TypeError: Column is not iterable - How to iterate over ArrayType()?

Yes you can do it by converting it to RDD and then back to DF.

>>> df.show(truncate=False)
+------+-----------------------+
|type  |names                  |
+------+-----------------------+
|person|[john, sam, jane]      |
|pet   |[whiskers, rover, fido]|
+------+-----------------------+

>>> df.rdd.mapValues(lambda x: [y.upper() for y in x]).toDF(["type","names"]).show(truncate=False)
+------+-----------------------+
|type  |names                  |
+------+-----------------------+
|person|[JOHN, SAM, JANE]      |
|pet   |[WHISKERS, ROVER, FIDO]|
+------+-----------------------+

In Spark < 2.4 you can use an user defined function:

from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, DataType, StringType

def transform(f, t=StringType()):
    if not isinstance(t, DataType):
       raise TypeError("Invalid type {}".format(type(t)))
    @udf(ArrayType(t))
    def _(xs):
        if xs is not None:
            return [f(x) for x in xs]
    return _

foo_udf = transform(str.upper)

df.withColumn('names', foo_udf(f.col('names'))).show(truncate=False)

+------+-----------------------+
|type  |names                  |
+------+-----------------------+
|person|[JOHN, SAM, JANE]      |
|pet   |[WHISKERS, ROVER, FIDO]|
+------+-----------------------+

Considering high cost of explode + collect_list idiom, this approach is almost exclusively preferred, despite its intrinsic cost.

In Spark 2.4 or later you can use transform* with upper (see SPARK-23909):

from pyspark.sql.functions import expr

df.withColumn(
    'names', expr('transform(names, x -> upper(x))')
).show(truncate=False)

+------+-----------------------+
|type  |names                  |
+------+-----------------------+
|person|[JOHN, SAM, JANE]      |
|pet   |[WHISKERS, ROVER, FIDO]|
+------+-----------------------+

It is also possible to use pandas_udf

from pyspark.sql.functions import pandas_udf, PandasUDFType

def transform_pandas(f, t=StringType()):
    if not isinstance(t, DataType):
       raise TypeError("Invalid type {}".format(type(t)))
    @pandas_udf(ArrayType(t), PandasUDFType.SCALAR)
    def _(xs):
        return xs.apply(lambda xs: [f(x) for x in xs] if xs is not None else xs)
    return _

foo_udf_pandas = transform_pandas(str.upper)

df.withColumn('names', foo_udf(f.col('names'))).show(truncate=False)

+------+-----------------------+
|type  |names                  |
+------+-----------------------+
|person|[JOHN, SAM, JANE]      |
|pet   |[WHISKERS, ROVER, FIDO]|
+------+-----------------------+

although only the latest Arrow / PySpark combinations support handling ArrayType columns (SPARK-24259, SPARK-21187). Nonetheless this option should be more efficient than standard UDF (especially with a lower serde overhead) while supporting arbitrary Python functions.

---

* A number of other higher order functions are also supported, including, but not limited to filter and aggregate. See for example

• Querying Spark SQL DataFrame with complex types
• How to slice and sum elements of array column?
• Filter array column content
• Spark Scala row-wise average by handling null.
• How to use transform higher-order function?.