Merge pull request #121 from fpgmaas/chore/ruff-f841

chore: enable ruff `F841`

pyproject.toml

@@ -71,7 +71,6 @@ preview = true
 ignore = [
     "E501",  # Line too long
     "F405",  # X may be undefined, or defined from star imports
-    "F841",  # Local variable `e_info` is assigned to but never used
 ]
 
 [tool.ruff.lint.flake8-type-checking]

tests/test_column_comparer.py

@@ -8,7 +8,7 @@ def describe_assert_column_equality():
     def it_throws_error_with_data_mismatch():
         data = [("jose", "jose"), ("li", "li"), ("luisa", "laura")]
         df = spark.createDataFrame(data, ["name", "expected_name"])
-        with pytest.raises(ColumnsNotEqualError) as e_info:
+        with pytest.raises(ColumnsNotEqualError):
             assert_column_equality(df, "name", "expected_name")
 
     def it_doesnt_throw_without_mismatch():
@@ -31,17 +31,17 @@ def it_works_with_no_mismatches():
     def it_throws_when_difference_is_bigger_than_precision():
         data = [(1.5, 1.1), (1.0004, 1.0005), (0.4, 0.45)]
         df = spark.createDataFrame(data, ["num1", "num2"])
-        with pytest.raises(ColumnsNotEqualError) as e_info:
+        with pytest.raises(ColumnsNotEqualError):
             assert_approx_column_equality(df, "num1", "num2", 0.1)
 
     def it_throws_when_comparing_floats_with_none():
         data = [(1.1, 1.1), (2.2, 2.2), (3.3, None)]
         df = spark.createDataFrame(data, ["num1", "num2"])
-        with pytest.raises(ColumnsNotEqualError) as e_info:
+        with pytest.raises(ColumnsNotEqualError):
             assert_approx_column_equality(df, "num1", "num2", 0.1)
 
     def it_throws_when_comparing_none_with_floats():
         data = [(1.1, 1.1), (2.2, 2.2), (None, 3.3)]
         df = spark.createDataFrame(data, ["num1", "num2"])
-        with pytest.raises(ColumnsNotEqualError) as e_info:
+        with pytest.raises(ColumnsNotEqualError):
             assert_approx_column_equality(df, "num1", "num2", 0.1)

tests/test_dataframe_comparer.py

@@ -14,7 +14,7 @@ def it_throws_with_schema_mismatches():
         df1 = spark.createDataFrame(data1, ["num", "expected_name"])
         data2 = [("bob", "jose"), ("li", "li"), ("luisa", "laura")]
         df2 = spark.createDataFrame(data2, ["name", "expected_name"])
-        with pytest.raises(SchemasNotEqualError) as e_info:
+        with pytest.raises(SchemasNotEqualError):
             assert_df_equality(df1, df2)
 
     def it_can_work_with_different_row_orders():
@@ -51,7 +51,7 @@ def it_throws_with_schema_column_order_mismatch():
         df1 = spark.createDataFrame(data1, ["num", "name"])
         data2 = [("jose", 1), ("li", 1)]
         df2 = spark.createDataFrame(data2, ["name", "num"])
-        with pytest.raises(SchemasNotEqualError) as e_info:
+        with pytest.raises(SchemasNotEqualError):
             assert_df_equality(df1, df2)
 
     def it_does_not_throw_on_schema_column_order_mismatch_with_transforms():
@@ -66,23 +66,23 @@ def it_throws_with_schema_mismatch():
         df1 = spark.createDataFrame(data1, ["num", "different_name"])
         data2 = [("jose", 1), ("li", 2)]
         df2 = spark.createDataFrame(data2, ["name", "num"])
-        with pytest.raises(SchemasNotEqualError) as e_info:
+        with pytest.raises(SchemasNotEqualError):
             assert_df_equality(df1, df2, transforms=[lambda df: df.select(sorted(df.columns))])
 
