LeetCode Python Solutions: 1768. Merge Strings Alternately #coding #python смотреть онлайн

ZeroStress LeetCode Python Solutions: 1768. Merge Strings Alternately#coding #python #leetcode

Welcome to today's tutorial on solving the 'Merge Strings Alternately' problem on LeetCode. This problem challenges us to merge two strings by alternating their characters, starting with the first character of the first string. If one string is longer than the other, we append the remaining characters to the end of the merged string.

In this tutorial, we will explore two different solutions to this problem, each offering its own insights and advantages. By understanding these solutions, you will gain a deeper understanding of string manipulation techniques, algorithmic thinking, and efficient problem-solving in Python.

Our first solution focuses on a step-by-step iterative approach. This solution is beginner-friendly, highlighting the core logic behind the problem without relying on advanced Python functions or modules.

Our second solution introduces a more concise approach utilizing the 'zip_longest' function from the 'itertools' module. This function allows us to iterate over multiple iterables in a synchronized manner, even when they have different lengths. By leveraging this function and generator expressions, we can merge the strings with elegance and efficiency.

We'll also explore the time and space complexities of both solutions, ensuring a comprehensive understanding of their efficiency.

The time complexity of the provided solution is O(max(len1, len2)), where len1 and len2 are the lengths of the input strings word1 and word2. This complexity arises from the while loop that iterates through the strings as long as there are characters available in both word1 and word2. Since we are traversing at most max(len1, len2) characters, the time complexity is linear with respect to the maximum length of the two input strings.

The space complexity of the solution is O(1), which means it uses constant extra space. The space consumption does not depend on the input size. The only additional space used is for the merged string, but its size is limited by the sum of the lengths of word1 and word2. Since we are not using any data structures or collections that scale with the input size, the space complexity is constant.

In summary:

Time complexity: O(max(len1, len2))
Space complexity: O(1)

Both solutions are valid and provide the correct output for the given problem. The choice between the two depends on factors such as code readability, familiarity with the utilized functions, and personal preference.

If you are a beginner or new to Python programming, I would recommend starting with the initial solution I provided. It uses basic string manipulation techniques and follows a straightforward iterative approach. This solution is easy to understand and can serve as a good learning opportunity for beginners to grasp the core logic of the problem.

On the other hand, if you are already familiar with the zip_longest function from the itertools module and comfortable with generator expressions, you may prefer the alternative solution. It offers a more concise code and utilizes a built-in function specifically designed for iterating over multiple iterables simultaneously.

In general, it is important to strike a balance between code simplicity and readability. While a shorter solution may appear more elegant, it should not compromise the clarity and understandability of the code, especially when working on collaborative projects or maintaining code in the long run.

Therefore, I would recommend starting with the initial solution if you are a beginner or looking for a more explicit approach. As you gain more experience and familiarity with Python, you can explore alternative solutions and utilize built-in functions or libraries to optimize and simplify your code.


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