Fix: test_concat.py and internals.py

Everything tests fine, except for pytest.

Author: jaehoonhwang

pandas/core/internals.py

@@ -4928,8 +4928,8 @@ def get_empty_dtype_and_na(join_units):
     elif 'timedelta' in upcast_classes:
         return np.dtype('m8[ns]'), tslib.iNaT
     else:  # pragma
-        g = pandas.types._.find_common_type(upcast_classes, [])
-        if is_float_type(g):
+        g = np.find_common_type(upcast_classes, [])
+        if is_float_dtype(g):
             return g, g.type(np.nan)
         elif is_numeric_dtype(g):
             if has_none_blocks:

pandas/tests/tools/test_concat.py

@@ -13,6 +13,7 @@
                                  makeCustomDataframe as mkdf,
                                  assert_almost_equal)
 
+import pytest
 
 class ConcatenateBase(tm.TestCase):
 
@@ -1900,7 +1901,7 @@ def test_concat_multiindex_dfs_with_deepcopy(self):
         result_no_copy = pd.concat(example_dict, names=['testname'])
         tm.assert_frame_equal(result_no_copy, expected)
 
-    def test_concat_no_unnecessary_upcats(self):
+    def test_concat_no_unnecessary_upcast(self):
         # GH 13247
         for pdt in [pd.Series, pd.DataFrame, pd.Panel, pd.Panel4D]:
             dims = pdt().ndim
@@ -1933,30 +1934,28 @@ def test_concat_no_unnecessary_upcats(self):
             objs.append(pdt(np.array([1], dtype=np.float16, ndmin=dims)))
             self.assertTrue(pd.concat(objs).values.dtype == np.float64)
 
-    @pytest.mark.parametrize('dtype', np.sctypes('float'))
-    @pytest.mark.parametrize('klass', [Series, DataFrame, Panel])
-    def test_concat_no_unnecessary_upcats_pytest(self, dtype, klass):
-        # GH 13247
-        for pdt in klass:
-            dims = pdt().ndim
-            for dt in dtype:
-                dfs = [pdt(np.array([1], dtype=dt, ndmin=dims)),
-                       pdt(np.array([np.nan], dtype=dt, ndmin=dims)),
-                       pdt(np.array([5], dtype=dt, ndmin=dims))]
-                x = pd.concat(dfs)
-                self.assertTrue(x.values.dtype == dt)
 
-            objs = []
-            objs.append(pdt(np.array([1], dtype=np.float32, ndmin=dims)))
-            objs.append(pdt(np.array([1], dtype=np.float16, ndmin=dims)))
-            self.assertTrue(pd.concat(objs).values.dtype == np.float32)
-
-            objs = []
-            objs.append(pdt(np.array([1], dtype=np.int32, ndmin=dims)))
-            objs.append(pdt(np.array([1], dtype=np.int64, ndmin=dims)))
-            self.assertTrue(pd.concat(objs).values.dtype == np.int64)
-
-            objs = []
-            objs.append(pdt(np.array([1], dtype=np.int32, ndmin=dims)))
-            objs.append(pdt(np.array([1], dtype=np.float16, ndmin=dims)))
-            self.assertTrue(pd.concat(objs).values.dtype == np.float64)
+@pytest.mark.parametrize('pdt', [Series, DataFrame, Panel])
+def test_concat_no_unnecessary_upcast_pytest(pdt):
+    dims = pdt().ndim
+    for dt in np.sctypes['float']:
+        dfs = [pdt(np.array([1], dtype=dt, ndmin=dims)),
+               pdt(np.array([np.nan], dtype=dt, ndmin=dims)),
+               pdt(np.array([5], dtype=dt, ndmin=dims))]
+        x = pd.concat(dfs)
+        assert x.values.dtype == dt
+
+        objs = []
+        objs.append(pdt(np.array([1], dtype=np.float32, ndmin=dims)))
+        objs.append(pdt(np.array([1], dtype=np.float16, ndmin=dims)))
+        assert pd.concat(objs).values.dtype == np.float32
+
+        objs = []
+        objs.append(pdt(np.array([1], dtype=np.int32, ndmin=dims)))
+        objs.append(pdt(np.array([1], dtype=np.int64, ndmin=dims)))
+        assert pd.concat(objs).values.dtype == np.int64
+
+        objs = []
+        objs.append(pdt(np.array([1], dtype=np.int32, ndmin=dims)))
+        objs.append(pdt(np.array([1], dtype=np.float16, ndmin=dims)))
+        assert pd.concat(objs).values.dtype == np.float64