Correct support for drop_duplicates (#466)

* Correct support for `drop_duplicates`

* Resolves #464
* No longer rely directly on index values
* Corrects issue introduced in #433
* Add tests

* Adding test case validating order after a sort

* Resolves #465

* Lint

* Address comments

Author: devin-petersohn

modin/pandas/dataframe.py

@@ -1277,8 +1277,8 @@ def drop_duplicates(self, subset=None, keep="first", inplace=False):
         """
         inplace = validate_bool_kwarg(inplace, "inplace")
         duplicates = self.duplicated(subset=subset, keep=keep)
-        indices, = duplicates.nonzero()
-        return self.drop(indices, inplace=inplace)
+        indices, = duplicates.values.nonzero()
+        return self.drop(index=self.index[indices], inplace=inplace)
 
     def duplicated(self, subset=None, keep="first"):
         return self._default_to_pandas(

modin/pandas/test/test_dataframe.py

@@ -1486,6 +1486,99 @@ def test_drop_duplicates(data):
     df_equals(modin_df, pandas.DataFrame({"A": [], "B": [], "C": []}))
 
 
+def test_drop_duplicates_with_missing_index_values():
+    data = {
+        "columns": ["value", "time", "id"],
+        "index": [
+            4,
+            5,
+            6,
+            7,
+            8,
+            9,
+            10,
+            11,
+            12,
+            13,
+            14,
+            15,
+            20,
+            21,
+            22,
+            23,
+            24,
+            25,
+            26,
+            27,
+            32,
+            33,
+            34,
+            35,
+            36,
+            37,
+            38,
+            39,
+            40,
+            41,
+        ],
+        "data": [
+            ["3", 1279213398000.0, 88.0],
+            ["3", 1279204682000.0, 88.0],
+            ["0", 1245772835000.0, 448.0],
+            ["0", 1270564258000.0, 32.0],
+            ["0", 1267106669000.0, 118.0],
+            ["7", 1300621123000.0, 5.0],
+            ["0", 1251130752000.0, 957.0],
+            ["0", 1311683506000.0, 62.0],
+            ["9", 1283692698000.0, 89.0],
+            ["9", 1270234253000.0, 64.0],
+            ["0", 1285088818000.0, 50.0],
+            ["0", 1218212725000.0, 695.0],
+            ["2", 1383933968000.0, 348.0],
+            ["0", 1368227625000.0, 257.0],
+            ["1", 1454514093000.0, 446.0],
+            ["1", 1428497427000.0, 134.0],
+            ["1", 1459184936000.0, 568.0],
+            ["1", 1502293302000.0, 599.0],
+            ["1", 1491833358000.0, 829.0],
+            ["1", 1485431534000.0, 806.0],
+            ["8", 1351800505000.0, 101.0],
+            ["0", 1357247721000.0, 916.0],
+            ["0", 1335804423000.0, 370.0],
+            ["24", 1327547726000.0, 720.0],
+            ["0", 1332334140000.0, 415.0],
+            ["0", 1309543100000.0, 30.0],
+            ["18", 1309541141000.0, 30.0],
+            ["0", 1298979435000.0, 48.0],
+            ["14", 1276098160000.0, 59.0],
+            ["0", 1233936302000.0, 109.0],
+        ],
+    }
+
+    pandas_df = pandas.DataFrame(
+        data["data"], index=data["index"], columns=data["columns"]
+    )
+    modin_df = pd.DataFrame(data["data"], index=data["index"], columns=data["columns"])
+    modin_result = modin_df.sort_values(["id", "time"]).drop_duplicates(["id"])
+    pandas_result = pandas_df.sort_values(["id", "time"]).drop_duplicates(["id"])
+    df_equals(modin_result, pandas_result)
+
+
+def test_drop_duplicates_after_sort():
+    data = [
+        {"value": 1, "time": 2},
+        {"value": 1, "time": 1},
+        {"value": 2, "time": 1},
+        {"value": 2, "time": 2},
+    ]
+    modin_df = pd.DataFrame(data)
+    pandas_df = pandas.DataFrame(data)
+
+    modin_result = modin_df.sort_values(["value", "time"]).drop_duplicates(["value"])
+    pandas_result = pandas_df.sort_values(["value", "time"]).drop_duplicates(["value"])
+    df_equals(modin_result, pandas_result)
+
+
 @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
 @pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
 @pytest.mark.parametrize("how", ["any", "all"], ids=["any", "all"])