Convert spark DataFrame column to python list

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].mvvOut: 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_arrayOut: [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!

Following one liner gives the list you want.

mvv = mvv_count_df.select("mvv").rdd.flatMap(lambda x: x).collect()

This will give you all the elements as a list.

mvv_list = list(    mvv_count_df.select('mvv').toPandas()['mvv'])