Optimizing Performance with HashSets in Python | CodeSignal Learn

Optimizing Performance with HashSets in Python | CodeSignal Learn

Introduction

Greetings, programming enthusiast! In this unit, we're embarking on a thrilling numerical quest, where unidentified bridges connect the islands of data. On these bridges, we'll see hashes and bins, all converging into sets! Throughout our journey, we'll utilize the fundamental concepts of Python's built-in data type, the set, to formulate an optimal solution. So, fasten your seatbelt and get ready to solve problems!

Task Statement

The task for this unit is to devise a Python function that accepts two lists containing unique integers and returns another list containing the elements common to both input lists. This task provides an intriguing perspective on deriving similarities between two data sequences, a scenario commonly encountered in data comparisons and analytics. For illustration, suppose we're given two lists: Python list1 = [1, 2, 3, 5, 8, 13, 21, 34] list2 = [2, 3, 5, 7, 13, 21, 31] The common_elements(list1, list2) function should comb through these arrays of integers and extract the common elements between them. The expected outcome in this case should be: Python [2, 3, 5, 13, 21]

Brute Force Solution and Complexity Analysis

O(n*m)

Sign up

Join the 1M+ learners on CodeSignal

Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal

Solution Building: Step 1

The initial step in crafting our solution is to transform these lists into Python's built-in datatype: sets. The computation of operations, like intersection, union, and difference, is highly optimized in sets. We'll leverage this optimization to our advantage. Python def common_elements(list1, list2): set1 = set(list1) set2 = set(list2)

Solution Building: Step 2

Having converted our data structure from a list to a set, we're now ready to identify the common elements between the two datasets. The Python set operation & allows us to perform the intersection operation seamlessly and swiftly. Python def common_elements(list1, list2): set1 = set(list1) set2 = set(list2) common = set1 & set2

Solution Building: Step 3

In this final step, we convert our set of common elements back into a list and sort the integers in ascending order. The sorted function in Python sorts a sequence and returns a new sorted list, leaving the original sequence untouched. Python def common_elements(list1, list2): set1 = set(list1) set2 = set(list2) common = set1 & set2 return sorted(list(common)) And there you have it, the solution is duly wrapped and ready!

Additional Set Methods

The set data type in Python is not only efficient but also provides a plethora of useful methods for performing various operations. Let's explore some of these operations (the average time complexity is also provided): Union (|): The union is a method or operation that combines all unique elements from two sets. Essentially, the resultant set will contain all elements present in both the sets. The | operator or the union() function can be used to perform this operation. Time complexity - O(len(set1)+len(set2)). Pythonset1 = {1, 2, 3, 4} set2 = {3, 4, 5, 6} union_set = set1 | set2 print(union_set) # prints: {1, 2, 3, 4, 5, 6} Difference (-): The difference operation removes the elements present in the second set from the first set. The - operator or the difference() function performs this operation. Time complexity - O(len(set1)). Pythonset1 = {1, 2, 3, 4} set2 = {3, 4, 5, 6} diff_set = set1 - set2 print(diff_set) # prints: {1, 2} Symmetric Difference (^): It returns a set containing elements which are in either of the sets but not in both. In other words, it outputs elements that only reside in one of the sets. The ^ operator or the symmetric_difference() function executes this operation. Time complexity - O(len(set1) + len(set2)). Pythonset1 = {1, 2, 3, 4} set2 = {3, 4, 5, 6} sym_diff_set = set1 ^ set2 print(sym_diff_set) # prints: {1, 2, 5, 6} Subset (<=): This operation checks if the first set is a subset of the second set. It returns True if all elements of the first set are present in the second set, otherwise False. The <= operator or the issubset() function achieves this. Time complexity - O(len(set1)). Pythonset1 = {1, 2, 3} set2 = {1, 2, 3, 4, 5} print(set1 <= set2) # prints: True Knowing these operations will allow you to use Python sets to their full potential and help you devise efficient solutions for a variety of problems.

The Power of `in` with Sets

Python's set datatype hands us a powerful capability for checking membership using the in operator. Performances of membership operations with sets in Python are light years ahead when compared to lists. Let's understand this with a simple code snippet: Pythonmy_set = {1, 2, 3, 4, 5} print(3 in my_set) # prints: True print(6 in my_set) # prints: False In this code, 3 in my_set checks if 3 is present in my_set and returns True if it exists, False otherwise. Similarly, 6 in my_set checks for 6 in my_set and as it doesn't exist in the set, it returns False. The magic lies in the time complexity of this process. The in operator works in constant time, O(1), when used with sets. This is contrasted with its counterpart in lists that works in linear time, O(n).