[SPARK-44289][SPARK-43874][SPARK-43869][SPARK-43607][PS] Support `ind…

…exer_between_time` for pandas 2.0.0 & enabling more tests

### What changes were proposed in this pull request?

This PR proposes to support `DatetimeIndex.indexer_between_time` to support pandas 2.0.0 and above. See pandas-dev/pandas#43248 for more detail.

This PR also enables bunch of tests for `Series`, `Index` and `GroupBy`.

### Why are the changes needed?

To match the behavior with latest pandas.

### Does this PR introduce _any_ user-facing change?

`DatetimeIndex.indexer_between_time` now has the same behavior with the latest pandas.

### How was this patch tested?

Enabling & updating the existing UTs and doctests.

Closes #42533 from itholic/enable-many-tests.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Ruifeng Zheng <ruifengz@apache.org>

python/pyspark/pandas/groupby.py

@@ -4165,6 +4165,7 @@ def value_counts(
 
         Examples
         --------
+        >>> import numpy as np
         >>> df = ps.DataFrame({'A': [1, 2, 2, 3, 3, 3],
         ...                    'B': [1, 1, 2, 3, 3, np.nan]},
         ...                   columns=['A', 'B'])
@@ -4183,7 +4184,7 @@ def value_counts(
         2  1.0    1
            2.0    1
         3  3.0    2
-        Name: B, dtype: int64
+        Name: count, dtype: int64
 
         Don't include counts of NaN when dropna is False.
 
@@ -4195,7 +4196,7 @@ def value_counts(
            2.0    1
         3  3.0    2
            NaN    1
-        Name: B, dtype: int64
+        Name: count, dtype: int64
         """
         warnings.warn(
             "The resulting Series will have a fixed name of 'count' from 4.0.0.",
@@ -4232,7 +4233,7 @@ def value_counts(
                 psser._internal.data_fields[0].copy(name=name)
                 for psser, name in zip(groupkeys, groupkey_names)
             ],
-            column_labels=[self._agg_columns[0]._column_label],
+            column_labels=[("count",)],
             data_spark_columns=[scol_for(sdf, agg_column)],
         )
         return first_series(DataFrame(internal))

python/pyspark/pandas/indexes/datetimes.py

@@ -730,24 +730,32 @@ def indexer_between_time(
 
         Examples
         --------
-        >>> psidx = ps.date_range("2000-01-01", periods=3, freq="T")  # doctest: +SKIP
-        >>> psidx  # doctest: +SKIP
+        >>> psidx = ps.date_range("2000-01-01", periods=3, freq="T")
+        >>> psidx
         DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:01:00',
                        '2000-01-01 00:02:00'],
                       dtype='datetime64[ns]', freq=None)
 
-        >>> psidx.indexer_between_time("00:01", "00:02").sort_values()  # doctest: +SKIP
+        >>> psidx.indexer_between_time("00:01", "00:02").sort_values()
         Index([1, 2], dtype='int64')
 
-        >>> psidx.indexer_between_time("00:01", "00:02", include_end=False)  # doctest: +SKIP
+        >>> psidx.indexer_between_time("00:01", "00:02", include_end=False)
         Index([1], dtype='int64')
 
-        >>> psidx.indexer_between_time("00:01", "00:02", include_start=False)  # doctest: +SKIP
+        >>> psidx.indexer_between_time("00:01", "00:02", include_start=False)
         Index([2], dtype='int64')
         """
 
         def pandas_between_time(pdf) -> ps.DataFrame[int]:  # type: ignore[no-untyped-def]
-            return pdf.between_time(start_time, end_time, include_start, include_end)
+            if include_start and include_end:
+                inclusive = "both"
+            elif not include_start and not include_end:
+                inclusive = "neither"
+            elif include_start and not include_end:
+                inclusive = "left"
+            elif not include_start and include_end:
+                inclusive = "right"
+            return pdf.between_time(start_time, end_time, inclusive=inclusive)
 
         psdf = self.to_frame()[[]]
         id_column_name = verify_temp_column_name(psdf, "__id_column__")

python/pyspark/pandas/tests/computation/test_cov.py

@@ -28,10 +28,6 @@
 
 
 class FrameCovMixin:
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43809): Enable DataFrameSlowTests.test_cov for pandas 2.0.0.",
-    )
     def test_cov(self):
         # SPARK-36396: Implement DataFrame.cov
 
