fix ruff errors

Author: kosiew

python/datafusion/dataframe.py

@@ -22,6 +22,7 @@
 from __future__ import annotations
 
 import warnings
+from enum import Enum
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -33,27 +34,21 @@
     overload,
 )
 
+import pyarrow as pa
 from typing_extensions import deprecated
 
+from datafusion import functions as f
+from datafusion._internal import DataFrame as DataFrameInternal
+from datafusion.expr import Expr, SortExpr, sort_or_default
 from datafusion.plan import ExecutionPlan, LogicalPlan
 from datafusion.record_batch import RecordBatchStream
 
-import pyarrow as pa
-from datafusion import functions as f
-
 if TYPE_CHECKING:
     import pathlib
     from typing import Callable, Sequence
 
     import pandas as pd
     import polars as pl
-
-from enum import Enum
-
-from datafusion._internal import DataFrame as DataFrameInternal
-from datafusion.expr import Expr, SortExpr, sort_or_default
-
-
 # excerpt from deltalake
 # https://github.com/apache/datafusion-python/pull/981#discussion_r1905619163
 class Compression(Enum):
@@ -868,14 +863,14 @@ def fill_null(self, value: Any, subset: list[str] | None = None) -> "DataFrame":
 
         Examples:
             >>> df = df.fill_null(0)  # Fill all nulls with 0 where possible
-            >>> df = df.fill_null("missing", subset=["name", "category"])  # Fill string columns
+            >>> # Fill nulls in specific string columns
+            >>> df = df.fill_null("missing", subset=["name", "category"])
 
         Notes:
             - Only fills nulls in columns where the value can be cast to the column type
             - For columns where casting fails, the original column is kept unchanged
             - For columns not in subset, the original column is kept unchanged
         """
-
         # Get columns to process
         if subset is None:
             subset = self.schema().names
@@ -910,29 +905,28 @@ def fill_null(self, value: Any, subset: list[str] | None = None) -> "DataFrame":
 
         return self.select(*exprs)
 
-    def fill_nan(
-        self, value: float | int, subset: list[str] | None = None
-    ) -> "DataFrame":
+    def fill_nan(self, value: float | int, subset: list[str] | None = None) -> "DataFrame":
         """Fill NaN values in specified numeric columns with a value.
 
         Args:
-            value: Numeric value to replace NaN values with
-            subset: Optional list of column names to fill. If None, fills all numeric columns.
+            value: Numeric value to replace NaN values with.
+            subset: Optional list of column names to fill. If None, fills all numeric
+                columns.
 
         Returns:
-            DataFrame with NaN values replaced in numeric columns
+            DataFrame with NaN values replaced in numeric columns.
 
         Examples:
             >>> df = df.fill_nan(0)  # Fill all NaNs with 0 in numeric columns
-            >>> df = df.fill_nan(99.9, subset=["price", "score"])  # Fill specific columns
+            >>> # Fill NaNs in specific numeric columns
+            >>> df = df.fill_nan(99.9, subset=["price", "score"])
 
         Notes:
             - Only fills NaN values in numeric columns (float32, float64)
             - Non-numeric columns are kept unchanged
             - For columns not in subset, the original column is kept unchanged
             - Value must be numeric (int or float)
         """
-
         if not isinstance(value, (int, float)):
             raise ValueError("Value must be numeric (int or float)")