Compare with empty DataFrame, not just check empty

Author: BranYang

pandas/core/reshape.py

@@ -1095,8 +1095,7 @@ def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False,
     cat = Categorical.from_array(Series(data), ordered=True)
     levels = cat.categories
 
-    # if all NaN
-    if not dummy_na and len(levels) == 0:
+    def get_empty_Frame(data, sparse):
         if isinstance(data, Series):
             index = data.index
         else:
@@ -1106,11 +1105,19 @@ def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False,
         else:
             return SparseDataFrame(index=index)
 
+    # if all NaN
+    if not dummy_na and len(levels) == 0:
+        return get_empty_Frame(data, sparse)
+
     codes = cat.codes.copy()
     if dummy_na:
         codes[codes == -1] = len(cat.categories)
         levels = np.append(cat.categories, np.nan)
 
+    # if dummy_na, we just fake a nan level. drop_first will drop it again
+    if drop_first and len(levels) == 1:
+        return get_empty_Frame(data, sparse)
+
     number_of_cols = len(levels)
 
     if prefix is not None:

pandas/tests/test_reshape.py

@@ -432,13 +432,28 @@ def test_basic_drop_first(self):
         assert_frame_equal(result, expected)
 
         expected.index = list('ABC')
-        result = get_dummies(s_series_index, sparse=self.sparse, drop_first=True)
+        result = get_dummies(s_series_index, sparse=self.sparse,
+                             drop_first=True)
         assert_frame_equal(result, expected)
 
-    # Test the case that categorical variable only has one level.
     def test_basic_drop_first_one_level(self):
-        result = get_dummies(list('aaa'), sparse=self.sparse, drop_first=True)
-        self.assertEqual(result.empty, True)
+        # Test the case that categorical variable only has one level.
+        s_list = list('aaa')
+        s_series = Series(s_list)
+        s_series_index = Series(s_list, list('ABC'))
+
+        expected = DataFrame(index=np.arange(3))
+
+        result = get_dummies(s_list, sparse=self.sparse, drop_first=True)
+        assert_frame_equal(result, expected)
+
+        result = get_dummies(s_series, sparse=self.sparse, drop_first=True)
+        assert_frame_equal(result, expected)
+
+        expected = DataFrame(index=list('ABC'))
+        result = get_dummies(s_series_index, sparse=self.sparse,
+                             drop_first=True)
+        assert_frame_equal(result, expected)
 
     def test_basic_drop_first_NA(self):
         # Test NA hadling together with drop_first
@@ -449,7 +464,6 @@ def test_basic_drop_first_NA(self):
                                2: 0.0}})
         assert_frame_equal(res, exp)
 
-        # Sparse dataframes do not allow nan labelled columns, see #GH8822
         res_na = get_dummies(s_NA, dummy_na=True, sparse=self.sparse,
                              drop_first=True)
         exp_na = DataFrame({'b': {0: 0.0,
@@ -463,7 +477,8 @@ def test_basic_drop_first_NA(self):
 
         res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse,
                                   drop_first=True)
-        tm.assert_numpy_array_equal(res_just_na.empty, True)
+        exp_just_na = DataFrame(index=np.arange(1))
+        assert_frame_equal(res_just_na, exp_just_na)
 
     def test_dataframe_dummies_drop_first(self):
         df = self.df[['A', 'B']]