Learn DSA in Python from scratch

Python suits learners who want concise DSA code and a gentle path from idea to implementation. Its standard library is strong for contests, but you still need to understand the cost of operations hidden behind short syntax.

Open the Python compiler Trace your code line by line Start the path

What you need from Python before DSA

Variables and types

Python names can point to values of different types, so know what each operation returns.

count = 3
label = "items"
print(label, count)

Output: items 3

Conditionals and loops

Most DSA code repeatedly tests a condition while scanning a structure.

total = 0
for value in [2, 4, 5]:
    if value % 2 == 0:
        total += value
print(total)

Output: 6

Functions

A named function keeps one algorithm's inputs, output and scope clear.

def double(value):
    return value * 2

print(double(6))

Output: 12

Recursion

Trees and divide-and-conquer algorithms call the same idea on smaller input.

def countdown(n):
    if n == 0:
        return
    print(n)
    countdown(n - 1)

countdown(3)

Output: 3 2 1

Arrays and lists

A list gives you indexed storage and is the base for many other structures.

numbers = [3, 1, 2]
numbers.append(4)
print(numbers[1], len(numbers))

Output: 1 4

Strings

Parsing and comparing text is common in frequency counts and two-pointer problems.

word = "level"
print(word == word[::-1])

Output: True

Hash maps and sets

They turn repeated membership and frequency checks into direct operations on average.

seen = {2, 4}
counts = {"a": 2}
print(4 in seen, counts["a"])

Output: True 2

References and copying

A list assignment shares the same object; copy deliberately when a branch must not mutate its source.

first = [1, 2]
second = first.copy()
second.append(3)
print(first, second)

Output: [1, 2] [1, 2, 3]

Classes or structs

Linked lists and trees need small node objects with fields such as value and next.

class Node:
    def __init__(self, value):
        self.value = value

print(Node(7).value)

Output: 7

Sorting with a custom comparator

Sorting by a derived key is a standard way to order intervals, pairs and records.

items = [("b", 2), ("a", 1)]
items.sort(key=lambda item: item[1])
print(items)

Output: [('a', 1), ('b', 2)]

Reading input

Contest and interview judges provide data through standard input, not hardcoded arrays.

import sys
values = list(map(int, sys.stdin.read().split()))
print(sum(values))

Input: 2 4

Output: 6

Python toolbox for data structures

Structure	Python type	Key operations
Dynamic array	list	append/pop at end O(1) amortized; indexing O(1)
String building	list + str.join	append pieces, then join once
Hash map	dict	get/set O(1) average
Hash set	set	add/lookup O(1) average
Stack	list	append and pop from the end
Queue	collections.deque	append right, popleft O(1)
Deque	collections.deque	append/pop at both ends O(1)
Priority queue	heapq	heappush/heappop min-heap
Ordered map	No built-in ordered map	use sorted keys or a third-party tree
Sorting	list.sort / sorted	key= comparator, stable

The order to learn DSA

Follow the SDE roadmap order. Each topic starts with two Easy problems from the curated set.