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Data Structures and Algorithms in Python

Python is the most complete way to take this path: every course is taught in it except bit manipulation, which is C++ only and listed after the steps. You build the stack, the hash table, the heap and the AVL tree yourself, so list, dict, deque and heapq stop being magic, then prove it on graded interview problems. Free, in your browser, with a certificate on most courses.

404 lessons228 challenges702 quiz questions

DSA in Python, step by step

40 courses404 lessons228 challenges702 quiz questions

Each step is a set of existing Coddy courses, and every Start button opens them in Python. The one course not taught in Python, bit manipulation, is listed after the steps.

  1. 1
    Start this stepStartStack, queue, binary tree, hash table and linked list, each built from scratch in Python and then used to solve problems. You finish knowing what list, deque and dict do for you, and what they cost.Start
  2. 2
    Start this stepStartDoubly linked list, heaps and priority queues, tries, graphs and the self-balancing AVL tree. After it, heapq is a binary heap you have written, and the balanced tree Python never gave you is one you can build.Start
  3. 3
    Start this stepStartBubble, selection, insertion, merge, quick, heap, counting and radix sort, written in Python and watched in the visualizer. Python's own sorted() is Timsort, a hybrid of merge sort and insertion sort, and after this step you can say why that is a good idea.Start
  4. 4
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Intro to Variables
Audio

A variable is a named container that stores a value you can reference later in your program.

In Python, you create one by writing the name, an equals sign, then the value you want to store.

The value can change over time - reassigning the name simply points it to a new value.

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Why learn DSA in Python on Coddy

  • The whole path, in one language. The data structures, the sorts, recursion, dynamic programming, the graph algorithms and the interview packs are all taught in Python, and so is the Python interview series, which exists in no other language. Only bit manipulation is not: it is taught in C++ and listed after the steps.
  • Built-ins you can explain. Python hands you list, dict, set, deque and heapq. Building the structures behind them is how you learn why list.pop(0) is slow, why a dict lookup is constant time on average, and when a heap beats sorting, which is exactly what an interviewer asks after you use one.
  • Short code, so the idea shows. A linked list or a binary search fits on one screen in Python, with no type declarations or memory management in the way. That is why it is the fastest language to learn algorithms in, and why so many candidates choose it for coding interviews.
  • Graded like an interview. Every lesson ends in a challenge checked by test cases, and when one fails, Bugsy reads your Python and nudges you toward the fix without handing over the answer. A free certificate on most courses, each verifiable at its own URL.

Frequently asked questions about DSA in Python

Is Python good for data structures and algorithms?

Yes, and for learning it is arguably the best choice: the code is short, so what you read is the algorithm, not the syntax around it. Two trade-offs are worth knowing. Python runs slower than Java or C++, which matters on tight competitive programming time limits but rarely in an interview, and its built-ins hide the costs you are learning to reason about, which is why this path has you build them first.

Which Python data structures should I know for coding interviews?

list (a dynamic array), dict and set (hash tables), tuple, collections.deque (a queue that is fast at both ends), heapq (a binary min-heap on a list) and collections.Counter. Know what each operation costs, not just its name. Python has no built-in linked list, tree, trie or graph, so those you write yourself, in steps one and two.

Why implement a stack or a queue when Python already has them?

Because interviews rarely ask you to use a queue and often ask why your solution is slow. A queue built on a list pays for every pop(0), because every remaining element shifts one place; deque.popleft() does not. Implementing each structure once is how you learn the costs well enough to pick the right one without thinking.

Is Python fast enough for competitive programming?

For most problems, yes. On tight time limits C++ is the safer choice, which is why most competitive programmers use it, and many judges offer PyPy, which runs the same Python code much faster. In an interview, how fast you write matters far more than how fast the code runs, and there Python wins.

What is Python's recursion limit, and does it matter for DSA?

CPython stops at a depth of 1,000 calls by default, so a recursive depth-first search over a long chain can raise RecursionError. You can raise the limit with sys.setrecursionlimit, but the better habit, and the one interviewers like to see, is knowing how to replace the recursion with a loop and an explicit stack: the structure you build in step one.

Do I need to know Python before starting this path?

You should be comfortable with functions, loops, lists and dictionaries, and ideally classes, since every structure here is written as one. If you are not there yet, Coddy's Python course takes you there first, free, and this path picks up where it ends.

Other learning paths

The same courses, arranged for a different role. Progress carries over: a course finished on one path counts on every path that includes it.

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