| CARVIEW |
Beginner
30h
Updated 2 weeks ago
Data Structures for Coding Interviews in Python
Content
1.
Introduction to Complexity Measures
22 Lessons
2.
Introduction to Lists
26 Lessons
3.
Introduction to Linked Lists
30 Lessons
4.
Introduction to Stacks and Queues
25 Lessons
5.
Introduction to Graphs
26 Lessons
6.
Introduction to Trees
38 Lessons
7.
Trie
14 Lessons
8.
Introduction to Heap
12 Lessons
9.
Introduction to Hashing
33 Lessons
10.
Summary of Data Structures
2 Lessons
Course Author:
Trusted by 2.8 million developers working at companies
Anthony Walker
@_webarchitect_
Evan Dunbar
ML Engineer
Software Developer
Carlos Matias La Borde
Souvik Kundu
Front-end Developer
Vinay Krishnaiah
Software Developer
See how Educative uses AI to make your learning more immersive than ever before.
AI Prompt
Code Feedback
Explain with AI
AI Code Mentor
Related Courses and Skill Paths
Free Resources
Frequently Asked Questions
What are the data structures for coding interviews in Python?
For coding interviews in Python, focus on these essential data structures:
- Lists: Used for dynamic arrays that support fast access, insertion, and deletion.
- Dictionaries: Implement hash tables for efficient key-value storage and lookups.
- Sets: Store unique elements and provide fast membership checks.
- Tuples: Immutable sequences used for fixed-size collections.
- Queues and stacks: Use collections.deque for double-ended queues, which can also efficiently implement stacks and queues.
- Heaps: Use heapq for priority queues.
- Linked lists, trees, and graphs: Implement manually using classes to handle more complex problems.
Mastering these structures and their operations will prepare you well for Python coding interviews.
Can I use Python for DSA in an interview?
Yes, you can use Python for DSA in interviews. Python is highly popular for its simple syntax, readability, and powerful built-in libraries, which make implementing data structures and algorithms easier. It offers built-in support for lists, dictionaries, sets, and other data structures, along with libraries like collections and heapq for more advanced needs. Python’s versatility and ease of use make it a great choice for demonstrating problem-solving skills in interviews.
How to prepare for a coding interview in Python
To prepare for a coding interview in Python, focus on mastering key data structures (like lists, dictionaries, sets, and heaps) and algorithms (such as sorting, searching, and dynamic programming). Practice solving problems on platforms like LeetCode or HackerRank to build familiarity with Python’s syntax and libraries. Understand time and space complexities and review Python-specific features like list comprehensions, generator expressions, and built-in functions. Regularly simulate coding interviews to improve your problem-solving speed and communication skills.
Is Python a good choice for coding interviews?
Yes, Python is an excellent choice for coding interviews. Its clean and concise syntax allows you to write and debug code quickly, making it ideal for solving complex problems under time constraints. Python’s extensive standard library provides built-in data structures (like lists, dictionaries, and sets) and algorithms, which help simplify implementations. It’s widely accepted in interviews across various companies, and many interviewers are familiar with its capabilities, making it a strong option for effectively demonstrating problem-solving skills.
What are the five data types in Python?
The five primary data types in Python are as follows:
- Integers (int): Represent whole numbers, such as 1, 42, or -7.
- Floating-point numbers (float): Represent decimal numbers, such as 3.14, 0.001, or -2.5.
- Strings (str): Represent sequences of characters, such as “hello”, “Python”, or “123”.
- Booleans (bool): Represent truth values, either True or False.
- NoneType (None): Represents the absence of a value or a null value, expressed as None.
These basic data types form the foundation for handling and manipulating data in Python.
