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].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!
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']
)