@@ -66,12 +62,8 @@ def test_cov(self):
         self.assert_eq(pdf.cov(min_periods=5), psdf.cov(min_periods=5))
 
         # extension dtype
-        if LooseVersion(pd.__version__) >= LooseVersion("1.2"):
-            numeric_dtypes = ["Int8", "Int16", "Int32", "Int64", "Float32", "Float64", "float"]
-            boolean_dtypes = ["boolean", "bool"]
-        else:
-            numeric_dtypes = ["Int8", "Int16", "Int32", "Int64", "float"]
-            boolean_dtypes = ["boolean", "bool"]
+        numeric_dtypes = ["Int8", "Int16", "Int32", "Int64", "Float32", "Float64", "float"]
+        boolean_dtypes = ["boolean", "bool"]
 
         sers = [pd.Series([1, 2, 3, None], dtype=dtype) for dtype in numeric_dtypes]
         sers += [pd.Series([True, False, True, None], dtype=dtype) for dtype in boolean_dtypes]
@@ -81,44 +73,19 @@ def test_cov(self):
         pdf.columns = [dtype for dtype in numeric_dtypes + boolean_dtypes] + ["decimal"]
         psdf = ps.from_pandas(pdf)
 
-        if LooseVersion(pd.__version__) >= LooseVersion("1.2"):
-            self.assert_eq(pdf.cov(), psdf.cov(), almost=True)
-            self.assert_eq(pdf.cov(min_periods=3), psdf.cov(min_periods=3), almost=True)
-            self.assert_eq(pdf.cov(min_periods=4), psdf.cov(min_periods=4))
-        else:
-            test_types = [
-                "Int8",
-                "Int16",
-                "Int32",
-                "Int64",
-                "float",
-                "boolean",
-                "bool",
-            ]
-            expected = pd.DataFrame(
-                data=[
-                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
-                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
-                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
-                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
-                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
-                    [0.0, 0.0, 0.0, 0.0, 0.0, 0.3333333, 0.3333333],
-                    [0.0, 0.0, 0.0, 0.0, 0.0, 0.3333333, 0.3333333],
-                ],
-                index=test_types,
-                columns=test_types,
-            )
-            self.assert_eq(expected, psdf.cov(), almost=True)
+        self.assert_eq(pdf.cov(numeric_only=True), psdf.cov(), almost=True)
 
         # string column
         pdf = pd.DataFrame(
             [(1, 2, "a", 1), (0, 3, "b", 1), (2, 0, "c", 9), (1, 1, "d", 1)],
             columns=["a", "b", "c", "d"],
         )
         psdf = ps.from_pandas(pdf)
-        self.assert_eq(pdf.cov(), psdf.cov(), almost=True)
-        self.assert_eq(pdf.cov(min_periods=4), psdf.cov(min_periods=4), almost=True)
-        self.assert_eq(pdf.cov(min_periods=5), psdf.cov(min_periods=5))
+        self.assert_eq(pdf.cov(numeric_only=True), psdf.cov(), almost=True)
+        self.assert_eq(
+            pdf.cov(numeric_only=True, min_periods=4), psdf.cov(min_periods=4), almost=True
+        )
+        self.assert_eq(pdf.cov(numeric_only=True, min_periods=5), psdf.cov(min_periods=5))
 
