LeetCode 75: A Pattern-Based Study Plan That Works

The LeetCode 75 works best as a pattern-recognition curriculum, not a checklist. If you group related problems, revisit them from memory, and practice explaining your decisions, the list becomes useful preparation for a live technical interview.

This LeetCode 75 study plan focuses on recognition and recall. The goal is not to memorize seventy-five solutions. It is to learn the smaller set of ideas that those solutions reuse.

What the LeetCode 75 List Is Designed to Teach#

The LeetCode 75 list gives you a compact foundation across common data structures and algorithmic patterns.

It covers enough ground to expose recurring interview ideas without asking you to work through the full problem catalog. You encounter array traversal, hashing, linked structures, trees, graphs, search, and optimization. You also get practice moving from a direct solution to one that handles larger constraints.

You can browse the complete LeetCode 75 list, but avoid treating its order as a required sequence. A curated list has to arrange problems somehow. That arrangement does not always produce the strongest learning progression for you.

LeetCode 75 vs Blind 75#

LeetCode 75 and Blind 75 overlap in purpose. Both provide a smaller set of representative interview problems. They differ in selection and organization.

Blind 75 originated as a compact interview-preparation list. LeetCode 75 is an official LeetCode study plan with its own topic mix and sequence. NeetCode-style roadmaps usually make patterns more explicit by placing problems under categories.

None of these formats changes the underlying task. You still need to:

• Recognize the relevant pattern from unfamiliar wording.
• Produce a reasonable brute-force baseline.
• Explain why that baseline becomes expensive.
• State the invariant behind the optimized approach.
• Implement it without depending on memorized syntax.
• Defend its time and space complexity.

The broader pattern reference and other curated lists are useful once you find a weak area. If binary search remains unclear after the list gives you limited exposure, solve several adjacent binary-search problems. Do not restart a completely different roadmap just to avoid targeted review.

LeetCode 75 is also not a guarantee that every topic in your interview will appear. It is a foundation. A company may emphasize concurrency, object-oriented design, domain-specific coding, or a pattern that receives little attention in this list.

Reorganize LeetCode 75 by Pattern, Not List Order#

You will notice similarities faster when you study adjacent problems that share a decision rule.

Before solving the list, create your own pattern index. Assign each problem a primary family and, where useful, a secondary one. Reasonable families include:

• Hash maps and sets
• Two pointers
• Sliding windows
• Prefix sums
• Stacks and monotonic stacks
• Linked lists
• Tree traversal
• Graph traversal
• Heaps and priority queues
• Binary search
• Backtracking
• Dynamic programming
• Greedy algorithms
• Tries
• Bit manipulation
• Matrix and grid traversal

The categories do not need to be perfect. A problem may support several valid approaches. Your classification records the idea you want to retrieve later.

Study related problems close together#

Pattern adjacency lets you compare decision rules while the previous solution is still available in memory.

For example, a group of sliding-window problems helps you distinguish between:

• A fixed-size window and a variable-size window.
• A condition that becomes valid when you expand.
• A condition that requires shrinking from the left.
• A count map that tracks membership.
• A numeric aggregate that can be updated incrementally.

The same applies to graph problems. Several nearby graph exercises make it easier to separate traversal mechanics from the actual state being tracked. One problem may count components. Another may detect a cycle. A third may search for a shortest unweighted path.

Use the pattern hubs to extend a group when the list provides too little repetition. The hash map pattern hub, for example, gives you more opportunities to practice frequency maps, canonical keys, and constant-time lookup.

Keep the original order as a coverage check#

Pattern-first study has one risk: you may remain in a comfortable category for too long.

Keep a copy of the original list and mark completed problems there. This preserves its breadth while letting you choose a better learning order. You can study a small hashing cluster, switch to trees, then return to mixed review.

Your final practice should not remain grouped. Pattern adjacency teaches the pattern. Mixed sessions test whether you can identify it without being told the category.

Use a Three-Pass Method for Every Problem#

Use three distinct passes: analyze, implement, and retrieve.

Each pass has a different purpose. Combining them into one long session makes it hard to tell whether you understood the solution or merely followed it.

