Find Longest Consecutive Sequence Algorithm Solved in Python One-Liner Style

Introduction

Finding the longest consecutive sequence in an unsorted list of integers is a classic problem that frequently shows up on coding interview platforms like Leetcode (Problem #128). The goal? Given an array of integers, return the length of the longest sequence of consecutive numbers. What makes this problem interesting is the requirement for a solution with O(n) time complexity.

Most traditional solutions span multiple lines and involve sorting or dictionary tracking. But what if we told you that it’s entirely possible to solve this in Python using just one line of code—without sacrificing performance or clarity? In this blog post, we’ll break down how to achieve that one-liner magic, explain why it works, and provide performance insights along the way.

1. Understanding the Problem

Let’s begin with a quick overview of what “longest consecutive sequence” actually means. Given a list like [100, 4, 200, 1, 3, 2], the longest consecutive subsequence is [1, 2, 3, 4]—a sequence of numbers that appear in order and are contiguous—even if they’re not adjacent in the array.

Your job: Return the length of that sequence. In the case above, the answer would be 4.

The key constraint here is linear time complexity (O(n)). Sorting the list—that is O(n log n)—won’t do. We’re going to need a set and some clever iteration logic.

2. The Set-Based Approach

Before we get to the one-liner, let’s examine the optimal set-based solution. Sets in Python offer average O(1) lookup time, which is perfect for the kind of existence checks we’ll need.

def longestConsecutive(nums):
    num_set = set(nums)
    longest = 0
    for n in num_set:
        if n - 1 not in num_set:  # start of a sequence
            length = 1
            while n + length in num_set:
                length += 1
            longest = max(longest, length)
    return longest

This function ensures that we only start counting when we’ve found the start of a sequence—that is, when n - 1 isn’t in the set.

Now, let’s compress that logic into a Pythonic one-liner.

3. The One-Liner Solution

Python’s expressive capabilities let us pack a punch in a single line:

longestConsecutive = lambda nums: max((sum((n+i in nums_set) for i in range(len(nums))) for n in (nums_set := set(nums)) if n-1 not in nums_set), default=0)

Let’s unravel what’s happening here:

nums_set := set(nums) — Using the walrus operator (Python 3.8+), we store the set.
if n-1 not in nums_set — Only start counting from sequence beginnings.
sum((n+i in nums_set) for i in range(len(nums))) — Check how long a sequence continues starting at n.
max(..., default=0) — Gets the max length, defaulting to 0 if empty.

While not the most readable, it captures the core logic concisely and performs just as efficiently.

4. Performance and Optimization Tips

Why is this version performant despite looking overly compact? Because:

Set lookups are O(1): Each n+i in nums_set is constant time.
Only sequence starts are considered: We reduce unnecessary work by filtering at if n - 1 not in nums_set.
No sorting needed: We avoid the O(n log n) bottleneck of sorting altogether.

However, bear in mind:

The one-liner uses the walrus operator, so it requires Python 3.8+.
Readability suffers: Consider keeping a clearer multi-line version for production code.

5. Real-World Usage and Automation Angles

This pattern is handy for automation scripts or pipeline transformations that require quick statistical calculations on large, sparse integer streams—such as analyzing timestamps, log sequence numbers, or user activity streaks.

In such cases, converting compact numerical data into Python sets and applying this algorithm can efficiently provide meaningful insights—like the longest streak of user activity with minimal code overhead. You can also integrate this one-liner into automated code pipelines where performance and brevity are both valued, such as in lambda functions in AWS or inline data transformations in frameworks like Pandas (with modification).

Conclusion

We’ve seen how a classic Leetcode challenge can be elegantly distilled into a single line in Python without giving up performance. While the dense syntax may look intimidating, breaking it down step by step proves its logic and efficiency. Use this one-liner as a smart code-golf trick or keep the expanded form handy for team collaboration. Either way, you’ve just added a powerful coding pattern to your Python toolbox!

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