         # nan
         np.random.seed(42)
@@ -132,7 +99,7 @@ def test_cov(self):
         # return empty DataFrame
         pdf = pd.DataFrame([("1", "2"), ("0", "3"), ("2", "0"), ("1", "1")], columns=["a", "b"])
         psdf = ps.from_pandas(pdf)
-        self.assert_eq(pdf.cov(), psdf.cov())
+        self.assert_eq(pdf.cov(numeric_only=True), psdf.cov())
 
 
 class FrameCovTests(FrameCovMixin, ComparisonTestBase, SQLTestUtils):

python/pyspark/pandas/tests/data_type_ops/test_date_ops.py

@@ -63,25 +63,25 @@ def test_add(self):
         for psser in self.pssers:
             self.assertRaises(TypeError, lambda: self.psser + psser)
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43571): Enable DateOpsTests.test_sub for pandas 2.0.0.",
-    )
     def test_sub(self):
         self.assertRaises(TypeError, lambda: self.psser - "x")
         self.assertRaises(TypeError, lambda: self.psser - 1)
         self.assert_eq(
-            (self.pser - self.some_date).dt.days,
+            (self.pser - self.some_date).apply(lambda x: x.days),
             self.psser - self.some_date,
         )
         pdf, psdf = self.pdf, self.psdf
         for col in self.df_cols:
             if col == "date":
-                self.assert_eq((pdf["date"] - pdf[col]).dt.days, psdf["date"] - psdf[col])
+                self.assert_eq(
+                    (pdf["date"] - pdf[col]).apply(lambda x: x.days), psdf["date"] - psdf[col]
+                )
             else:
                 self.assertRaises(TypeError, lambda: psdf["date"] - psdf[col])
         pdf, psdf = self.date_pdf, self.date_psdf
-        self.assert_eq((pdf["this"] - pdf["that"]).dt.days, psdf["this"] - psdf["that"])
+        self.assert_eq(
+            (pdf["this"] - pdf["that"]).apply(lambda x: x.days), psdf["this"] - psdf["that"]
+        )
 
     def test_mul(self):
         self.assertRaises(TypeError, lambda: self.psser * "x")
@@ -128,15 +128,11 @@ def test_radd(self):
         self.assertRaises(TypeError, lambda: 1 + self.psser)
         self.assertRaises(TypeError, lambda: self.some_date + self.psser)
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43570): Enable DateOpsTests.test_rsub for pandas 2.0.0.",
-    )
     def test_rsub(self):
         self.assertRaises(TypeError, lambda: "x" - self.psser)
         self.assertRaises(TypeError, lambda: 1 - self.psser)
         self.assert_eq(
-            (self.some_date - self.pser).dt.days,
+            (self.some_date - self.pser).apply(lambda x: x.days),
             self.some_date - self.psser,
         )
 

python/pyspark/pandas/tests/groupby/test_aggregate.py

@@ -40,10 +40,6 @@ def pdf(self):
     def psdf(self):
         return ps.from_pandas(self.pdf)
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-44289): Enable GroupbyAggregateTests.test_aggregate for pandas 2.0.0.",
-    )
     def test_aggregate(self):
         pdf = pd.DataFrame(
             {"A": [1, 1, 2, 2], "B": [1, 2, 3, 4], "C": [0.362, 0.227, 1.267, -0.562]}
@@ -173,12 +169,8 @@ def sort(df):
         stats_psdf = psdf.groupby(10).agg({20: ["min", "max"], 30: "sum"})
         stats_pdf = pdf.groupby(10).agg({20: ["min", "max"], 30: "sum"})
         self.assert_eq(
-            stats_psdf.sort_values(by=[(20, "min"), (20, "max"), (30, "sum")]).reset_index(
-                drop=True
-            ),
-            stats_pdf.sort_values(by=[(20, "min"), (20, "max"), (30, "sum")]).reset_index(
-                drop=True
-            ),
+            stats_psdf.reset_index(drop=True),
+            stats_pdf.reset_index(drop=True),
         )
 
     def test_aggregate_func_str_list(self):