First pass: analyze the problem#

Do not start typing immediately. Identify four things first:

1. Input shape: Is the input an array, matrix, tree, graph, interval list, or stream?
2. Likely constraints: Which dimensions can grow? What work would become expensive?
3. Brute-force baseline: What direct method would produce a correct answer?
4. Bottleneck: Which repeated operation dominates that baseline?

Say these points out loud. Interviewers need access to your reasoning, not just the final code.

Suppose your baseline compares every pair of objects. Ask whether you can represent each object once and replace repeated comparison with lookup. If the baseline repeatedly scans a range, ask whether a prefix sum or maintained window can reuse previous work.

That transition matters more than naming the pattern quickly.

Second pass: implement and narrate#

Write the optimized solution while describing its invariant.

An invariant is a condition that remains true as the algorithm runs. Examples include:

• The current window contains no duplicate values.
• The stack remains monotonic after each insertion.
• Every node in the queue has been discovered but not processed.
• The frequency map represents exactly the values before the current index.
• A dynamic-programming entry stores the best answer for a defined prefix.

Explain each data-structure choice. “I need a dictionary” is incomplete. Say what the keys represent, what the values count, and why lookup helps.

Then state complexity in terms of the input. Avoid saying “linear” before defining what the input size means, especially for grids and graphs.

Third pass: retrieve without notes#

Close the solution and solve the problem again from a blank editor.

Test normal cases, empty or minimal inputs where allowed, duplicates, boundary positions, and highly repetitive data. If you cannot reconstruct the invariant, you do not yet own the solution.

Record a short trigger phrase after the re-solve. Good triggers describe evidence in the prompt:

• “Repeated equality checks over structured sequences.”
• “Contiguous range with a condition that changes incrementally.”
• “Dependencies must be processed before dependents.”
• “Need the smallest current item while new items arrive.”
• “Decision repeats over overlapping suffixes.”

Avoid triggers such as “use a hash map.” That records the answer without preserving the reason.

A compact problem log#

Use one row per attempt:

Keep entries short. The log should help you choose the next practice problem, not become a second coding task.

Worked Example: Equal Row and Column Pairs#

LeetCode 2352, Equal Row and Column Pairs is a clean example of replacing repeated structural comparisons with hash lookups.

You receive a square integer grid. You must count row-column pairs whose values match in the same order.

Start with the brute-force approach#

A direct solution considers every row and every column. For each pair, it compares all corresponding positions.

If the grid has side length n, there are n² row-column pairs. Comparing one pair can inspect n values. The total time is therefore O(n³).

The issue is not that comparison is wrong. It is that the same rows and columns are reconstructed or examined repeatedly.

Build a canonical representation#

A row is already an ordered sequence. Convert it to a tuple so Python can use it as a dictionary key.

Next, construct each column as a tuple in the same order. If that tuple appears among the rows, every matching row contributes one valid pair.

A frequency map matters because rows can repeat. Storing only membership would lose their multiplicity.

from collections import Counter
from typing import List

def equalPairs(grid: List[List[int]]) -> int:
    n = len(grid)
    row_counts = Counter(tuple(row) for row in grid)
    pairs = 0

    for column_index in range(n):
        column = tuple(grid[row][column_index] for row in range(n))
        pairs += row_counts[column]

    return pairs

These checks cover a standard match, repeated structures, and a single-cell grid:

assert equalPairs([[3, 2, 1], [1, 7, 6], [2, 7, 7]]) == 1
assert equalPairs([[3, 1, 2, 2], [1, 4, 4, 5],
                   [2, 4, 2, 2], [2, 4, 2, 2]]) == 3
assert equalPairs([[8]]) == 1

Line-by-line explanation#

• n = len(grid) records the grid’s side length.
• Counter(tuple(row) for row in grid) converts every row to a hashable tuple and counts how often each tuple occurs.
• pairs = 0 initializes the result.
• The loop visits each column index once.
• The generator reads that column from top to bottom.
• tuple(...) gives the column the same representation as a row.
• row_counts[column] returns the number of matching rows. It returns zero when the column tuple is absent.
• Adding that count handles each column independently.
• The function returns the total number of matching row-column pairs.

Complexity#

Creating the row tuples processes n² values. Creating all column tuples also processes n² values. Dictionary operations are constant-time average-case lookups for these keys, though hashing a newly created tuple itself depends on its length.

The total time is O(n²). The frequency map can store O(n²) values across its tuple keys, so auxiliary space is O(n²) in the worst case.

