# Pispark | Linear regression with extended functional dataset using Apache MLlib

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Method # 1: Using loop + `remove()`
A combination of the above functions can be used to accomplish this task. In this we remove elements using remove () and check for similar elements using a loop.

 ` # Python3 code to demonstrate how it works ` ` # Subtract linelists ` ` # using loop + delete () `   ` # initialize lists ` ` test_list1 ` ` = ` ` [` ` "gfg" ` `, ` ` "is" ` `, ` ` "best" ` `, ` ` "for" ` `, ` ` "CS" ` `] ` ` test_list2 ` ` = [ "preffered" , "is" , "gfg" ] ````   # print original lists print ( "The original list 1:" + str (test_list1)) print ( "The original list 2:" + str (test_list2))   # Subtract linelists # using loop + delete () re s = [ele for ele in test_list1] for a in test_list2: if a in test_list1: res.remove ( a)   # print result print ( "The Subtracted list is:" + str (res)) ```

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< pre> The original list 1: [`gfg`,` is`, `best`,` for`, `CS`] The original list 2: [` preffered`, `is`,` gfg`] The Subtracted list is : [`best`,` for`, `CS`]

Method # 2: Using ` Counter () + elements () `
A combination of the above functions provides a shortcut to solve this problem. In this, we extract the number of elements in both lists and then perform splitting by extracting them using element ().

 ` # Python3 code to demonstrate how it works # Subtract linelists # using Counter () + elements () ```` from collections import Counter   # initialize lists ```` test_list1 = [ "gfg " , " is " , "best" , "for" ` `, ` ` "CS" ` `] ` ` test_list2 ` ` = ` ` [` ` "preffered" ` `, ` ` "is" ` `, ` ` "gfg" ` `] `   ` # printing original lists ` ` print ` ` (` `" The original list 1: "` ` + ` ` str ` ` (test_list1)) ` ` print ` ` ( "The original list 2:" + str (test_list2)) ````    # Subtract linelists # using Counter () + elements () res = list ((Counter (test_list1) - Counter (test_list2)). elements () )   # print result print ( "The Subtracted list is:" + str (res)) ```

Output:

` The original list 1: [`gfg`,` is`, `best`,` for`, `CS`] The original list 2: [` preffered`, `is`,` gfg`] The Subtracted list is: [`best`,` for`, `CS`] `
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