python/pyspark/pandas/tests/groupby/test_apply_func.py

@@ -42,10 +42,6 @@ def pdf(self):
     def psdf(self):
         return ps.from_pandas(self.pdf)
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43708): Enable GroupByTests.test_apply " "for pandas 2.0.0.",
-    )
     def test_apply(self):
         pdf = pd.DataFrame(
             {"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9, 16, 25, 36]},
@@ -87,14 +83,17 @@ def test_apply(self):
         self.assert_eq(
             psdf.groupby(psdf.b // 5).apply(lambda x: x + x.min()).sort_index(),
             pdf.groupby(pdf.b // 5).apply(lambda x: x + x.min()).sort_index(),
+            almost=True,
         )
         self.assert_eq(
             psdf.groupby(psdf.b // 5)["a"].apply(lambda x: x + x.min()).sort_index(),
             pdf.groupby(pdf.b // 5)["a"].apply(lambda x: x + x.min()).sort_index(),
+            almost=True,
         )
         self.assert_eq(
             psdf.groupby(psdf.b // 5)[["a"]].apply(lambda x: x + x.min()).sort_index(),
             pdf.groupby(pdf.b // 5)[["a"]].apply(lambda x: x + x.min()).sort_index(),
+            almost=True,
         )
         self.assert_eq(
             psdf.groupby(psdf.b // 5)[["a"]].apply(len).sort_index(),
@@ -139,10 +138,6 @@ def test_apply(self):
             pdf.groupby([("x", "a"), ("x", "b")]).apply(len).sort_index(),
         )
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43706): Enable GroupByTests.test_apply_without_shortcut " "for pandas 2.0.0.",
-    )
     def test_apply_without_shortcut(self):
         with option_context("compute.shortcut_limit", 0):
             self.test_apply()

python/pyspark/pandas/tests/groupby/test_groupby.py

@@ -769,10 +769,6 @@ def test_unique(self):
                 for act, exp in zip(actual, expect):
                     self.assertTrue(sorted(act) == sorted(exp))
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43444): Enable GroupBySlowTests.test_value_counts for pandas 2.0.0.",
-    )
     def test_value_counts(self):
         pdf = pd.DataFrame(
             {"A": [np.nan, 2, 2, 3, 3, 3], "B": [1, 1, 2, 3, 3, np.nan]}, columns=["A", "B"]
@@ -785,6 +781,7 @@ def test_value_counts(self):
         self.assert_eq(
             psdf.groupby("A")["B"].value_counts(dropna=False).sort_index(),
             pdf.groupby("A")["B"].value_counts(dropna=False).sort_index(),
+            almost=True,
         )
         self.assert_eq(
             psdf.groupby("A", dropna=False)["B"].value_counts(dropna=False).sort_index(),
@@ -804,6 +801,7 @@ def test_value_counts(self):
             pdf.groupby("A")["B"]
             .value_counts(sort=True, ascending=False, dropna=False)
             .sort_index(),
+            almost=True,
         )
         self.assert_eq(
             psdf.groupby("A")["B"]
@@ -812,6 +810,7 @@ def test_value_counts(self):
             pdf.groupby("A")["B"]
             .value_counts(sort=True, ascending=True, dropna=False)
             .sort_index(),
+            almost=True,
         )
         self.assert_eq(
             psdf.B.rename().groupby(psdf.A).value_counts().sort_index(),

python/pyspark/pandas/tests/indexes/test_base.py

@@ -1577,10 +1577,6 @@ def test_asof(self):
         psmidx = ps.MultiIndex.from_tuples([("a", "a"), ("a", "b"), ("a", "c")])
         self.assertRaises(NotImplementedError, lambda: psmidx.asof(("a", "b")))
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43608): Enable IndexesTests.test_union for pandas 2.0.0.",
-    )
     def test_union(self):
         # Index
         pidx1 = pd.Index([1, 2, 3, 4])
@@ -1593,13 +1589,6 @@ def test_union(self):
         self.assert_eq(psidx1.union(psidx2), pidx1.union(pidx2))
         self.assert_eq(psidx2.union(psidx1), pidx2.union(pidx1))
         self.assert_eq(psidx1.union(psidx3), pidx1.union(pidx3))
-        # Deprecated case, but adding to track if pandas stop supporting union
-        # as a set operation. It should work fine until stop supporting anyway.
-        # No longer supported from pandas 2.0.0.
-        if LooseVersion(pd.__version__) >= LooseVersion("2.0.0"):
-            self.assert_eq(psidx1 | psidx2, ps.Index([3, 4], dtype="int64"))
-        else:
-            self.assert_eq(pidx1 | pidx2, psidx1 | psidx2)
 
