LeetCode Python Guide — The Complete Intermediate Reference

Python accounts for over 60% of LeetCode submissions — more than any other language. That dominance is not accidental. Python's concise syntax, powerful standard library, and readable code make it the fastest language to write correct solutions under time pressure.

For technical interviews, Python provides a decisive edge: list comprehensions replace verbose loops, built-in sorting handles comparison logic in one line, and the collections module gives you hash maps, heaps, and queues without any imports beyond the standard library.

This guide is the complete intermediate reference for leetcode python. It covers every data structure, syntax shortcut, and standard library pattern you need — organized so you can use it as a cheat sheet during practice and a review guide before interviews.

Whether you are switching from Java or C++, or deepening your Python fluency, this reference will show you the patterns where Python's design philosophy — batteries included — gives you the biggest interview advantage.

Choosing the right data structure is half the battle in any LeetCode problem. Python's built-in types and the collections module give you everything you need — you just need to know which one to reach for and its time complexity.

list: Python's dynamic array. O(1) append/pop from end, O(n) insert/remove from middle. Use as a stack (append/pop) or a general sequence. Slicing creates a copy — be mindful of O(n) slice cost in tight loops.

dict: Hash map with O(1) average get/set/delete. Use for frequency counting, memoization, and graph adjacency. Python 3.7+ dicts maintain insertion order, which matters for certain interview problems.

set: Hash set with O(1) average add/remove/contains. Use for duplicate detection, visited tracking in BFS/DFS, and set intersection/union operations. Union is |, intersection is &, difference is -.

collections.deque: Double-ended queue with O(1) append/pop from both ends. Use instead of list when you need a queue (appendleft/popleft) or a sliding window. list.pop(0) is O(n); deque.popleft() is O(1).

heapq: Python's min-heap module. heapq.heappush(h, val) and heapq.heappop(h) both run in O(log n). For a max-heap, push negative values. Use for top-K problems, merge K sorted lists, and Dijkstra's algorithm.

collections.Counter: Subclass of dict that counts hashable objects. Counter("aabbc") gives {"a":2,"b":2,"c":1}. Supports arithmetic — you can add and subtract Counters. Python's collections.Counter eliminates the need to manually build frequency maps — a pattern that appears in 30%+ of LeetCode Easy and Medium problems.

collections.defaultdict: dict subclass that never raises KeyError. defaultdict(int) initializes missing keys to 0; defaultdict(list) initializes to []. Use for graph adjacency lists and grouping problems.

Python's expressive syntax lets you write correct solutions faster. These shortcuts are not just style — they reduce the lines of code you have to think through under interview pressure.

List comprehensions: [x*2 for x in nums if x > 0] replaces a four-line for loop. Use them for filtering, mapping, and flattening. Nested comprehensions handle matrix operations: [[row[i] for row in matrix] for i in range(cols)] transposes a matrix in one line.

Slicing: nums[::-1] reverses a list. nums[l:r+1] extracts a subarray. s[1:] removes the first character of a string. Slicing creates a new object — use it freely for readability, but be aware of the O(k) copy cost for length-k slices.

zip and enumerate: for i, val in enumerate(nums) gives index and value together. for a, b in zip(list1, list2) pairs elements. zip(matrix, matrix[1:]) pairs adjacent rows. These replace the verbose range(len(x)) pattern.

sorted with key: sorted(intervals, key=lambda x: x[0]) sorts by first element. sorted(words, key=len) sorts by length. sorted(nums, key=lambda x: -x) sorts descending. The key parameter handles any custom comparison without a comparator function.

Ternary expressions: val = x if condition else y replaces three-line if/else blocks. Use for short conditionals in comprehensions and assignments.

Tuple unpacking: a, b = b, a swaps two variables without a temp. left, right = node.left, node.right destructures in one line. x, *rest = nums gives the first element and the remainder.

Multiple assignment and chaining: a = b = 0 initializes both to zero. 1 <= x <= 10 is valid Python — chained comparisons work as expected and are more readable than x >= 1 and x <= 10.

Python's standard library is deep. For LeetCode, five modules stand out as giving you the biggest edge: collections, heapq, bisect, functools, and itertools.

collections.Counter: As noted above, Counter handles all frequency-counting problems — anagram detection, character frequency comparison, and majority element finding. counter.most_common(k) returns the top K elements in O(n log k) time, solving top-K-frequent in one line.

heapq: Python only has a min-heap. For problems requiring a max-heap, push -val and negate when you pop. heapq.nlargest(k, nums) and heapq.nsmallest(k, nums) give you top-K in O(n log k) without manually managing a heap.

bisect: bisect.bisect_left(sorted_list, target) returns the insertion point using binary search in O(log n). Use it for binary search problems without writing the search loop manually. bisect_right returns the index after any existing matches.

functools.lru_cache and functools.cache: @functools.cache (Python 3.9+) or @functools.lru_cache(maxsize=None) memoizes a recursive function automatically. Adding one decorator line converts a naive recursive DFS into a memoized DP solution.

itertools: itertools.combinations(nums, k) generates all k-combinations. itertools.permutations(nums) generates all permutations. itertools.product(ranges) generates the Cartesian product. For backtracking problems, these can replace the recursive generator entirely when all combinations are needed.

