Data Structures and Algorithms in Python - Comprehensive Guide

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This beginner-friendly tutorial will explore data structures and algorithms in Python programming language. Moreover, we will also learn the basics, starting with built-in data structures like lists, tuples, and dictionaries. We'll also dive into custom-made structures such as linked lists, trees, and graphs. Along the way, we'll cover how to move through these structures (traversal), find things (searching), and organize stuff (sorting).

Let’s join us and navigate through the fundamental concepts of DSA in Python and empower your programming prowess.

Learn Data Structures And Algorithms in Python

First, we will learn about data structures and then algorithms in Python. 

Built-in Data Structures in Python

There are various in-built data structures of Python, such as - 
 

1. Python Lists
   
   Python Lists are groups of organized information, similar to arrays in other programming languages. You can add all kinds of items to a list. Python's list implementation is comparable to how vectors regulated in C++ or ArrayLists operated in Java. Moreover, you can add or remove items from the beginning of a list. As a result, it can be slow and requires shifting everything else. Additionally, doing the same at the end can be tricky if the list is almost complete.

      2. Tuple

  Python tuples are a list that can't be changed once you've made them. Unlike lists that can be tweaked or updated, it stays the same once you create a tuple. Similar to lists, tuples can hold different types of things. Creating a tuple in Python is like arranging bits of information using commas. Moreover, you can put them in parentheses to make it clear under data structures and algorithms in Python.
 

3. Set

Python sets are a flexible way to group items, with a rule that no duplicates are allowed. Sets are useful for checking if an item belongs to the group and ensuring no repeated entries. This clever trick allows for quick addition, removal, and finding of items in the group. If there are multiple items in the same spot, they form an organized list. In CPython, sets are similar to dictionaries but with clever shortcuts to make everything faster and more efficient.
 

4. Frozen Sets

Python frozen sets a set that doesn't change once you make it. While typical sets can be changed whenever you want, frozen sets are like a snapshot that stays as it is. You can do things with them, but they won't change under data structures and algorithms in Python.
 

5. Strings

Python strings are a variety of collections of letters and symbols you can't change once you've made them. In Python data structures, we don't have a unique type for just one letter; even a single letter is treated like a tiny collection called a string. So, a string is a lineup of letters and characters, and once you set it up, it stays the same.
 

6. Dictionary

Python dictionaries are handy organizers that don't care about the order of things. This tag is called a key, and it's linked to some data. So, if you want to find something in this box, just use its key to get it quickly. Apart from that, it doesn't matter what kind of info it is – it can be words, numbers, or even small collections.
 

7. Matrix

A matrix is a neat grid where every little square is the same size. So, in a matrix, everything is lined up in rows and columns, and all the bits of info are the same size.
 

8. Bytearray

Python byte array is a changeable lineup of numbers, but there's a rule as the numbers can only be between 0 and 255. In addition, you can adjust as needed, but they always stay within a specific range.
 

9. Stack

A stack is a neat pile of items where the last thing you put on top is the first thing you take off. In the world of stacks, we call ‘push" and take off "pop".
 

10. Queue

A queue is just like the first person to arrive is the first to get served. It's like when you're patiently waiting for your turn. In a queue, the thing that's been waiting the longest is the one that gets taken care of first. In short, the least recently added thing gets attention first. 

Here are some in-built data structures of Python. Now, we will learn different algorithms of DSA with Python. 

Algorithms in Python

1. Depth First Traversals
 

• In order (Left, Root, Right): First, go to the left side, then visit the root, and finally go to the right side.

Example: 4, 2, 5, 1, 3
 

• Preorder (Root, Left, Right): Start at the root, then go left, and then go right. 

Example: 1, 2, 4, 5, 3
 

• Postorder (Left, Right, Root): This time, first, go left, then go right, and finally, visit the root. 

Example: 4, 5, 2, 3, 1 

2. Level Order or Breadth-First Traversals
 

In the example tree, it would be like strolling through the levels 1, 2, 3, 4, and 5.
 

• First, create an empty line (let's call it a queue) where you wait your turn.
• After that, start from the very top, the root of the tree.
• Keep going while there's still a place to visit (not NULL).
• At each spot, say hello to the node you're at.
• Add the node's kids (first left, then right) to the waiting line.
• At last, move on to the next spot in line.

This way, you can cover the levels separately, meeting each node and ensuring not miss anyone.
 

3. Binary Search Tree
 

• Lesser on the Left: Each node has a key. If someone is younger, they go on the left. So, the left side is for the younger folks.
• Greater on the Right: On the other side, if someone is older, they go on the right. The right side is for the older folks.
• Family Tree Rules: Now, these rules apply not just to the main person but to everyone in the family. Each person, or node, follows the same family tree rules - younger ones to the left and older ones to the right. Also, the left and right sides, the younger and older parts, follow these same rules.
   

4. Graph

A graph is a bunch of points or nodes or vertices) and lines connecting these points (edges). It's a way of showing relationships between different things. In short, a graph is just a collection of these dots and lines. Formally, you can say having a set of dots (nodes) and lines (edges) that connect pairs of dots.

Questions For Python Data Structures And Algorithms For Interviews

Here are some common related questions for data structures and algorithms in Python that you might encounter in interviews:
 

Q1. What are lists and tuples?

Ans. Python's list implementation is comparable to how vectors are regulated in C++ or ArrayLists are operated in Java. Unlike lists that can be tweaked or updated, it stays the same once you create a tuple.
 

Q2. Why do we utilize queues?

Ans. In a queue, the thing that's been waiting the longest is the one that gets taken care of first. In short, the least recently added thing gets attention first.
 

Q3. Define stalk.

Ans. A stack is a neat pile of items where the last thing you put on top is the first thing you take off. In the world of stacks, we call ‘push" and take off "pop."

Other interview questions include:

1. Write a Python function to reverse a given string in-place.
2. Implement a function to detect if a linked list contains a cycle.
3. Write a Python function to perform a binary search on a sorted array.
4. Solve the Fibonacci sequence problem using dynamic programming in Python.
5. Write a Python function that takes two sorted arrays as input and returns a new array containing common elements.

Conclusion

Learning data structures and algorithms in Python is necessary to understand each concept. By using this guide, you have taken a significant step forward. Moreover, mastering the fundamentals of these concepts in Python can help you solve complex problems and write efficient code. In addition, remember it is essential to practice and apply what you have learned in situations. As you comprehend the various data structures and algorithms we have covered here. However, it will help you gain a better understanding and become a more skilled Python programmer.