STYLE: Extending codespell to pandas/tests Part 2 (#40332)

* STYLE: Extending codespell to pandas/tests part 2

* DOC: small change in test_setitem.py

Author: 01-vyom

pandas/tests/frame/indexing/test_indexing.py

@@ -439,8 +439,8 @@ def test_setitem_corner(self, float_frame):
         dm["foo"] = "bar"
         assert dm["foo"].dtype == np.object_
 
-        dm["coercable"] = ["1", "2", "3"]
-        assert dm["coercable"].dtype == np.object_
+        dm["coercible"] = ["1", "2", "3"]
+        assert dm["coercible"].dtype == np.object_
 
     def test_setitem_corner2(self):
         data = {

pandas/tests/frame/indexing/test_setitem.py

@@ -420,7 +420,7 @@ def test_setitem_intervals(self):
         assert isinstance(ser.cat.categories, IntervalIndex)
 
         # B & D end up as Categoricals
-        # the remainer are converted to in-line objects
+        # the remainder are converted to in-line objects
         # containing an IntervalIndex.values
         df["B"] = ser
         df["C"] = np.array(ser)
@@ -433,13 +433,13 @@ def test_setitem_intervals(self):
         assert is_categorical_dtype(df["D"].dtype)
         assert is_interval_dtype(df["D"].cat.categories)
 
-        # Thes goes through the Series constructor and so get inferred back
+        # These go through the Series constructor and so get inferred back
         #  to IntervalDtype
         assert is_interval_dtype(df["C"])
         assert is_interval_dtype(df["E"])
 
         # But the Series constructor doesn't do inference on Series objects,
-        #  so setting df["F"] doesnt get cast back to IntervalDtype
+        #  so setting df["F"] doesn't get cast back to IntervalDtype
         assert is_object_dtype(df["F"])
 
         # they compare equal as Index

pandas/tests/frame/methods/test_cov_corr.py

@@ -207,7 +207,7 @@ def test_corr_item_cache(self, using_array_manager):
 
         _ = df.corr()
 
-        # Check that the corr didnt break link between ser and df
+        # Check that the corr didn't break link between ser and df
         ser.values[0] = 99
         assert df.loc[0, "A"] == 99
         assert df["A"] is ser

pandas/tests/frame/methods/test_quantile.py

@@ -550,11 +550,11 @@ class TestQuantileExtensionDtype:
             pd.date_range("2016-01-01", periods=9, tz="US/Pacific"),
             pytest.param(
                 pd.array(np.arange(9), dtype="Int64"),
-                marks=pytest.mark.xfail(reason="doesnt implement from_factorized"),
+                marks=pytest.mark.xfail(reason="doesn't implement from_factorized"),
             ),
             pytest.param(
                 pd.array(np.arange(9), dtype="Float64"),
-                marks=pytest.mark.xfail(reason="doesnt implement from_factorized"),
+                marks=pytest.mark.xfail(reason="doesn't implement from_factorized"),
             ),
         ],
         ids=lambda x: str(x.dtype),

pandas/tests/frame/methods/test_replace.py

@@ -1439,8 +1439,8 @@ def test_categorical_replace_with_dict(self, replace_dict, final_data):
 
         a = pd.Categorical(final_data[:, 0], categories=[3, 2])
 
-        excat = [3, 2] if replace_dict["b"] == 1 else [1, 3]
-        b = pd.Categorical(final_data[:, 1], categories=excat)
+        ex_cat = [3, 2] if replace_dict["b"] == 1 else [1, 3]
+        b = pd.Categorical(final_data[:, 1], categories=ex_cat)
 
         expected = DataFrame({"a": a, "b": b})
         result = df.replace(replace_dict, 3)

pandas/tests/frame/methods/test_reset_index.py

@@ -137,8 +137,8 @@ def test_reset_index(self, float_frame):
 
         # preserve column names
         float_frame.columns.name = "columns"
-        resetted = float_frame.reset_index()
-        assert resetted.columns.name == "columns"
+        reset = float_frame.reset_index()
+        assert reset.columns.name == "columns"
 
         # only remove certain columns
         df = float_frame.reset_index().set_index(["index", "A", "B"])
@@ -159,10 +159,10 @@ def test_reset_index(self, float_frame):
 
         # test resetting in place
         df = float_frame.copy()
-        resetted = float_frame.reset_index()
+        reset = float_frame.reset_index()
         return_value = df.reset_index(inplace=True)
         assert return_value is None
-        tm.assert_frame_equal(df, resetted, check_names=False)
+        tm.assert_frame_equal(df, reset, check_names=False)
 
         df = float_frame.reset_index().set_index(["index", "A", "B"])
         rs = df.reset_index("A", drop=True)
@@ -224,11 +224,11 @@ def test_reset_index_right_dtype(self):
         )
         df = DataFrame(s1)
 
-        resetted = s1.reset_index()
-        assert resetted["time"].dtype == np.float64
+        reset = s1.reset_index()
+        assert reset["time"].dtype == np.float64
 
-        resetted = df.reset_index()
-        assert resetted["time"].dtype == np.float64
+        reset = df.reset_index()
+        assert reset["time"].dtype == np.float64
 
     def test_reset_index_multiindex_col(self):
         vals = np.random.randn(3, 3).astype(object)

pandas/tests/frame/methods/test_to_dict_of_blocks.py

@@ -53,7 +53,7 @@ def test_to_dict_of_blocks_item_cache():
 
     df._to_dict_of_blocks()
 
-    # Check that the to_dict_of_blocks didnt break link between ser and df
+    # Check that the to_dict_of_blocks didn't break link between ser and df
     ser.values[0] = "foo"
     assert df.loc[0, "b"] == "foo"
 

pandas/tests/frame/methods/test_values.py

@@ -91,7 +91,7 @@ def test_frame_values_with_tz(self):
         )
         tm.assert_numpy_array_equal(result, expected)
 
-        # two columns, homogenous
+        # two columns, homogeneous
 
         df["B"] = df["A"]
         result = df.values

pandas/tests/frame/test_subclass.py

@@ -519,7 +519,7 @@ def test_subclassed_apply(self):
         def check_row_subclass(row):
             assert isinstance(row, tm.SubclassedSeries)
 
-        def strech(row):
+        def stretch(row):
             if row["variable"] == "height":
                 row["value"] += 0.5
             return row
@@ -547,7 +547,7 @@ def strech(row):
             columns=["first", "last", "variable", "value"],
         )
 
-        result = df.apply(lambda x: strech(x), axis=1)
+        result = df.apply(lambda x: stretch(x), axis=1)
         assert isinstance(result, tm.SubclassedDataFrame)
         tm.assert_frame_equal(result, expected)
 

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -758,7 +758,7 @@ def test_agg_relabel_multiindex_column(
 
 
 def test_agg_relabel_multiindex_raises_not_exist():
-    # GH 29422, add test for raises senario when aggregate column does not exist
+    # GH 29422, add test for raises scenario when aggregate column does not exist
     df = DataFrame(
         {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
     )
@@ -769,7 +769,7 @@ def test_agg_relabel_multiindex_raises_not_exist():
 
 
 def test_agg_relabel_multiindex_duplicates():
-    # GH29422, add test for raises senario when getting duplicates
+    # GH29422, add test for raises scenario when getting duplicates
     # GH28426, after this change, duplicates should also work if the relabelling is
     # different
     df = DataFrame(