Learn Python with Handwritten Notes and Free Courses

Take your Python programming skills to the next level with this comprehensive collection of Python programs and Questions. From foundational concepts like loops and functions to practical applications like file handling and basic algorithms, this compilation is perfect for beginners and learners looking to solidify their Python knowledge.🚀 Google has launched 25 free courses with free certificates. No payment is required! (The top 10 are my favorite.) Here are 25 FREE Google courses to master a career in 2025. 📣 Join my telegram for regular job updates, free coding resources and premium notes: telegram.me/CodeTreasure 1 Meta Front-End Developer 🔗 imp.i384100.net/g1KEQ5 2. Programming with JavaScript 🔗 imp.i384100.net/XYDqvg 3. Machine Learning Specialization 🔗 imp.i384100.net/XYQ9jy 4. Deep Learning Specialization 🔗 imp.i384100.net/jroLxe 5. IBM Data Science Professional Certificate 🔗 imp.i384100.net/LXbNjj 6. Python for Data Science, AI & Development 🔗 imp.i384100.net/1rq3Km 7. Google Data Analytics 🔗 imp.i384100.net/KjnNrn 8. Google Cybersecurity 🔗 imp.i384100.net/Or5L6G 9. Google Project Management 🔗 imp.i384100.net/OeRLoP 10. Meta Social Media Marketing 🔗 imp.i384100.net/RGyDYv Google Cloud https://lnkd.in/d9H6U78F Data Structures and Algorithm https://lnkd.in/dKzeg9cj IBM Full STACK Developer https://lnkd.in/dM4ZeQcB Full Stack Java Developer https://lnkd.in/dKHegRm6 Mean Stack Developer https://lnkd.in/d_MW_u4a CI/CD Jenkins https://lnkd.in/ddP9Jrxx Google IT support https://lnkd.in/d_Aqkifz Google IT automation with python https://lnkd.in/dFWpQrU7 IBM Introduction of AI https://lnkd.in/dv-Tae9H Natural Language Processing https://lnkd.in/duA3xb5C Generative AI: Promote Engineering Basics https://lnkd.in/dZCDzW3W Prompt Engineering for ChatGPT https://lnkd.in/dkqeFa8Z Microsoft Cybersecurity Analyst https://lnkd.in/dTHU5KnJ 🪢 7000+ Course Free Access: https://lnkd.in/dA-qUzTc If you find this useful, drop a comment to let me know. Your share would mean the world to me! ❤️🤗 Happy Learning 🌟 ------------------------------------------------------------------ if you found this post helpful: 1️⃣ Join Telegram for more Free Coding & AI Courses with Proper Notes: telegram.me/CodeTreasure 2️⃣ Follow Manish Kumar Shah for more such content. #datascience #machinelearning #dataanlytics #datascientists #python LinkedIn Learning

✨ Python Revision Journey — From Basics to Advanced Concepts As part of my continuous learning and upskilling journey, I’ve successfully completed a comprehensive Python revision, revisiting and practicing every essential concept — from fundamentals to advanced-level programming and project development. 📚 Topics Covered (Day 1 → Today): Core Python Concepts: Syntax, variables, data types, type casting, operators, conditional statements, loops, input/output Functions: Parameters, return values, recursion, lambda, default arguments Code Reusability: Aggregation, Inheritance (Single, Multiple, Multilevel, Hierarchical, Hybrid), Polymorphism, Method Overriding, Method Chaining, super(), and __init__ constructor OOPs Concepts: Abstraction, Encapsulation, Access Specifiers, Dunder/Magic Methods, Operator Overloading, MRO (Method Resolution Order) Comprehensions: List, Set, and Dictionary Iterators & Generators: iter(), next(), yield, custom iterators, difference between return vs yield Singleton Class & Decorators: Understanding instance control and design reusability Exception Handling: Default, Generic, Nested, try/except/else/finally, raise, user-defined exceptions, assert File Handling: Read, Write, Append, Exclusive (x), and combined (+) modes JSON Handling: dump, dumps, load, loads, data serialization Pickling & Unpickling: Data encryption/decryption concepts SQL Integration with Python: CRUD operations, connecting Python with SQLite3, saving and fetching records Regular Expressions: Email validation and pattern matching Multi-threading: Real-time examples (like cooking and cleaning analogy) using Thread, start(), join(), and synchronization Project Practice: 🗂️ To-Do List Application (SQLite3-based) – Implementing CRUD operations and database persistence 🎓 Student Management Example – Using Aggregation, Inheritance, and DB storage + PDF conversion 💻 Real-time practice programs for recursion, abstraction, polymorphism, file handling, and threading 🧩 Learning Outcome: This journey enhanced my understanding of Python’s backend capabilities, database integration, data handling, and OOP design patterns — shaping me into a more structured and efficient Python Developer. I’m also sharing my practice programs (basic → advanced) as part of this learning journey, which include hands-on code implementations and real-time problem-solving exercises. 🔖 #Python #BackendDeveloper #PythonDeveloper #SoftwareDeveloper #CodeReusability #ObjectOrientedProgramming #ProgrammingJourney #LearningInPublic #DevelopersCommunity #SQL #SQLite #JSON #FileHandling #ExceptionHandling #Iterators #Generators #Comprehension #Abstraction #Encapsulation #Polymorphism #Inheritance #MultiThreading #CodingPractice #TechLearning #ContinuousLearning #OpenToWork #DeveloperGrowth #PythonProjects #ToDoListApp