         self.assert_eq(psidx1.union([3, 4, 5, 6]), pidx1.union([3, 4, 5, 6]), almost=True)
         self.assert_eq(psidx2.union([1, 2, 3, 4]), pidx2.union([1, 2, 3, 4]), almost=True)
@@ -1904,10 +1893,6 @@ def test_hasnans(self):
         psmidx = ps.Index([("a", 1), ("b", 2)])
         self.assertRaises(NotImplementedError, lambda: psmidx.hasnans())
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43607): Enable IndexesTests.test_intersection for pandas 2.0.0.",
-    )
     def test_intersection(self):
         pidx = pd.Index([1, 2, 3, 4], name="Koalas")
         psidx = ps.from_pandas(pidx)
@@ -1919,15 +1904,6 @@ def test_intersection(self):
         self.assert_eq(
             (pidx + 1).intersection(pidx_other), (psidx + 1).intersection(psidx_other).sort_values()
         )
-        # Deprecated case, but adding to track if pandas stop supporting intersection
-        # as a set operation. It should work fine until stop supporting anyway.
-        # No longer supported from pandas 2.0.0.
-        if LooseVersion(pd.__version__) >= LooseVersion("2.0.0"):
-            self.assert_eq(
-                (psidx & psidx_other).sort_values(), ps.Index([3, 1, 7, 1], dtype="int64")
-            )
-        else:
-            self.assert_eq(pidx & pidx_other, (psidx & psidx_other).sort_values())
 
         pidx_other_different_name = pd.Index([3, 4, 5, 6], name="Databricks")
         psidx_other_different_name = ps.from_pandas(pidx_other_different_name)

python/pyspark/pandas/tests/indexes/test_datetime.py

@@ -161,11 +161,6 @@ def test_strftime(self):
                 psidx.strftime(date_format="%B %d, %Y"), pidx.strftime(date_format="%B %d, %Y")
             )
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43644): Enable DatetimeIndexTests.test_indexer_between_time "
-        "for pandas 2.0.0.",
-    )
     def test_indexer_between_time(self):
         for psidx, pidx in self.idx_pairs:
             self.assert_eq(

python/pyspark/pandas/tests/indexes/test_reindex.py

@@ -39,10 +39,6 @@ def df_pair(self):
         psdf = ps.from_pandas(pdf)
         return pdf, psdf
 
-    @unittest.skipIf(
-        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
-        "TODO(SPARK-43811): Enable DataFrameTests.test_reindex for pandas 2.0.0.",
-    )
     def test_reindex(self):
         index = pd.Index(["A", "B", "C", "D", "E"])
         columns = pd.Index(["numbers"])
@@ -64,9 +60,12 @@ def test_reindex(self):
             psdf.reindex(["A", "B", "C"], columns=["numbers", "2", "3"]).sort_index(),
         )
 
+        # We manually test this due to the bug in pandas.
+        expected_result = ps.DataFrame([1.0, 2.0, 3.0], index=ps.Index(["A", "B", "C"]))
+        expected_result.columns = pd.Index(["numbers"], name="cols")
         self.assert_eq(
-            pdf.reindex(["A", "B", "C"], index=["numbers", "2", "3"]).sort_index(),
             psdf.reindex(["A", "B", "C"], index=["numbers", "2", "3"]).sort_index(),
+            expected_result,
         )
 
         self.assert_eq(