Pyspark code is not performant enough when compared to pure python alternative
You have instantiated a sparkContext object with "local" mode configuration. It means you have allocated ressources for a single multi-core Java Virtual Machine on your computer. In this configuration, you can't reach better performs than with python. Because : 


With local mode, you have less ressources (cpu, memory...) with Spark than with python (you can't have a JVM with more ressources than your computer).
PySpark code is converted into Scala code before execution.


Spark benefits arise when you use Spark on multiple nodes. In this configuration, spark master is yarn, mesos or standalone. In that case, a spark job would be separated in multiple tasks and each node would be dedicated to different tasks. 

For example, if you have 3 nodes and 6 tasks, each node can handle 2 tasks. If you have 6 nodes and 6 tasks, each node will handle a task. With simple python, think that you have only 1 node for "6 tasks". So, for large tasks (big enough datasets), Spark will results in better latencies than simple python.