# Python – Understanding Dictionaries in Python # Definition: A Dictionary in Python is an unordered, mutable, and indexed collection of key-value pairs. It allows you to store data in a structured form, where each value is associated with a unique key. # Example: student = {"name": "Balaji", "age": 22, "course": "Python"} print(student["name"]) # Output: Balaji # Characteristics: - Data stored as key-value pairs - Keys must be unique and immutable (strings, numbers, tuples) - Values can be of any data type - Dictionaries are mutable (can add, update, or delete entries) - Unordered (insertion order preserved in Python 3.7+) - Fast for lookups, insertions, and deletions # Advantages: - Provides efficient key-based data access - Helps represent real-world data (like JSON) - Supports dynamic data manipulation - Ideal for structured, relational, and configuration data # Commonly Used Methods: clear() → Removes all items copy() → Returns a shallow copy get(key) → Returns value of key, None if not found items() → Returns all key-value pairs keys() → Returns all keys values() → Returns all values pop(key) → Removes item with given key popitem() → Removes last inserted key-value pair update() → Updates dictionary with another dictionary or iterable setdefault() → Returns value of key, inserts it if not present fromkeys() → Creates dictionary from given keys with default value # Common Functions: len() → Returns number of items str() → Returns string representation type() → Returns dictionary type sorted() → Returns sorted list of keys any(), all() → Checks truth value of keys # Example Problem: student = {"name": "Balaji", "age": 22, "branch": "CSE"} student["college"] = "JNTUA" # Add student["age"] = 23 # Update print(student.get("branch")) # Access student.pop("college") # Remove print(student) # LeetCode Problems: 1. First Unique Character in string - 387 2. Majority Element - 167 3. Number of good pairs - 1512 4. Roman to Integer - 13 # Summary: Dictionaries are one of Python’s most powerful and versatile data structures. They allow efficient data mapping, retrieval, and manipulation — making them essential for real-world applications like APIs, JSON handling, and data analysis. LogicWhile #Python #Dictionaries #PythonProgramming #ProblemSolving #DataStructures #CodingJourney #LearnToCode #PythonForBeginners #LeetCode #100DaysOfCode #CodingCommunity #LogicBuilding #Developer #SoftwareEngineering #PythonLearning #CodeNewbie #ProgrammingBasics #TechLearning #CodingPractice #BuildInPublic #StudyWithMe #PythonDeveloper #CodeDaily #TechEducation #CodingLife #LearningInPublic

