Bug in DataFrame.drop_duplicates for empty DataFrame throws error

doc/source/whatsnew/v0.24.0.txt

@@ -711,7 +711,7 @@ Reshaping
 - Bug in :func:`get_dummies` with Unicode attributes in Python 2 (:issue:`22084`)
 - Bug in :meth:`DataFrame.replace` raises ``RecursionError`` when replacing empty lists (:issue:`22083`)
 - Bug in :meth:`Series.replace` and meth:`DataFrame.replace` when dict is used as the `to_replace` value and one key in the dict is is another key's value, the results were inconsistent between using integer key and using string key (:issue:`20656`)
--
+- Bug in :meth:`DataFrame.drop_duplicates` for empty ``DataFrame`` which incorrectly raises error (:issue:`20516`)
 
 Build Changes
 ^^^^^^^^^^^^^

pandas/core/frame.py

@@ -4369,6 +4369,9 @@ def duplicated(self, subset=None, keep='first'):
         from pandas.core.sorting import get_group_index
         from pandas._libs.hashtable import duplicated_int64, _SIZE_HINT_LIMIT
 
+        if self.empty:
+            return Series()
+
         def f(vals):
             labels, shape = algorithms.factorize(
                 vals, size_hint=min(len(self), _SIZE_HINT_LIMIT))

pandas/tests/frame/test_duplicates.py

@@ -263,6 +263,30 @@ def test_drop_duplicates_tuple():
     tm.assert_frame_equal(result, expected)
 
 
+def test_drop_duplicates_empty():
+    # GH 20516
+    expected = DataFrame()
+    result = expected.drop_duplicates()
+    tm.assert_frame_equal(result, expected)
+
+    expected = DataFrame(columns=[])
+    result = expected.drop_duplicates()
+    tm.assert_frame_equal(result, expected)
+
+    df = DataFrame(columns=['A', 'B', 'C'])
+    result = df.drop_duplicates()
+    expected = DataFrame(columns=[])  # The column infos are not carrying over
+    tm.assert_frame_equal(result, expected)
+
+    expected = DataFrame(index=[])
+    result = expected.drop_duplicates()
+    tm.assert_frame_equal(result, expected)
+
+    expected = DataFrame(index=['A', 'B', 'C'])
+    result = expected.drop_duplicates()
+    tm.assert_frame_equal(result, expected)
+
+
 def test_drop_duplicates_NA():
     # none
     df = DataFrame({'A': [None, None, 'foo', 'bar',