Duplicate rows are preserved by the counter. Duplicate columns are each visited and counted. A single-cell grid produces one match because its only row and only column have the same one-element tuple.

How the Hashing Pattern Transfers to Other Questions#

The transferable idea is to convert a structured object into a stable representation, then replace repeated comparison with lookup.

In the grid problem, an ordered sequence becomes a tuple. The dictionary maps that tuple to its frequency. Other problems use the same broad technique with different representations.

Anagram grouping#

Two strings belong to the same anagram group when they have the same character counts. A canonical key might be a sorted string or a tuple of character frequencies.

The representation removes irrelevant ordering from the original strings. Equal keys mean the strings belong to the same group.

Frequency matching#

Some problems ask whether two collections have the same multiplicities or whether one collection can supply another. A map from value to count summarizes the information needed for later comparisons.

The important question is not merely whether a value exists. It is whether its available count satisfies the requirement.

Subtree serialization#

A tree can sometimes be serialized into a representation that includes node values, child structure, and explicit missing children. Repeated serialized forms can then reveal duplicate structures.

This requires care. A representation must distinguish different tree shapes. Concatenating values without separators or null markers can create ambiguous keys.

These problems are not identical. They share a design move: define equality precisely, construct a canonical key, and use hashing to avoid repeated structural work.

Hashable and non-hashable values in Python#

Python dictionary keys must be hashable. Tuples work when every element inside the tuple is also hashable.

If your structure contains lists, convert nested lists to tuples. If it contains a dictionary, use a carefully normalized tuple of key-value pairs when ordering is irrelevant. For trees or graphs, build an unambiguous serialization or assign stable identifiers to previously seen states.

Do not use a representation until you can explain this rule:

Two objects that should count as equivalent must produce the same key, and objects that are not equivalent must not collide by construction.

Build a Study Schedule Around Retrieval, Not Completion#

A useful schedule alternates new work with delayed re-solves and mixed-pattern practice.

Do not prescribe the same deadline for every candidate. Your available time, current background, and interview format matter. Instead, divide each study session by purpose.

A longer session might contain:

1. One delayed re-solve from an earlier pattern.
2. One new problem in the current pattern cluster.
3. One mixed problem whose category is hidden.
4. A short update to the problem log.

With less time, rotate those activities across sessions rather than dropping retrieval entirely. A single independent re-solve often tells you more than several new solutions read passively.

Use hints in stages#

When you get stuck, reveal the smallest amount of information that can restart your reasoning.

Use this order:

1. Restate the input and output with a small example.
2. Write the brute-force method.
3. Identify the repeated work.
4. Review your trigger notes for related patterns.
5. Read a category-level hint.
6. Read the core insight.
7. Read the full editorial or implementation.

After reading a solution, close it. Explain the invariant in your own words. Then implement from a blank file.

Attempt the problem again after enough delay that you cannot copy from short-term memory. If you reproduce the code but cannot explain why it works, schedule another re-solve. If you remember only the final data structure, reconstruct the brute-force-to-optimized transition.

Mixed sessions should become more common as your interview approaches. Real prompts do not arrive with labels such as “sliding window” or “dynamic programming.”

Know When You Are Ready to Move Beyond the List#

You are ready to expand beyond the list when you can solve representative problems independently and defend each decision.

Use observable checks rather than completion status. For a problem you have seen before, you should be able to:

• State a correct brute-force baseline.
• Identify its bottleneck.
• Choose a fitting pattern without opening your notes.
• Define the algorithm’s invariant.
• Implement the solution from scratch.
• Test meaningful edge cases.
• Define the variables used in the complexity analysis.
• Explain how a follow-up constraint would change the approach.

Your problem log will show where this breaks down. Repeated failures to recognize a window condition suggest more sliding-window practice. Correct code with weak complexity analysis calls for explicit counting of loops, states, edges, and stored objects. Tree solutions that fail on missing children point to an invariant or representation problem.

Use the broader LeetCode reference to choose targeted follow-ups rather than collecting another list at random.

Then add unseen variants and mock interviews. Practice speaking while you code. Let your first approach be imperfect, notice the bottleneck, and revise it in front of another person. That is closer to the work required in an interview than finishing the LeetCode 75 problems once and marking them complete.