DSA with Python - Why? Pros, Pitfalls & Best Practices. Frequently updated.

Hello readers!

Why Python is Great for Learning Data Structures & Algorithms (DSA)

When it comes to mastering Data Structures and Algorithms (DSA), choosing the right language can make a huge difference. Python stands out as one of the best choices—and here's why.

🧠 Simple Syntax, Clear Logic

Python's clean and readable syntax makes it much easier to focus on the logic rather than getting lost in the language. Whether it's a binary tree, graph traversal, or dynamic programming, you write fewer lines of code, and they’re easier to understand at a glance.

🎯 Perfect for Interviews

In coding interviews, time is precious. Python helps you explain your approach clearly, thanks to its natural-language-like structure. It’s much easier to walk your interviewer through a solution written in Python than in verbose languages like Java or C++.

🔧 Built-in Power

Python offers powerful built-in data structures like lists, sets, and dictionaries, which allow you to quickly prototype and test ideas without reinventing the wheel.

🚀 Fast to Write, Easy to Debug

Quick iterations and minimal boilerplate make Python ideal for solving multiple problems efficiently while preparing for interviews or contests.

In short: Python lets you focus on thinking like a problem-solver—not just a programmer. And that’s exactly what interviews test for.

Now, starting below, I'm appending important notes about using Python in DSA as I come across them.

Python’s Recursion Limit

One common pitfall in Python when writing recursive solutions—especially for problems like DFS or tree traversal—is hitting the recursion depth limit. Unlike some other languages, Python has a relatively low default recursion limit (usually around 1000), which can easily be exceeded for deep recursive calls.

import sys

sys.setrecursionlimit(10**6)  # Use cautiously!

def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n - 1)

print(factorial(1000))  # This will likely raise a RecursionError

If you're not careful, this can lead to a RecursionError: maximum recursion depth exceeded. Always consider whether a recursive solution is the best approach—or whether it can be rewritten iteratively or optimized with memoization or tail recursion (which Python doesn’t optimize by default).

Sure! Here's your cleaned-up, well-formatted, and streamlined mini blog on Python operations that might appear O(n) but actually aren't (or vice versa) — with duplicate info removed and sections better organized:

🧠 Understanding Python's Time Complexity: Operations That Seem O(n) But Aren't

When working with Python for DSA, understanding the real time complexity of common operations can help you write more optimized code — and explain it better in interviews! Some operations might look like they take linear time but are actually constant (or vice versa). Let’s explore these nuanced cases:

🔍 in Operator — list vs set/dict

✅ in on a set or dict: O(1) average

⚠️ in on a list: O(n)

# O(n): list
print(3 in [1, 2, 3, 4, 5])

# O(1): set or dict
print(3 in {1, 2, 3, 4, 5})

Python uses hash tables for sets and dictionaries, enabling constant-time lookups.

➕ list.append(x)

✅ Looks like: O(n)

Actually: Amortized O(1)

Python lists are dynamic arrays. They resize occasionally, but append() is amortized constant time.

nums = [1, 2]
nums.append(3)  # O(1) on average

🧹 list.pop(0) vs deque.pop()

⚠️ list.pop(0): O(n)

✅ deque.pop() (either end): O(1)

from collections import deque

# O(n): shifts all elements
lst = [1, 2, 3]
lst.pop(0)

# O(1): deque
dq = deque([1, 2, 3])
dq.pop()

Use deque for efficient queue-like operations.

↔️ list.insert(0, x) and list.remove(x)

• insert(0, x) shifts all elements right → O(n)
• remove(x) searches + shifts → also O(n)

nums = [1, 2, 3]
nums.insert(0, 0)  # Insert at start: O(n)

nums.remove(2)     # Remove specific value: O(n)

🧩 Copying a List

new_list = old_list[:]  # or list(old_list)

Although it looks trivial, copying a list is O(n) because it duplicates every element.

🔀 list.sort() / sorted(list)

✅ Looks like: O(n)

Actually: O(n log n)

nums = [4, 1, 3]
nums.sort()  # Uses Timsort: O(n log n)

Python uses Timsort, which is optimized but still O(n log n) in worst-case scenarios.

✂️ String Concatenation in a Loop

s = ""
for word in words:
    s += word  # O(n²) in total!

Each += creates a new string, leading to quadratic time if done repeatedly.

✅ Better: ''.join(words) → O(n) total.

⚙️ Dict Resizing

Dictionaries in Python offer amortized O(1) insertions. But occasionally, the internal table resizes and rehashes all keys, which spikes to O(n).

You don't need to worry in practice, but it's good to mention in interviews for depth.

✅ TL;DR

Using Comparator Functions in Python’s sorted() and .sort()

In Python, both the sorted() function and the .sort() method are used to sort collections. While they are quite similar, they differ slightly in their usage and functionality. Python also allows you to provide a comparator function to customize the sorting order.

🔄 sorted() vs .sort()

• sorted(): Returns a new sorted list from the elements of any iterable.
• .sort(): Sorts the list in-place and returns None.

💡 Using a Comparator Function

Python uses a key function instead of a traditional comparator. The key function transforms each element before comparison, which is more efficient and preferred in Python. However, you can simulate the effect of a comparator using functools.cmp_to_key().

Note: cmp_to_key is part of the functools library, which is built into Python, so you do not need to install it separately.

Example 1: Using sorted() with a Comparator

from functools import cmp_to_key

# Comparator function
def compare(x, y):
    return x - y  # Sort in ascending order (simulating a comparator)

lst = [5, 2, 9, 1]
sorted_lst = sorted(lst, key=cmp_to_key(compare))  # Using comparator function
print(sorted_lst)

Example 2: Using .sort() with a Comparator

from functools import cmp_to_key

# Comparator function
def compare(x, y):
    return y - x  # Sort in descending order (simulating a comparator)

lst = [5, 2, 9, 1]
lst.sort(key=cmp_to_key(compare))  # Using comparator function
print(lst)

🧠 Internal Algorithm

Both sorted() and .sort() use the Timsort algorithm internally, a hybrid sorting algorithm derived from merge sort and insertion sort. It's optimized for real-world data and has a time complexity of O(n log n) in the average and worst cases.

MIN_INT and MAX_INT

MIN_INT = -sys.maxsize
MAX_INT = sys.maxsize

Will update it with more learnings,
Happy hacking! 🚀