Added DataFrame.to_numpy

pandas/core/frame.py

@@ -1117,6 +1117,43 @@ def from_dict(cls, data, orient='columns', dtype=None, columns=None):
 
         return cls(data, index=index, columns=columns, dtype=dtype)
 
+    def to_numpy(self):
+        """
+        Convert the DataFrame to a NumPy array.
+
+        The dtype of the returned array will be the common NumPy
+        dtype of all types in the DataFrame. This may require copying
+        data and coercing values, which may be expensive.
+
+        Returns
+        -------
+        array : numpy.ndarray
+
+        See Also
+        --------
+        Series.to_nummpy
+        Series.array
+
+        Examples
+        --------
+        >>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
+
+        With heterogenous data, the lowest common type will have to
+        be used.
+
+        >>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
+        >>> df.to_numpy()
+
+        When 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)
+        """
+        return self.values
+
     def to_dict(self, orient='dict', into=dict):
         """
         Convert the DataFrame to a dictionary.

pandas/tests/frame/test_api.py

@@ -319,6 +319,12 @@ def test_values(self, float_frame, float_string_frame):
         expected = float_frame.reindex(columns=['A', 'B']).values
         assert_almost_equal(arr, expected)
 
+    def test_to_numpy():
+        df = pd.DataFrame({"A": [1, 2], "B": [3, 4.5]})
+        expected = np.array([[1, 2], [3, 4.5]])
+        result = df.to_numpy()
+        tm.assert_numpy_array_equal(result, expected)
+
     def test_transpose(self, float_frame):
         frame = float_frame
         dft = frame.T

pandas/tests/series/test_api.py

@@ -305,6 +305,9 @@ def test_keys(self):
     def test_values(self):
         tm.assert_almost_equal(self.ts.values, self.ts, check_dtype=False)
 
+    def test_to_numpy(self):
+        pass
+
     def test_iteritems(self):
         for idx, val in compat.iteritems(self.series):
             assert val == self.series[idx]