Python Functions!⚙️ Functions help us write reusable and neat code like loops. They are one of the core concepts of python and therefore, today we are going to review what I learned about python functions so far! 👇 👉Basics of functions: grouping statements to perform specific tasks, avoiding repetition, and understanding how to define and call them. ⚙️Parameters & arguments: passing values to functions, using default values, and learning the difference between parameters and arguments. ⚙️Return vs print: how return sends a value back to the caller and print just displays output, and why functions without return give None. ⚙️Recursion: functions calling themselves, importance of base cases, factorial calculation, and printing sequences both forwards and backwards using recursion. 💪Mini exercises: I have added simple and beginner friendly beginner exercises in my jupyter notebook for python functions! Dropping link down below! 😊 😊Good News: My Jupyter Notebook for Python Functions Is Up On My Github! Make sure to check it out and try to do mini projects for python functions that i made there! Good Luck Python Beginners! 😊What Is Coming Next?: Python Lists In detail for Beginners like me! --------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- #pythonfunctions #functionsinpython #pythonforbeginnners #pythonprogramming #pythonfordatascience #pythonformachinelearning

 💠 Python Control Flow & Conditional Statements :- 💠 What is Control Flow? ➜ Control flow in Python decides the order in which statements are executed based on conditions and loops. • Purpose / Use :- ➢ To make decisions in code ➢ To repeat tasks automatically (loops) ➢ To control program execution dynamically 🔹 if Statement :- ➜ Executes a block of code only if a condition is True. Example :- a = 10 if a > 5: print("a is greater than 5") Output :- a is greater than 5 🔸 if–else Statement :- ➜ Executes one block if the condition is True, otherwise executes the else block. Example :- num = int(input("Enter a number: ")) if num % 2 == 0: print("Even number") else: print("Odd number") Input: 7 Output :- Odd number 🔹 elif Ladder :- ➜ Used to check multiple conditions one after another. If one condition is True, the rest are skipped. Example :- marks = 82 if marks >= 90: print("Excellent") elif marks >= 75: print("Good") elif marks >= 60: print("Average") else: print("Needs Improvement") Output :- Good ➥ Use :- When you need to test several conditions in a sequence. 🔸 Nested if :- ➜ You can use an if statement inside another if. It helps in checking multiple layers of conditions. Example :- x = 10 if x > 5: if x < 15: print("x is between 5 and 15") Output :- x is between 5 and 15 ➥ Use :- When decision-making depends on another condition. 🔹 for Loop :- ➜ Used to repeat a block of code for each item in a sequence (like list, string, or range). Example :- for i in range(5): print(i) Output :- 0 1 2 3 4 ➥ Use :- Best for iterating through lists, tuples, strings, etc. ➢ Example with List :- fruits = ["apple", "banana", "cherry"] for f in fruits: print(f) Output :- apple banana cherry 🔸while Loop :- ➜ Executes a block while the condition is True. Used when you don’t know beforehand how many times the loop should run. Example :- count = 1 while count <= 5: print(count) count += 1 Output :- 1 2 3 4 5 ➥Use :- When looping depends on a condition that changes dynamically. 🔹Loop Control Statements :- Keyword Description break ⟶ Stops the loop immediately continue ⟶ Skips current iteration & moves to next pass ⟶ Placeholder — does nothing (used in empty loops or blocks) Example :- for i in range(5): if i == 3: continue print(i) Output :- 0 1 2 4 🧩 Quick Summary :- if ➜ check a condition if-else ➜ choose between two paths elif ➜ check multiple conditions nested if ➜ layered decisions for ➜ fixed iterations while ➜ condition-based iterations #Python #ControlFlow #IfElse #Loops #ProgrammingBasics #LogicBuilding #Developers #LearnPython #CodeNewbie #PythonLearning

