Python from scratch
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
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🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐞𝐥𝐭 𝐢𝐦𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐛𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟗 𝐬𝐭𝐞𝐩𝐬 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠!
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1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.
9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this 👍❤️
#Python
.
.
1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.
9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this 👍❤️
#Python
👍4
Python For Everything!🐍
Python, the versatile language, can be combined with various libraries to build amazing things:🚀
1. Python + Pandas = Data Manipulation
2. Python + Scikit-Learn = Machine Learning
3. Python + TensorFlow = Deep Learning
4. Python + Matplotlib = Data Visualization
5. Python + Seaborn = Advanced Visualization
6. Python + Flask = Web Development
7. Python + Pygame = Game Development
8. Python + Kivy = Mobile App Development
#Python
Python, the versatile language, can be combined with various libraries to build amazing things:🚀
1. Python + Pandas = Data Manipulation
2. Python + Scikit-Learn = Machine Learning
3. Python + TensorFlow = Deep Learning
4. Python + Matplotlib = Data Visualization
5. Python + Seaborn = Advanced Visualization
6. Python + Flask = Web Development
7. Python + Pygame = Game Development
8. Python + Kivy = Mobile App Development
#Python
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Topic: Data Structures – Trees – Top 15 Interview Questions with Answers
---
### 1. What is a tree data structure?
A hierarchical structure with nodes connected by edges, having a root node and child nodes with no cycles.
---
### 2. What is the difference between binary tree and binary search tree (BST)?
A binary tree allows up to two children per node; BST maintains order where left child < node < right child.
---
### 3. What are the types of binary trees?
Full, perfect, complete, skewed (left/right), and balanced binary trees.
---
### 4. Explain tree traversal methods.
Inorder (LNR), Preorder (NLR), Postorder (LRN), and Level Order (BFS).
---
### 5. What is a balanced tree? Why is it important?
A tree where the height difference between left and right subtrees is minimal to ensure O(log n) operations.
---
### 6. What is an AVL tree?
A self-balancing BST maintaining balance factor (-1, 0, 1) with rotations to balance after insert/delete.
---
### 7. What are rotations in AVL trees?
Operations (Left, Right, Left-Right, Right-Left) used to rebalance the tree after insertion or deletion.
---
### 8. What is a Red-Black Tree?
A balanced BST with red/black nodes ensuring balance via color rules, offering O(log n) operations.
---
### 9. How does a Trie work?
A tree structure used for storing strings, where nodes represent characters, allowing fast prefix searches.
---
### 10. What is the height of a binary tree?
The number of edges on the longest path from root to a leaf node.
---
### 11. How do you find the lowest common ancestor (LCA) of two nodes?
By traversing from root, checking if nodes lie in different subtrees, or by storing parent pointers.
---
### 12. What is the difference between DFS and BFS on trees?
DFS explores as far as possible along branches; BFS explores neighbors level by level.
---
### 13. How do you detect if a binary tree is a BST?
Check if inorder traversal yields a sorted sequence or verify node values within valid ranges recursively.
---
### 14. What are leaf nodes?
Nodes with no children.
---
### 15. How do you calculate the number of nodes in a complete binary tree?
Using the formula: number\_of\_nodes = 2^(height + 1) - 1 (if perfect), else traverse and count.
---
### Exercise
Write functions for inorder, preorder, postorder traversals, check if tree is BST, and find LCA of two nodes.
---
#DSA #Trees #InterviewQuestions #BinaryTrees #Python #Algorithms
---
### 1. What is a tree data structure?
A hierarchical structure with nodes connected by edges, having a root node and child nodes with no cycles.
---
### 2. What is the difference between binary tree and binary search tree (BST)?
A binary tree allows up to two children per node; BST maintains order where left child < node < right child.
---
### 3. What are the types of binary trees?
Full, perfect, complete, skewed (left/right), and balanced binary trees.
---
### 4. Explain tree traversal methods.
Inorder (LNR), Preorder (NLR), Postorder (LRN), and Level Order (BFS).
---
### 5. What is a balanced tree? Why is it important?
A tree where the height difference between left and right subtrees is minimal to ensure O(log n) operations.
---
### 6. What is an AVL tree?
A self-balancing BST maintaining balance factor (-1, 0, 1) with rotations to balance after insert/delete.
---
### 7. What are rotations in AVL trees?
Operations (Left, Right, Left-Right, Right-Left) used to rebalance the tree after insertion or deletion.
---
### 8. What is a Red-Black Tree?
A balanced BST with red/black nodes ensuring balance via color rules, offering O(log n) operations.
---
### 9. How does a Trie work?
A tree structure used for storing strings, where nodes represent characters, allowing fast prefix searches.
---
### 10. What is the height of a binary tree?
The number of edges on the longest path from root to a leaf node.
---
### 11. How do you find the lowest common ancestor (LCA) of two nodes?
By traversing from root, checking if nodes lie in different subtrees, or by storing parent pointers.
---
### 12. What is the difference between DFS and BFS on trees?
DFS explores as far as possible along branches; BFS explores neighbors level by level.
---
### 13. How do you detect if a binary tree is a BST?
Check if inorder traversal yields a sorted sequence or verify node values within valid ranges recursively.
---
### 14. What are leaf nodes?
Nodes with no children.
---
### 15. How do you calculate the number of nodes in a complete binary tree?
Using the formula: number\_of\_nodes = 2^(height + 1) - 1 (if perfect), else traverse and count.
---
### Exercise
Write functions for inorder, preorder, postorder traversals, check if tree is BST, and find LCA of two nodes.
---
#DSA #Trees #InterviewQuestions #BinaryTrees #Python #Algorithms
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