Github user felixcheung commented on a diff in the pull request:
    --- Diff: docs/ ---
    @@ -1800,6 +1800,7 @@ working with timestamps in `pandas_udf`s to get the 
best performance, see
     ## Upgrading From Spark SQL 2.3 to 2.4
       - Since Spark 2.4, Spark maximizes the usage of a vectorized ORC reader 
for ORC files by default. To do that, `spark.sql.orc.impl` and 
`spark.sql.orc.filterPushdown` change their default values to `native` and 
`true` respectively.
    +  - In PySpark, when Arrow optimization is enabled, previously `toPandas` 
just failed when Arrow optimization is unabled to be used whereas 
`createDataFrame` from Pandas DataFrame allowed the fallback to 
non-optimization. Now, both `toPandas` and `createDataFrame` from Pandas 
DataFrame allow the fallback by default, which can be switched by 
    --- End diff --
    `which can be switched by` -> `which can be switched on by` or `which can 
be switched on with`


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