     def it_throws_with_content_mismatches():
         data1 = [("jose", "jose"), ("li", "li"), ("luisa", "laura")]
         df1 = spark.createDataFrame(data1, ["name", "expected_name"])
         data2 = [("bob", "jose"), ("li", "li"), ("luisa", "laura")]
         df2 = spark.createDataFrame(data2, ["name", "expected_name"])
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_df_equality(df1, df2)
 
     def it_throws_with_length_mismatches():
         data1 = [("jose", "jose"), ("li", "li"), ("laura", "laura")]
         df1 = spark.createDataFrame(data1, ["name", "expected_name"])
         data2 = [("jose", "jose"), ("li", "li")]
         df2 = spark.createDataFrame(data2, ["name", "expected_name"])
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_df_equality(df1, df2)
 
     def it_can_consider_nan_values_equal():
@@ -97,7 +97,7 @@ def it_does_not_consider_nan_values_equal_by_default():
         df1 = spark.createDataFrame(data1, ["num", "name"])
         data2 = [(float("nan"), "jose"), (2.0, "li")]
         df2 = spark.createDataFrame(data2, ["num", "name"])
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_df_equality(df1, df2, allow_nan_equality=False)
 
     def it_can_ignore_metadata():
@@ -126,7 +126,7 @@ def it_catches_mismatched_metadata():
         ])
         df1 = spark.createDataFrame(rows_data, schema1)
         df2 = spark.createDataFrame(rows_data, schema2)
-        with pytest.raises(SchemasNotEqualError) as e_info:
+        with pytest.raises(SchemasNotEqualError):
             assert_df_equality(df1, df2)
 
 
@@ -159,15 +159,15 @@ def it_throws_with_content_mismatch():
         df1 = spark.createDataFrame(data1, ["num", "expected_name"])
         data2 = [(1.0, "jose"), (1.05, "li"), (1.0, "laura"), (None, "hi")]
         df2 = spark.createDataFrame(data2, ["num", "expected_name"])
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_approx_df_equality(df1, df2, 0.1)
 
     def it_throws_with_with_length_mismatch():
         data1 = [(1.0, "jose"), (1.1, "li"), (1.2, "laura"), (None, None)]
         df1 = spark.createDataFrame(data1, ["num", "expected_name"])
         data2 = [(1.0, "jose"), (1.05, "li")]
         df2 = spark.createDataFrame(data2, ["num", "expected_name"])
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_approx_df_equality(df1, df2, 0.1)
 
     def it_does_not_throw_with_no_mismatch():

tests/test_readme_examples.py

@@ -48,7 +48,7 @@ def test_remove_non_word_characters_nice_error():
             "clean_name", remove_non_word_characters(F.col("name"))
         )
         # assert_column_equality(df, "clean_name", "expected_name")
-        with pytest.raises(ColumnsNotEqualError) as e_info:
+        with pytest.raises(ColumnsNotEqualError):
             assert_column_equality(df, "clean_name", "expected_name")
 
 
@@ -78,7 +78,7 @@ def test_remove_non_word_characters_long_error():
         ]
         expected_df = spark.createDataFrame(expected_data, ["name", "clean_name"])
         # assert_df_equality(actual_df, expected_df)
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_df_equality(actual_df, expected_df)
 
     def ignore_row_order():
@@ -141,7 +141,7 @@ def it_prints_underline_message():
             ("rick", 66),
         ]
         df2 = spark.createDataFrame(data, ["firstname", "age"])
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_df_equality(df1, df2, underline_cells=True)
 
     def it_shows_assert_basic_rows_equality(my_formats):
@@ -161,7 +161,7 @@ def it_shows_assert_basic_rows_equality(my_formats):
         ]
         df2 = spark.createDataFrame(data, ["firstname", "age"])
         # assert_basic_rows_equality(df1.collect(), df2.collect(), formats=my_formats)
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_basic_rows_equality(df1.collect(), df2.collect(), underline_cells=True)
 
