Address comments

Author: icexelloss

python/pyspark/sql/functions.py

@@ -3007,9 +3007,8 @@ def pandas_udf(f=None, returnType=None, functionType=None):
        +---+----+------+
 
        .. note:: For performance reasons, the input series to window functions are not copied.
-            Therefore, changing the value of the input series is not allowed and will
-            result incorrect results. For the same reason, users should also not rely
-            on the index of the input series.
+            Therefore, mutating the input series is not allowed and will cause incorrect results.
+            For the same reason, users should also not rely on the index of the input series.
 
        .. seealso:: :meth:`pyspark.sql.GroupedData.agg` and :class:`pyspark.sql.Window`
 

python/pyspark/worker.py

@@ -147,7 +147,7 @@ def wrapped(*series):
 
 def wrap_window_agg_pandas_udf(f, return_type, runner_conf, udf_index):
     window_bound_types_str = runner_conf.get('pandas_window_bound_types')
-    window_bound_type = [t.strip() for t in window_bound_types_str.split(',')][udf_index]
+    window_bound_type = [t.strip().lower() for t in window_bound_types_str.split(',')][udf_index]
     if window_bound_type == 'bounded':
         return wrap_bounded_window_agg_pandas_udf(f, return_type)
     elif window_bound_type == 'unbounded':

sql/core/src/main/scala/org/apache/spark/sql/execution/python/WindowInPandasExec.scala

@@ -37,7 +37,7 @@ import org.apache.spark.util.Utils
 /**
  * This class calculates and outputs windowed aggregates over the rows in a single partition.
  *
- * This is similar to [[WindowExec]]. The main difference is that this node doesn't not compute
+ * This is similar to [[WindowExec]]. The main difference is that this node does not compute
  * any window aggregation values. Instead, it computes the lower and upper bound for each window
  * (i.e. window bounds) and pass the data and indices to Python worker to do the actual window
  * aggregation.