Ignore empty indexes in _merge_index_list

This reflects in the columns index of DataFrame.append, it will ignore
empty indexes (of dtype object)!

Some tests are not passing, but this is due to columns dtypes, not
indexes.

Author: araraonline

pandas/core/indexes/api.py

@@ -276,9 +276,14 @@ def _merge_index_list(index_list,
         When verify_dups=True and at least one of the input indexes contain
         duplicate values. This is error is not raised if
         allow_matching_dups=True and all the indexes have a common identity.
+
+    Notes
+    -----
+    Empty indexes (of object dtype) are forgotten.
     """
     # unique index list (a is b)
     uindex_list = com.get_distinct_objs(index_list)
+    uindex_list = [i for i in uindex_list if not i.is_empty()]
 
     # verify duplicates
     if sort or verify_dups:

pandas/core/indexes/base.py

@@ -1541,6 +1541,9 @@ def is_unique(self):
     def has_duplicates(self):
         return not self.is_unique
 
+    def is_empty(self):
+        return self.inferred_type in ['empty']
+
     def is_boolean(self):
         return self.inferred_type in ['boolean']
 

pandas/tests/reshape/test_append.py

@@ -388,6 +388,35 @@ def test_preserve_index_type(self, sort, index):
         expected = pd.DataFrame([[1, 2, np.nan], [3, 4, 5]], columns=index)
         assert_frame_equal(result, expected)
 
+    def test_ignore_empty_index_dtype(self, sort):
+        # When one of the indexes is empty and of object dtype, it should be
+        # ignored in the result (as empty).
+
+        df1 = pd.DataFrame()
+        df2 = pd.DataFrame([[11, 12, 13]], columns=[1, 2, 3])
+
+        result1 = df1.append(df2, sort=sort)
+        result2 = df2.append(df1, sort=sort)
+
+        expected = df2.copy()
+        assert_frame_equal(result1, expected)
+        assert_frame_equal(result2, expected)
+
+    def test_account_empty_index_dtype(self, sort):
+        # When one of the indexes is empty and of dtype different from object,
+        # it should not be ignored when calculating the result dtype.
+
+        df1 = pd.DataFrame(columns=pd.Float64Index([]))
+        df2 = pd.DataFrame([[11, 12, 13]], columns=[1, 2, 3])
+
+        result1 = df1.append(df2, sort=sort)
+        result2 = df2.append(df1, sort=sort)
+
+        expected = df2.copy()
+        expected.columns = [1.0, 2.0, 3.0]
+        assert_frame_equal(result1, expected)
+        assert_frame_equal(result2, expected)
+
     @pytest.mark.parametrize('index2', indexes, ids=cls_name)
     @pytest.mark.parametrize('index1', indexes, ids=cls_name)
     def test_preserve_index_values_without_sort(self, index1, index2):
@@ -751,6 +780,35 @@ def test_preserve_index_name(self, sort, idx_name1, idx_name2, idx_name3):
         expected.index.name = idx_name1
         assert_frame_equal(result, expected)
 
+    def test_ignore_empty_index_dtype(self, sort):
+        # When one of the indexes is empty and of object dtype, it should be
+        # ignored in the result (as empty).
+
+        df1 = pd.DataFrame()
+        df2 = pd.DataFrame([[11], [12], [13]], index=[1, 2, 3])
+
+        result1 = df1.append(df2, sort=sort)
+        result2 = df2.append(df1, sort=sort)
+
+        expected = df2.copy()
+        assert_frame_equal(result1, expected)
+        assert_frame_equal(result2, expected)
+
+    def test_account_empty_index_dtype(self, sort):
+        # When one of the indexes is empty and of dtype different from object,
+        # it should not be ignored when calculating the result dtype.
+
+        df1 = pd.DataFrame(index=pd.Float64Index([]))
+        df2 = pd.DataFrame([[11], [12], [13]], index=[1, 2, 3])
+
+        result1 = df1.append(df2, sort=sort)
+        result2 = df2.append(df1, sort=sort)
+
+        expected = df2.copy()
+        expected.index = [1.0, 2.0, 3.0]
+        assert_frame_equal(result1, expected)
+        assert_frame_equal(result2, expected)
+
     @pytest.mark.parametrize('index', indexes, ids=cls_name)
     def test_preserve_index_type(self, sort, index):
         # when there's only one index type in the inputs,