Fix ruff fomatting, linting, namespace issues using pre-commit hooks

Author: akj2018

doc/source/whatsnew/v3.0.0.rst

@@ -697,7 +697,7 @@ I/O
 - Bug in :meth:`.DataFrame.to_json` when ``"index"`` was a value in the :attr:`DataFrame.column` and :attr:`Index.name` was ``None``. Now, this will fail with a ``ValueError`` (:issue:`58925`)
 - Bug in :meth:`DataFrame._repr_html_` which ignored the ``"display.float_format"`` option (:issue:`59876`)
 - Bug in :meth:`DataFrame.from_records` where ``columns`` parameter with numpy structured array was not reordering and filtering out the columns (:issue:`59717`)
-- Bug in :meth:`DataFrame.to_csv` where `quoting=csv.QUOTE_NONNUMERIC` adds extra decimal places when ``dtype=float32``, ``dtype=float16``  and ``float_format=None`` in the csv output (:issue:`60699`)
+- Bug in :meth:`DataFrame.to_csv` where ``quoting=csv.QUOTE_NONNUMERIC`` adds extra decimal places when ``dtype=float32``, ``dtype=float16``  and ``float_format=None`` in the csv output (:issue:`60699`)
 - Bug in :meth:`DataFrame.to_dict` raises unnecessary ``UserWarning`` when columns are not unique and ``orient='tight'``. (:issue:`58281`)
 - Bug in :meth:`DataFrame.to_excel` when writing empty :class:`DataFrame` with :class:`MultiIndex` on both axes (:issue:`57696`)
 - Bug in :meth:`DataFrame.to_excel` where the :class:`MultiIndex` index with a period level was not a date (:issue:`60099`)

pandas/core/indexes/base.py

@@ -7756,11 +7756,12 @@ def get_values_for_csv(
             mask = isna(values)
 
             # GH60699
-            # Ensure quoting don't add extra decimal places in output for float16, float32 
+            # Ensure quoting don't add extra decimal places in output
+            # for float16, float32
             if values.dtype in [np.float16, np.float32]:
-                values = np.array(values, dtype="str")  
-                values = values.astype(float, copy=False)  
-            
+                values = np.array(values, dtype="str")
+                values = values.astype(float, copy=False)
+
             values = values.astype(object, copy=False)
             values[mask] = na_rep
             return values

pandas/tests/frame/methods/test_to_csv.py

@@ -1316,37 +1316,68 @@ def test_to_csv_quoting(self):
         expected = tm.convert_rows_list_to_csv_str(expected_rows)
         assert df.to_csv(quoting=csv.QUOTE_ALL) == expected
 
-    @pytest.mark.parametrize("data, dtype, expected_rows", 
+    @pytest.mark.parametrize(
+        "data, dtype, expected_rows",
         [
             # Test Case 1: float16 precision
             (
                 {"col": [8.57, 0.156, -0.312, 123.3, -54.5, np.nan]},
                 "float16",
-                ['"","col"', '0,8.57', '1,0.156', '2,-0.312', '3,123.3', '4,-54.5', '5,""']
+                [
+                    '"","col"',
+                    "0,8.57",
+                    "1,0.156",
+                    "2,-0.312",
+                    "3,123.3",
+                    "4,-54.5",
+                    '5,""',
+                ],
             ),
-
             # Test Case 2: float32 precision
             (
-                {"col": [8.57, 1.234567, -2.345678, 1e6, -1.5e6, np.nan]}, 
+                {"col": [8.57, 1.234567, -2.345678, 1e6, -1.5e6, np.nan]},
                 "float32",
-                ['"","col"', '0,8.57', '1,1.234567', '2,-2.345678', '3,1000000.0', '4,-1500000.0', '5,""']
+                [
+                    '"","col"',
+                    "0,8.57",
+                    "1,1.234567",
+                    "2,-2.345678",
+                    "3,1000000.0",
+                    "4,-1500000.0",
+                    '5,""',
+                ],
             ),
-
             # Test Case 3: float64 precision
             (
-                {"col": [8.57, 3.141592653589793, -2.718281828459045, 1.01e12, -5.67e11, np.nan]},
+                {
+                    "col": [
+                        8.57,
+                        3.141592653589793,
+                        -2.718281828459045,
+                        1.01e12,
+                        -5.67e11,
+                        np.nan,
+                    ]
+                },
                 "float64",
-                ['"","col"', '0,8.57', '1,3.141592653589793', '2,-2.718281828459045', 
-                '3,1010000000000.0', '4,-567000000000.0', '5,""']
+                [
+                    '"","col"',
+                    "0,8.57",
+                    "1,3.141592653589793",
+                    "2,-2.718281828459045",
+                    "3,1010000000000.0",
+                    "4,-567000000000.0",
+                    '5,""',
+                ],
             ),
-        ]
+        ],
     )
     def test_to_csv_decimal_and_nonnumeric_quoting(self, data, dtype, expected_rows):
         # https://github.com/pandas-dev/pandas/issues/60699
-        # combination of float dtype, no special formatting and 
+        # combination of float dtype, no special formatting and
         # quoting is specified (quoting=csv.QUOTE_NONNUMERIC)
-        df = pd.DataFrame(data, dtype=dtype)
-        result = df.to_csv(quoting=csv.QUOTE_NONNUMERIC) 
+        df = DataFrame(data, dtype=dtype)
+        result = df.to_csv(quoting=csv.QUOTE_NONNUMERIC)
         expected = tm.convert_rows_list_to_csv_str(expected_rows)
         assert result == expected