Unlike Java or C++, Python has no integer overflow — interviewers often expect candidates to know this and use it as a follow-up question about language differences. Python integers are arbitrary precision, so you never need to worry about overflow guards or Long types.

Python's data structures and syntax map naturally onto the most common LeetCode patterns. Here is how each core pattern looks in idiomatic Python.

Two pointers: left, right = 0, len(nums)-1 with a while left < right loop. Python's swap idiom (nums[left], nums[right] = nums[right], nums[left]) makes in-place operations clean. Slicing (s == s[::-1]) is a readable palindrome check for strings.

Sliding window with deque: Use collections.deque with popleft() as the window shrinks on the left and append() as it expands on the right. For monotonic queues (max/min in window), maintain a deque of indices and pop elements that are no longer valid.

DFS with recursion: Python's default recursion limit is 1000. For deep trees, either use sys.setrecursionlimit() or convert to iterative DFS with an explicit stack. Recursive DFS is clean and readable — use it unless the input depth could hit the limit.

BFS with deque: from collections import deque; queue = deque([start]). Use popleft() for O(1) dequeue. Track visited with a set. BFS level-order traversal uses a while queue loop with an inner for _ in range(len(queue)) loop to process one level at a time.

DP with memoization: @functools.cache above a recursive function handles memoized DP automatically. For bottom-up DP, Python list initialization is clean: dp = [0] * (n+1) or dp = [[False]*(cols+1) for _ in range(rows+1)].

Graph with defaultdict: adj = defaultdict(list) builds an adjacency list without KeyError guards. For weighted graphs: adj = defaultdict(list) with adj[u].append((v, weight)). This is cleaner than pre-initializing all keys.

Even experienced Python developers make these mistakes under interview pressure. Knowing them ahead of time prevents debugging spirals during the interview.

Mutable default arguments: def func(nums=[]) is a classic Python trap — the default list is created once and shared across all calls. Under interview pressure, it is easy to write this and be confused why values persist between test cases. Always use None as default and initialize inside the function: if nums is None: nums = [].

list.pop(0) vs deque.popleft(): Removing from the front of a list is O(n) because all elements shift. If your solution uses a queue pattern with pop(0), it will TLE on large inputs. Replace with collections.deque and popleft() for O(1) behavior.

Shallow copy trap: new_list = old_list just creates another reference to the same list. new_list = old_list[:] or new_list = list(old_list) creates a shallow copy. For 2D grids, copy.deepcopy(grid) is required if you need a fully independent copy.

Integer overflow does not exist in Python: Unlike Java (int is 32-bit) or C++ (int overflow is undefined behavior), Python integers grow to arbitrary size. You never need to mod by 10^9+7 for safety — only when the problem explicitly requires it. Interviewers often ask about this difference.

In-place vs returning a new list: Many Python built-ins have both in-place and returning forms. list.sort() sorts in place and returns None. sorted(list) returns a new sorted list. list.reverse() modifies in place; list[::-1] returns a new reversed list. Using sorted() when you mean list.sort() on a large dataset copies the entire list unnecessarily.

String immutability: Python strings are immutable. Building a string in a loop with += creates O(n^2) intermediate strings. Use a list to collect characters and "".join(chars) at the end for O(n) string building.

Python's dominance on LeetCode is well-earned. The language lets you focus on the algorithm rather than boilerplate — but only if you know the idioms. A developer who reaches instinctively for Counter instead of a manual dict, or for @functools.cache instead of a memo dict, writes solutions that are both faster to produce and easier to read.

The patterns in this guide — data structures, syntax shortcuts, standard library wins, and common mistakes — cover the vast majority of what you need for Python coding interviews. They are not obscure tricks; they are the core vocabulary of idiomatic Python as applied to algorithms.

The fastest path to fluency is deliberate repetition. Use this guide as a reference during practice sessions, and use spaced repetition to keep the patterns sharp. YeetCode flashcards cover Python-specific LeetCode patterns alongside the algorithmic content, so you build both skills simultaneously.

Review the cheat sheet in section 2 before each interview session. After enough repetitions, these patterns become automatic — and automatic recall under interview pressure is exactly what separates a clean solution from a frustrated debugging session.