 
@@ -174,7 +174,7 @@ def test_approx_col_equality_same():
     def test_approx_col_equality_different():
         data = [(1.1, 1.1), (2.2, 2.15), (3.3, 5.0), (None, None)]
         df = spark.createDataFrame(data, ["num1", "num2"])
-        with pytest.raises(ColumnsNotEqualError) as e_info:
+        with pytest.raises(ColumnsNotEqualError):
             assert_approx_column_equality(df, "num1", "num2", 0.1)
 
     def test_approx_df_equality_same():
@@ -190,7 +190,7 @@ def test_approx_df_equality_different():
         data2 = [(1.1, "a"), (5.0, "b"), (3.3, "z"), (None, None)]
         df2 = spark.createDataFrame(data2, ["num", "letter"])
         # assert_approx_df_equality(df1, df2, 0.1)
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_approx_df_equality(df1, df2, 0.1)
 
 
@@ -200,7 +200,7 @@ def test_schema_mismatch_message():
         df1 = spark.createDataFrame(data1, ["num", "letter"])
         data2 = [(1, 6), (2, 7), (3, 8), (None, None)]
         df2 = spark.createDataFrame(data2, ["num", "num2"])
-        with pytest.raises(SchemasNotEqualError) as e_info:
+        with pytest.raises(SchemasNotEqualError):
             assert_df_equality(df1, df2)
 
 
@@ -216,5 +216,5 @@ def test_remove_non_word_characters_long_error(my_chispa):
     ]
     expected_df = spark.createDataFrame(expected_data, ["name", "clean_name"])
     # my_chispa.assert_df_equality(actual_df, expected_df)
-    with pytest.raises(DataFramesNotEqualError) as e_info:
+    with pytest.raises(DataFramesNotEqualError):
         assert_df_equality(actual_df, expected_df)

tests/test_rows_comparer.py

@@ -11,15 +11,15 @@ def it_throws_with_row_mismatches():
         df1 = spark.createDataFrame(data1, ["num", "expected_name"])
         data2 = [("bob", "jose"), ("li", "li"), ("luisa", "laura")]
         df2 = spark.createDataFrame(data2, ["name", "expected_name"])
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_basic_rows_equality(df1.collect(), df2.collect())
 
     def it_throws_when_rows_have_different_lengths():
         data1 = [(1, "jose"), (2, "li"), (3, "laura"), (4, "bill")]
         df1 = spark.createDataFrame(data1, ["num", "expected_name"])
         data2 = [(1, "jose"), (2, "li"), (3, "laura")]
         df2 = spark.createDataFrame(data2, ["name", "expected_name"])
-        with pytest.raises(DataFramesNotEqualError) as e_info:
+        with pytest.raises(DataFramesNotEqualError):
             assert_basic_rows_equality(df1.collect(), df2.collect())
 
     def it_works_when_rows_are_the_same():

tests/test_schema_comparer.py

@@ -31,7 +31,7 @@ def it_throws_when_column_names_differ():
             StructField("name", StringType(), True),
             StructField("age", IntegerType(), True),
         ])
-        with pytest.raises(SchemasNotEqualError) as e_info:
+        with pytest.raises(SchemasNotEqualError):
             assert_schema_equality(s1, s2)
 
     def it_throws_when_schema_lengths_differ():
@@ -44,7 +44,7 @@ def it_throws_when_schema_lengths_differ():
             StructField("age", IntegerType(), True),
             StructField("fav_number", IntegerType(), True),
         ])
-        with pytest.raises(SchemasNotEqualError) as e_info:
+        with pytest.raises(SchemasNotEqualError):
             assert_schema_equality(s1, s2)
 
 
@@ -60,7 +60,7 @@ def it_has_good_error_messages_for_different_sized_schemas():
             StructField("something", IntegerType(), True),
             StructField("else", IntegerType(), True),
         ])
-        with pytest.raises(SchemasNotEqualError) as e_info:
+        with pytest.raises(SchemasNotEqualError):
             assert_schema_equality_ignore_nullable(s1, s2)
 
     def it_does_nothing_when_equal():