How to zip two columns, explode them and finally pivot in Pyspark

I have two array columns (names, score). I need to explode both of them. Make names as column name for score(similar to pivot).

+------------+-------------------------+--------------------+                        
|      id    |     names               |      score         |
+------------+-------------------------+--------------------+
|ab01        |[F1 , F2, F3, F4, F5]    |[00123, 000.001, 00127, 00.0123, 111]
|ab02        |[F1 , F2, F3, F4, F5, F6]|[00124, 000.003, 00156, 00.067,  156, 254]
|ab03        |[F1 , F2, F3, F4, F5]    |[00234, 000.078, 00188, 00.0144, 188]
|ab04        |[F1 , F2, F3, F4, F5]    |[00345, 000.01112, 001567, 00.0186, 555]

Expected output:

 id       F1      F2        F3        F4    F5  F6
ab01    00123   000.001    00127    00.0123 111 null
ab02    00124   000.003    00156    00.067  156 254
ab03    00234   000.078    00188    00.0144 188 null
ab04    00345   000.01112  001567   00.0186 555 null

I tried zipping up names and score and then exploding them

combine = F.udf(lambda x, y: list(zip(x, y)),
                ArrayType(
                          StructType(
                                     [StructField("names", StringType()),
                                      StructField("score", StringType())
                                     ]
                                    )
                         )
               )

df2 = df.withColumn("new", combine("score", "names"))
         .withColumn("new", F.explode("new"))
         .select("id", 
                 F.col("new.names").alias("names"), 
                 F.col("new.score").alias("score")
                )

I'm getting an error:

TypeError: zip argument #1 must support iteration

I also tried exploding using rdd flatMap() and I still get the same error.

Is there an alternate way to achieve this?

Thanks in advance.