✨ Ever wondered what happens behind the scenes when you run your Python code? 👀 Python Interpreter 🐍 Before you get into the real work of writing programs with the Python programming language, it's important to understand how the Python Interpreter works. A Python Interpreter is a program that reads and executes Python code. Think of it as a translator that tells the computer: “𝘏𝘦𝘺, 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘸𝘩𝘢𝘵 𝘸𝘦’𝘷𝘦 𝘣𝘦𝘦𝘯 𝘢𝘴𝘬𝘦𝘥 𝘵𝘰 𝘥𝘰.” 😅 The main job of the interpreter is to read and execute your Python code. Depending on your environment (setup), you might start the interpreter by clicking an icon or by typing 𝚙𝚢𝚝𝚑𝚘𝚗 in your command line. When it starts, you’ll see something like this: 𝙿𝚢𝚝𝚑𝚘𝚗 𝟹．𝟺．𝟶 (𝚍𝚎𝚏𝚊𝚞𝚕𝚝, 𝙹𝚞𝚗 𝟷𝟿 𝟸𝟶𝟷𝟻, 𝟷𝟺:𝟸𝟶:𝟸𝟷) [𝙶𝙲𝙲 𝟺．𝟾．𝟸] 𝚘𝚗 𝙻𝚒𝚗𝚞𝚡 𝚃𝚢𝚙𝚎 "𝚑𝚎𝚕𝚙", "𝚌𝚘𝚙𝚢𝚛𝚒𝚐𝚑𝚝", "𝚌𝚛𝚎𝚍𝚒𝚝𝚜" 𝚘𝚛 "𝚕𝚒𝚌𝚎𝚗𝚜𝚎" 𝚏𝚘𝚛 𝚖𝚘𝚛𝚎 𝚒𝚗𝚏𝚘𝚛𝚖𝚊𝚝𝚒𝚘𝚗． >>> Here’s what it means 👇🏽 ✨ The first few lines contain information about the interpreter and the operating system it’s running on. ✨ The version number (3.4.0) shows you’re using Python 3, if it begins with a 2, that means Python 2. ✨ The last line (>>>) is a prompt that indicates the interpreter is ready for you to enter code. For example, one of the first commands I ever typed in my interpreter was this simple calculation 👇🏽 >>> 1 + 1 2 You’ll get an instant result. That was my “aha” moment 😅, realizing that even the simplest command was a conversation between me and my computer through Python. If you’re using Windows, the same concept applies, but your display might look a bit different, and it may even show the bit your Python interpreter is running on (e.g., 64-bit). But once you see the >>> prompt, you’re good to go! 😎 So next time you see that display on your command line, you’ll know exactly what’s going on! 😎 Think of the interpreter just like a messenger delivering a package (your code) to the computer, so it can open it, understand the instructions, and take action. I hope this helps you understand it better 😊💻 💬 What was the first thing you ever typed or tried when you started learning Python? 🐍 #Python #LearningJourney #DataAnalytics #CodingJourney #KeepBuilding

🐍✨ Exploring Python’s Core Data Structures: Lists, Tuples, Dictionaries & Sets Data structures are the backbone of programming — they help us store, organize, and manage data efficiently. In Python, we have four powerful built-in structures that make working with data simple and intuitive. ✅ List — Managing Dynamic Collections, Dynamic, ordered collection 💠 A list is a built-in data structure in Python that is used to store multiple items in a single variable. 💠 It is an ordered and mutable (changeable) collection. ❔ Why a list? 1.The order in which tasks were added matters (today’s tasks in sequence) 2.Users will add, reorder, remove tasks constantly — so mutability is needed 3.Duplicates may appear (e.g., “Call John” might recur) ✅ Tuple – Immutable, ordered collection 💠 A tuple is a built-in data structure in Python used to store multiple items in a single variable. 💠 It is ordered and immutable, meaning you cannot change, add, or remove items once the tuple is created. Tuples are performed only length and count methods. ❔ Why a tuple? 1. The two values ) form a fixed record — once set, you 2. don’t want accidental updates 3. It’s ordered 4. It’s efficient and signals “this is a fixed-value pair” ✅ Dictionary – Key-value mapping, structured data 💠 A dictionary in Python is a collection of key-value pairs used to store data in an organized and easily accessible way. 💠 Each key in a dictionary is unique, and it’s used to access its corresponding value. 💠 A dictionary is a mutable, unordered data structure that stores data in the form of key: value pairs. ❔ Why a dictionary? 1. Each piece of information has a label (key) and a value → easy to access 2. The structure easily mirrors JSON objects returned by APIs 3. It’s mutable: you can update the profile (e.g., add a new skill) ✅ Set – Unordered collection of unique items 💠 A set in Python is a collection of unique, unordered elements. It is mainly used when you want to store non-duplicate items and perform mathematical 💠 set operations such as union, intersection, and difference. 💠 A set is an unordered, mutable data structure that does not allow duplicate values. ❔ Why a set? 1. Automatically removes duplicates (important for unique subscriber lists) 2. Supports operations like union, intersection, difference which are useful in analytics (e.g., “who signed up this week but wasn’t already subscribed?”) 3. Order doesn’t matter for uniqueness scenarios 📖 Read the full blog here: 🔗https: //https://lnkd.in/gcH6H_6D Big thanks to Vishwanath Nyathani,Kanav Bansal,Raghu Ram Aduri, @ Naman Goswami,Harsha M., for guiding me throughout this journey. Special Thanks to Innomatics Research Labs for providing the perfect environment. #DataScientist #Python #Programming #DataTypes #PythonForBeginners #LearningJourney #DataScience #CodingCommunity #LinkedInBlog

