precommit

Author: arthurlw

doc/source/user_guide/user_defined_functions.rst

@@ -151,25 +151,25 @@ while preserving the original shape of the object.
 
 .. ipython:: python 
 
-    # Sample DataFrame  
-    df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})  
+    # Sample DataFrame
+    df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
 
-    # User-Defined Function  
-    def double(x):  
-        return x * 2  
+    # User-Defined Function
+    def double(x):
+        return x * 2
 
-    # Apply transform  
-    df_transformed = df.transform(double)  
-    print(df_transformed)  
+    # Apply transform
+    df_transformed = df.transform(double)
+    print(df_transformed)
 
-    # This works with lambda functions too  
-    df_lambda = df.transform(lambda x: x * 2)  
-    print(df_lambda)  
+    # This works with lambda functions too
+    df_lambda = df.transform(lambda x: x * 2)
+    print(df_lambda)
 
-Attempting to use common aggregation functions such as `mean` or `sum` will result in
+Attempting to use common aggregation functions such as ``mean`` or ``sum`` will result in
 values being broadcasted to the original dimensions:
 
-.. ipython:: python 
+.. ipython:: python
 
     # Sample DataFrame
     df = pd.DataFrame({
@@ -197,7 +197,7 @@ match particular conditions.
     :meth:`DataFrame.filter` does not accept user-defined functions, but can accept
     list comprehensions that have user-defined functions applied to them.
 
-.. ipython:: python 
+.. ipython:: python
 
     # Sample DataFrame
     df = pd.DataFrame({
@@ -267,7 +267,7 @@ Performance Considerations
 
 While user-defined functions provide flexibility, their use is currently discouraged as they can introduce
 performance issues, especially when written in pure Python. To improve efficiency,
-consider using built-in `NumPy` or `pandas` functions instead of user-defined functions
+consider using built-in ``NumPy`` or ``pandas`` functions instead of user-defined functions
 for common operations.
 
 .. note::
@@ -302,4 +302,4 @@ with user-defined functions because they leverage highly optimized C functions
 via NumPy to process entire arrays at once. This approach avoids the overhead of looping
 through rows in Python and making separate function calls for each row, which is slow and
 inefficient. Additionally, NumPy arrays benefit from memory efficiency and CPU-level
-optimizations, making vectorized operations the preferred choice whenever possible.
+optimizations, making vectorized operations the preferred choice whenever possible.