Fixing regression in DataFrame.all and DataFrame.any

* `bool_only` parameter is supported again
* Commit 36ab8c9 created this regression
  due to a bug in all/any (pandas-dev#23070)
* Reverted the regression and fixed the bug with a condition
* Added tests for `bool_only` parameter

Author: devin-petersohn

doc/source/whatsnew/v0.24.1.rst

@@ -56,6 +56,7 @@ Fixed Regressions
 - Fixed regression in :class:`Index.intersection` incorrectly sorting the values by default (:issue:`24959`).
 - Fixed regression in :func:`merge` when merging an empty ``DataFrame`` with multiple timezone-aware columns on one of the timezone-aware columns (:issue:`25014`).
 - Fixed regression in :meth:`Series.rename_axis` and :meth:`DataFrame.rename_axis` where passing ``None`` failed to remove the axis name (:issue:`25034`)
+- Fixed regression in :meth:`DataFrame.all` and :meth:`DataFrame.any` where ``bool_only`` as ``True`` was ignored (:issue:`25101`)
 
 **Timedelta**
 

pandas/core/frame.py

@@ -7476,7 +7476,8 @@ def f(x):
                 if filter_type is None or filter_type == 'numeric':
                     data = self._get_numeric_data()
                 elif filter_type == 'bool':
-                    data = self
+                    # GH 25101, # GH 24434
+                    data = self._get_bool_data() if axis == 0 else self
                 else:  # pragma: no cover
                     msg = ("Generating numeric_only data with filter_type {f}"
                            "not supported.".format(f=filter_type))

pandas/tests/frame/test_analytics.py

@@ -1442,6 +1442,26 @@ def test_any_datetime(self):
         expected = Series([True, True, True, False])
         tm.assert_series_equal(result, expected)
 
+    def test_any_all_bool_only(self):
+
+        # GH 25101
+        df = DataFrame({"col1": [1,2,3],
+                        "col2": [4,5,6],
+                        "col3": [None, None, None]})
+
+        result = df.all(bool_only=True)
+        expected = Series()
+        tm.assert_equal(result, expected)
+
+        df = DataFrame({"col1": [1,2,3],
+                        "col2": [4,5,6],
+                        "col3": [None, None, None],
+                        "col4":[False, False, True]}
+
+        result = df.all(bool_only=True)
+        expected = Series()
+        tm.assert_equal(result, expected)
+
     @pytest.mark.parametrize('func, data, expected', [
         (np.any, {}, False),
         (np.all, {}, True),