BUG: Change numeric_only default to True

doc/source/whatsnew/v1.5.0.rst

@@ -352,6 +352,7 @@ Numeric
 - Bug in operations with array-likes with ``dtype="boolean"`` and :attr:`NA` incorrectly altering the array in-place (:issue:`45421`)
 - Bug in division, ``pow`` and ``mod`` operations on array-likes with ``dtype="boolean"`` not being like their ``np.bool_`` counterparts (:issue:`46063`)
 - Bug in multiplying a :class:`Series` with ``IntegerDtype`` or ``FloatingDtype`` by an array-like with ``timedelta64[ns]`` dtype incorrectly raising (:issue:`45622`)
+- Bug in :meth:`DataFrame.quantile` attribute ``numeric_only"`` should default False (:issue:`7308`)
 -
 
 Conversion

pandas/core/frame.py

@@ -10558,7 +10558,7 @@ def quantile(
         self,
         q=0.5,
         axis: Axis = 0,
-        numeric_only: bool = True,
+        numeric_only: bool = False,
         interpolation: str = "linear",
     ):
         """
@@ -10570,7 +10570,7 @@ def quantile(
             Value between 0 <= q <= 1, the quantile(s) to compute.
         axis : {0, 1, 'index', 'columns'}, default 0
             Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
-        numeric_only : bool, default True
+        numeric_only : bool, default False
             If False, the quantile of datetime and timedelta data will be
             computed as well.
         interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}

pandas/tests/frame/methods/test_quantile.py

@@ -14,6 +14,23 @@
 
 
 class TestDataFrameQuantile:
+    def test_numeric_only_default_false(self):
+        # GH #7308
+        df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]})
+        df["C"] = pd.date_range("2014-01-01", periods=3, freq="m")
+
+        df_expected_num_only_false = Series(
+            [2.0, 3.0, Timestamp("2014-02-28 00:00:00")],
+            index=["A", "B", "C"],
+            name=0.5,
+        )
+        tm.assert_series_equal(df.quantile(0.5), df_expected_num_only_false)
+
+        df_expected_num_only_true = Series([2.0, 3.0], index=["A", "B"], name=0.5)
+        tm.assert_series_equal(
+            df.quantile(0.5, numeric_only=True), df_expected_num_only_true
+        )
+
     @pytest.mark.parametrize(
         "df,expected",
         [