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Master-Level Python Assignment Question 1

Question:
Write a Python program to find the longest substring without repeating characters in a given string. The program should return both the substring and its length.

Solution:

def longest_unique_substring(s):
    char_set = set()
    left = 0
    max_length = 0
    longest_sub = ""

    for right in range(len(s)):
        while s[right] in char_set:
            char_set.remove(s[left])
            left += 1

        char_set.add(s[right])

        if right - left + 1 > max_length:
            max_length = right - left + 1
            longest_sub = s[left:right+1]

    return longest_sub, max_length


string = "programmingpython"
substring, length = longest_unique_substring(string)
print("Longest substring:", substring)
print("Length:", length)

Explanation:
This algorithm uses the sliding window technique and a set to track unique characters. By dynamically adjusting the window boundaries, the program efficiently finds the longest substring without repeating characters in linear time complexity O(n). Students often struggle with optimization techniques like this, which is why many rely on expert python assignment help usa services for proper guidance.

Master-Level Python Assignment Question 2

Question:
Create a Python class that implements a Least Recently Used (LRU) Cache with a fixed capacity using OrderedDict. The cache should support get() and put() operations in constant time.

Solution:

from collections import OrderedDict

class LRUCache:

    def __init__(self, capacity):
        self.cache = OrderedDict()
        self.capacity = capacity

    def get(self, key):
        if key not in self.cache:
            return -1
        else:
            self.cache.move_to_end(key)
            return self.cache[key]

    def put(self, key, value):
        if key in self.cache:
            self.cache.move_to_end(key)

        self.cache[key] = value

        if len(self.cache) > self.capacity:
            self.cache.popitem(last=False)


cache = LRUCache(2)
cache.put(1, 10)
cache.put(2, 20)
print(cache.get(1))
cache.put(3, 30)
print(cache.get(2))

Explanation:
The OrderedDict data structure maintains insertion order, making it suitable for implementing an LRU cache. When a key is accessed, it moves to the end of the dictionary to mark it as recently used. If the capacity is exceeded, the least recently used element is removed. Many advanced Python assignments involve designing efficient data structures like this, and expert python assignment help usa services can help students master such implementations.

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