💪Python DSAs: Linked Lists 🤓Since from today i have started Python DSAs, my first tutorial was about Linked Lists. 👉Linked lists use nodes linked together with pointers and contain data 👉We can play around with the pointers of nodes and perform node insertion and deletion ops on linked lists easily 👀 It is crucial for to understand how nodes and pointers perform in linked lists before we dig deeper into the concept 💭I have to say today was a much productive day since im started with DSAs now, i hope i will be exploring actual field of DS quite sooon! 🫡Until my next post! Happy Coding Fellow Pythonists! ‼️My Python DSAs Repo is up on my GitHub! Check it out for curiousity purposes! -------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK 📁Python DSAs: https://lnkd.in/ebR3rjkt ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗 GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #pythondatastructures #pythonalgorithms #pythonlinkedlists #pythonforbeginners #pythonprogramming

Python OOP: Message Passing ✉️ 🤔What is message passing in python OOP? In Python message passing means an object calling a method on another object or in other words basically, sending a “message” to ask it to do something 👉 We can simply understand from the dog and cat examples below 👉 I used dog.bark and cat.meouw to indicate that a message must be passed to dog and cat object 👉 When we call p.call_dog(my_dog), the Person object “sends a message” to the Dog object, asking it to bark() 👉 dog.bark() executes inside the Dog object and same with the cat example ‼️Message Passing is a way for objects to communicate in OOP ------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK 📁Python DSAs: https://lnkd.in/ebR3rjkt ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗 GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #messagepassing #objectorientedprogramming #pythonoop #OOP #oopfundamentals #OOPinpython

🐍 String Manipulation Operations in Python: A Beginner’s Guide If you’re learning Python, mastering string manipulation is a must! Strings are everywhere, from user input to data processing, and knowing how to handle them makes your code cleaner and smarter 💡 Let’s explore the most common string operations in Python with examples 👇 🧩 1. Concatenation (Joining Strings) Use the + operator to join two or more strings. first_name = "Zahid" last_name = "Hameed" full_name = first_name + " " + last_name print(full_name) ✅ Output: Zahid Hameed 🔠 2. Changing Case You can easily change the case of your text using built-in methods. text = "hello python" print(text.upper()) print(text.lower()) print(text.title()) ✅ Output: HELLO PYTHON hello python Hello Python ✂️ 3. Slicing Strings Extract specific parts of a string using slicing. message = "Python Programming" print(message[0:6]) print(message[-11:]) ✅ Output: Python Programming 🔍 4. Finding and Replacing Find specific text or replace one word with another. sentence = "I love Java" new_sentence = sentence.replace("Java", "Python") print(new_sentence) ✅ Output: I love Python 📏 5. String Length Count the total number of characters in a string using len(). word = "Python" print(len(word)) ✅ Output: 6 💬 6. Splitting and Joining Split a string into words and join them back with a custom separator. data = "Python is fun" words = data.split() joined = "-".join(words) print(words) print(joined) ✅ Output: ['Python', 'is', 'fun'] Python-is-fun 💡 Pro Tip: Strings in Python are immutable — once created, they can’t be changed. Every operation returns a new string instead of modifying the original one. 🚀 Why Learn String Manipulation? String handling is essential for: ✅ Data cleaning ✅ Web scraping ✅ File handling ✅ Chatbots ✅ Text analytics 💬 Your Turn! Which string operation do you find most useful in Python? Share your thoughts in the comments 👇 #Python #LearnPython #PythonForBeginners #DataScience #ProgrammingTips #ZahidLearnsPython