precommit

Author: arthurlw

doc/source/user_guide/user_defined_functions.rst

@@ -165,7 +165,7 @@ When to use: Use :meth:`DataFrame.filter` when you want to use a UDF to create a
     df_filtered = df[[col for col in df.columns if is_long_name(col)]]
     print(df_filtered)
 
-Since filter does not direclty accept a UDF, you have to apply the UDF indirectly,
+Since filter does not directly accept a UDF, you have to apply the UDF indirectly,
 such as by using list comprehensions.
 
 :meth:`DataFrame.map`
@@ -229,4 +229,4 @@ with UDFs 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.