Reverts DataFrame Indexing Changes (Bears-R-Us#3323)

* Revert "missed an iloc call that was affecting benchmark performance"

This reverts commit d664ac9.

* Revert "missing iloc usage"

This reverts commit 31d3185.

* stragglers for reverting dataframe indexing

* updates to remove .value calls

* flake changes

PROTO_tests/tests/io_test.py

@@ -483,7 +483,7 @@ def test_read_nested(self):
             assert "idx" in data
             assert "seg" in data
             assert df["idx"].to_list() == data["idx"].to_list()
-            assert df["seg"].values.to_list() == data["seg"].to_list()
+            assert df["seg"].to_list() == data["seg"].to_list()
 
             # test read with read_nested=false and no supplied datasets
             data = ak.read_parquet(f"{file_name}*", read_nested=False)["idx"]
@@ -495,7 +495,7 @@ def test_read_nested(self):
             assert "idx" in data
             assert "seg" in data
             assert df["idx"].to_list() == data["idx"].to_list()
-            assert df["seg"].values.to_list() == data["seg"].to_list()
+            assert df["seg"].to_list() == data["seg"].to_list()
 
     @pytest.mark.parametrize("comp", COMPRESSIONS)
     def test_ipv4_columns(self, comp):
@@ -524,7 +524,7 @@ def test_ipv4_columns(self, comp):
 
         # test replacement of IPv4 with uint representation
         df = ak.DataFrame({"a": ak.IPv4(ak.arange(10))})
-        df["a"] = df["a"].values.export_uint()
+        df["a"] = df["a"].export_uint()
         assert ak.arange(10).to_list() == df["a"].to_list()
 
     def test_empty_segs_segarray(self):
@@ -562,7 +562,7 @@ def test_empty_segs_segarray(self):
                 pddf.to_parquet(file_path)
                 akdf = ak.DataFrame(ak.read_parquet(file_path))
 
-                to_pd = pd.Series(akdf["rand"].values.to_list())
+                to_pd = pd.Series(akdf["rand"].to_list())
                 # raises an error if the two series aren't equal
                 # we can't use np.allclose(pddf['rand'].to_list, akdf['rand'].to_list) since these
                 # are lists of lists. assert_series_equal handles this and properly handles nans.
@@ -702,7 +702,7 @@ def test_read_and_write_with_dict(self):
             for col_name in akdf.columns.values:
                 gen_arr = ak.read_hdf(f"{file_name}*", datasets=[col_name])[col_name]
                 if akdf[col_name].dtype != ak.float64:
-                    assert akdf[col_name].values.to_list() == gen_arr.to_list()
+                    assert akdf[col_name].to_list() == gen_arr.to_list()
                 else:
                     a = akdf[col_name].to_ndarray()
                     b = gen_arr.to_ndarray()
@@ -742,7 +742,7 @@ def test_read_and_write_with_dict(self):
                 # verify generic load works
                 gen_arr = ak.load(path_prefix=file_name, dataset=col_name)[col_name]
                 if akdf[col_name].dtype != ak.float64:
-                    assert akdf[col_name].values.to_list() == gen_arr.to_list()
+                    assert akdf[col_name].to_list() == gen_arr.to_list()
                 else:
                     a = akdf[col_name].to_ndarray()
                     b = gen_arr.to_ndarray()
@@ -754,7 +754,7 @@ def test_read_and_write_with_dict(self):
                 # verify generic load works with file_format parameter
                 gen_arr = ak.load(path_prefix=file_name, dataset=col_name, file_format="HDF5")[col_name]
                 if akdf[col_name].dtype != ak.float64:
-                    assert akdf[col_name].values.to_list() == gen_arr.to_list()
+                    assert akdf[col_name].to_list() == gen_arr.to_list()
                 else:
                     a = akdf[col_name].to_ndarray()
                     b = gen_arr.to_ndarray()
@@ -1229,7 +1229,7 @@ def test_hdf_overwrite_dataframe(self):
                 data = ak.read_hdf(f"{file_name}*")
                 odf_keys = list(odf.keys())
                 for key in df.keys():
-                    assert (data[key] == (odf[key].values if key in odf_keys else df[key].values)).all()
+                    assert (data[key] == (odf[key] if key in odf_keys else df[key])).all()
 
     def test_overwrite_segarray(self):
         sa1 = ak.SegArray(ak.arange(0, 1000, 5), ak.arange(1000))
@@ -1429,9 +1429,9 @@ def test_special_objtype(self):
             assert isinstance(rd_df["ip"], ak.IPv4)
             assert isinstance(rd_df["datetime"], ak.Datetime)
             assert isinstance(rd_df["timedelta"], ak.Timedelta)
-            assert df["ip"].values.to_list() == rd_df["ip"].to_list()
-            assert df["datetime"].values.to_list() == rd_df["datetime"].to_list()
-            assert df["timedelta"].values.to_list() == rd_df["timedelta"].to_list()
+            assert df["ip"].to_list() == rd_df["ip"].to_list()
+            assert df["datetime"].to_list() == rd_df["datetime"].to_list()
+            assert df["timedelta"].to_list() == rd_df["timedelta"].to_list()
 
 
 class TestCSV:

PROTO_tests/tests/series_test.py

@@ -341,12 +341,3 @@ def test_fillna(self):
 
         fill_values3 = 100.0
         assert data.fillna(fill_values3).to_list() == [1.0, 100.0, 3.0, 100.0, 5.0]
-
-    def test_series_segarray_to_pandas(self):
-        # reproducer for issue #3222
-        sa = ak.SegArray(ak.arange(0, 30, 3), ak.arange(30))
-        akdf = ak.DataFrame({"test": sa})
-        pddf = pd.DataFrame({"test": sa.to_list()})
-
-        assert_frame_equal(akdf.to_pandas(), pddf)
-        assert_series_equal(akdf['test'].to_pandas(), pddf['test'], check_names=False)