
        copy: bool = False,
        na_value: object = lib.no_default,
    ) -> np.ndarray:
        """
        Convert the DataFrame to a NumPy array.

        By default, the dtype of the returned array will be the common NumPy
        dtype of all types in the DataFrame. For example, if the dtypes are
        ``float16`` and ``float32``, the results dtype will be ``float32``.
        This may require copying data and coercing values, which may be
        expensive.

        Parameters
        ----------
        dtype : str or numpy.dtype, optional
            The dtype to pass to :meth:`numpy.asarray`.
        copy : bool, default False
            Whether to ensure that the returned value is not a view on
            another array. Note that ``copy=False`` does not *ensure* that
            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
            a copy is made, even if not strictly necessary.
        na_value : Any, optional
            The value to use for missing values. The default value depends
            on `dtype` and the dtypes of the DataFrame columns.

        Returns
        -------
        numpy.ndarray
            The NumPy array representing the values in the DataFrame.

        See Also
        --------
        Series.to_numpy : Similar method for Series.

        Examples
        --------
        >>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
        array([[1, 3],
               [2, 4]])

        With heterogeneous data, the lowest common type will have to
        be used.

        >>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
        >>> df.to_numpy()
        array([[1. , 3. ],
               [2. , 4.5]])

        For a mix of numeric and non-numeric types, the output array will
        have object dtype.

        >>> df["C"] = pd.date_range("2000", periods=2)
        >>> df.to_numpy()
        array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
               [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)