Further parsing

You've molded this dataset into a significantly different format than it was before, but there are still a few things left to do. You need to prep the column data for use in later analysis and remove a few intermediary columns.

The spark context is available and pyspark.sql.functions is aliased as F. The types from pyspark.sql.types are already imported. The split_df DataFrame is as you last left it. Remember, you can use .printSchema() on a DataFrame in the console area to view the column names and types.

Create a new function called retriever that takes two arguments, the split columns (cols) and the total number of columns (colcount). This function should return a list of the entries that have not been defined as columns yet (i.e., everything after item 4 in the list).
Define the function as a Spark UDF, returning an Array of strings.
Create the new column dog_list using the UDF and the available columns in the DataFrame.
Remove the columns _c0, colcount, and split_cols.

Exercise

Further parsing

Instructions