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Which of the following code blocks correctly returns a new DataFrame with a modified storeDescription column where the prefix "Description: " has been removed from each value in the storeDescription column of DataFrame storesDF?
A sample of DataFrame storesDF is shown below:
storeId storeDescription
0 Description: ultr...
1 Description: sagi...
2 Description: port...
3 Description: tris...
4 Description: ulla...
Which of the following code blocks correctly returns a new DataFrame with a modified storeDescription column where the prefix "Description: " has been removed from each value in the storeDescription column of DataFrame storesDF?
A sample of DataFrame storesDF is shown below:
storeId storeDescription
0 Description: ultr...
1 Description: sagi...
2 Description: port...
3 Description: tris...
4 Description: ulla...
Explanation:
The question asks to remove the 'Description: ' prefix from the storeDescription column using regex. Options A and B are incorrect because A misses the replacement argument, and B uses an invalid method call. Option C uses regexp_extract, which extracts the pattern instead of replacing it. Options D and E both correctly use regexp_replace with three arguments. In PySpark, the first argument can be a column name (string) or a Column object, making both D and E valid. Thus, the correct answers are D and E.