This solution is also an approach that uses less code, avoids serialization to RDD and is likely easier to understand:

from pyspark.sql.types import IntegerType

# notice the variable name (more below)
mylist = [1, 2, 3, 4]

# notice the parens after the type name
spark.createDataFrame(mylist, IntegerType()).show()

NOTE: About naming your variable list: the term list is a Python builtin function and as such, it is strongly recommended that we avoid using builtin names as the name/label for our variables because we end up overwriting things like the list() function. When prototyping something fast and dirty, a number of folks use something like: mylist.

You can convert the list to a list of Row objects, then use spark.createDataFrame which will infer the schema from your data:

from pyspark.sql import Row
R = Row('ID', 'words')

# use enumerate to add the ID column
spark.createDataFrame([R(i, x) for i, x in enumerate(my_data)]).show() 
+---+--------------------+
| ID|               words|
+---+--------------------+
|  0|[apple, ball, bal...|
|  1| [cat, camel, james]|
|  2| [none, focus, cake]|
+---+--------------------+

here is how -

from pyspark.sql.types import *

cSchema = StructType([StructField("WordList", ArrayType(StringType()))])

# notice extra square brackets around each element of list 
test_list = [['Hello', 'world']], [['I', 'am', 'fine']]

df = spark.createDataFrame(test_list,schema=cSchema) 

If you have pandas package installed then can just import the dataframe to pyspark using spark.createDataFrame

import pandas as pd
from pyspark.sql import SparkSession


lst = [[1, 'A', 'aa'], [2, 'B', 'bb'], [3, 'C', 'cc']]
cols = ['col1', 'col2', 'col3']

df = pd.DataFrame(data=lst,columns=cols)

#Create PySpark SparkSession
spark = SparkSession.builder \
    .master("local[1]") \
    .appName("spark") \
    .getOrCreate()

#Create PySpark DataFrame from Pandas
sparkDF=spark.createDataFrame(df) 
sparkDF.printSchema()
sparkDF.show()

Alternatively, you can also do it without having pandas

from pyspark.sql import SparkSession

lst = [[1, 'A', 'aa'], [2, 'B', 'bb'], [3, 'C', 'cc']]
cols = ['col1', 'col2', 'col3']

#Create PySpark SparkSession
spark = SparkSession.builder \
    .master("local[1]") \
    .appName("spark") \
    .getOrCreate()

df = spark.createDataFrame(lst).toDF(*cols)
df.printSchema()
df.show()

See, why this way that you are doing is not working. First, you are trying to get integer from a Row Type, the output of your collect is like this:

>>> mvv_list = mvv_count_df.select('mvv').collect()
>>> mvv_list[0]
Out: Row(mvv=1)

If you take something like this:

>>> firstvalue = mvv_list[0].mvv
Out: 1

You will get the mvv value. If you want all the information of the array you can take something like this:

>>> mvv_array = [int(row.mvv) for row in mvv_list.collect()]
>>> mvv_array
Out: [1,2,3,4]

But if you try the same for the other column, you get:

>>> mvv_count = [int(row.count) for row in mvv_list.collect()]
Out: TypeError: int() argument must be a string or a number, not 'builtin_function_or_method'

This happens because count is a built-in method. And the column has the same name as count. A workaround to do this is change the column name of count to _count:

>>> mvv_list = mvv_list.selectExpr("mvv as mvv", "count as _count")
>>> mvv_count = [int(row._count) for row in mvv_list.collect()]

But this workaround is not needed, as you can access the column using the dictionary syntax:

>>> mvv_array = [int(row['mvv']) for row in mvv_list.collect()]
>>> mvv_count = [int(row['count']) for row in mvv_list.collect()]

And it will finally work!