Merge Intervals: Beginner

Sort by start, scan once, merge overlaps. The pattern never changes.

Sort by start, scan once, merge overlaps. The pattern never changes.

Category

Python

Difficulty

beginner

Duration

25 minutes

Challenges

0 hands-on challenges

Topics covered: Merge Intervals: Sort First, Scan Once, Insert Interval: Three Phases, Non-Overlapping Intervals: Greedy by End Time, Data Engineering Applications, Interval Representation in Python

Lesson Sections

1. Merge Intervals: Sort First, Scan Once (concepts: pyMergeIntervals, pyIntervalSort)

   The algorithm is three lines of logic after sorting. Initialize a result list with the first interval. For each subsequent interval: if its start is less than or equal to the current end (overlapping), extend the current end to the maximum of both ends. Otherwise, this interval does not overlap, so push it onto the result as a new standalone interval. That is the entire algorithm. The reason it feels elegant is that sorting eliminates all backward comparisons. You are always merging with the las

2. Insert Interval: Three Phases (concepts: pyInsertInterval, pyThreePhase)

   Insert interval is merge intervals with a twist: the existing list is already sorted and non-overlapping. You are given one new interval to insert in the right place and merge any conflicts. The key insight is that the problem has exactly three phases, and if you code them as three explicit phases, the solution is clean and obvious. If you try to handle everything in one loop with a pile of conditionals, you will make a mistake. The Three Phases The elegance here is the separation of concerns. P

3. Non-Overlapping Intervals: Greedy by End Time (concepts: pyIntervalScheduling, pyGreedyEndTime)

   This problem has two equivalent framings. 'Find the minimum number of intervals to remove so no two intervals overlap.' And: 'Find the maximum number of non-overlapping intervals you can keep.' These are the same problem: max_keep = n - min_remove. Focus on maximizing what you keep. This is the classic interval scheduling maximization problem from algorithm theory, and the greedy strategy is proven optimal. Why Sort by End Time? The greedy insight is this: always keep the interval that ends earl

4. Data Engineering Applications (concepts: pySessionMerging, pyGapDetection, pyTimeRangeOverlap)

   Every one of the three algorithms above has a direct production analog in data engineering. Knowing these connections is what lets you take a coding answer and turn it into a DE answer. After you solve the coding problem, spend 20 seconds naming the production application. It is the difference between a candidate who solved a LeetCode problem and a candidate who understands why this pattern matters. Session Merging User sessions in analytics systems are almost never raw — they are derived by mer

5. Interval Representation in Python (concepts: pyIntervalRepresentation, pyDatetimeIntervals, pyTouchingConvention)

   Before you start coding, you need to decide how to represent intervals. This is not just a style question. The choice of representation affects mutability (can you update the end in-place?), readability, and what the interviewer expects. Most LeetCode problems hand you List[List[int]], so tuples are usually wrong there. In production code, dataclasses are almost always the right choice. Know all three and know when to use each. Tuples vs Lists vs Dataclass Datetime Intervals In production, inter