Learn Python in 30 Days: Basics to Advanced Skills

🚀 𝗗𝗮𝘆 𝟮𝟭/𝟯𝟬 – 𝟯𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Continuing my journey of building one Python project every day to improve consistency and real-world problem-solving. Today’s focus: **Core Python Concepts** 🧠 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗧𝘆𝗽𝗶𝗻𝗴 𝗦𝗽𝗲𝗲𝗱 𝗧𝗲𝘀𝘁 Build a Python-based typing speed test that measures how fast and accurately a user can type a given sentence. A simple yet powerful project to understand timing, user input handling, and performance metrics. ✨ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • Random sentences loaded from external file 📄 • Real-time typing speed calculation (WPM) ⚡ • Error detection (word-based comparison) ❌ • Accuracy calculation (%) 🎯 • Continuous testing loop for practice 🔁 💡 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗨𝘀𝗲𝗱: • File handling in Python (`.txt` file) • Time module for performance tracking • Random module for sentence selection • String manipulation & comparison • Looping and control flow 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/d3MUYR2T A simple idea, but very useful and practical — and a great way to improve both coding and typing skills together. Building discipline through code — one project at a time. Follow along as I complete 30 Python projects in 30 days 🚀 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #PythonProjects #Coding #Automation #Learning

🗓️ DAY 5 — Python vs Excel: You’re Asking the Wrong Question I see this debate every week on LinkedIn. Here’s the honest answer: Use Excel when: → Your data is under 100K rows → You’re sharing with non-technical stakeholders → Speed matters more than scale Use Python when: → Your data is large or messy → You need to automate repetitive tasks → You’re building something repeatable Neither tool wins. The analyst who knows when to use which — wins.

Day 78 of my Python Journey 🐍(week 11) Taking a moment to reflect on everything I’ve learned so far in my journey from beginner to now: • Basics of Python (variables, data types, type casting) • User input and string handling (slicing, methods) • Conditional statements and operators • Loops (for, while, break, continue) • Functions (built-in, user-defined, arguments, recursion) • Data structures (lists, tuples, sets, dictionaries) • List methods, set methods, dictionary methods • F-strings, docstrings, enumerate • Exception handling, custom errors, finally • Modules (import, from, as, dir) • File I/O (read, write, modes, with statement) • Map, filter, reduce, lambda functions • is vs == • Introduction to OOP • Classes and objects • Constructors • Instance vs class variables • Class methods & static methods • Getters and setters • Inheritance & access specifiers • Decorators • dict, help(), super() Still a long way to go, but consistent effort is building strong fundamentals every day. #Python #CodingJourney #LearnInPublic #Day78 #Consistency

Day 03 of my #30DaysOfPython journey taught me something important: the basics are where the real control begins. 🐍 Today I explored a few core concepts that make Python way easier to understand: 🔹 Casting Converting one data type into another using int(), float(), str(), list(), and set(). This one matters a lot because data does not always come in the format you need. 🔹 Number data types Python has 3 main number types: Integer → negative, zero, positive Float → decimal numbers Complex → numbers with real and imaginary parts 🔹 Boolean data types Booleans only have two values: True and False. Simple, but super powerful when you start making decisions in code. 🔹 Operators 1. Assignment → used to assign values to variables 2. Arithmetic → +, -, *, /, % (remainder) , **, // (floor division) 3. Comparison → used to compare values and return True or False 4. Logical → and, or, not One thing that stood out to me today: these are not just “basic topics” — they are the tools that make programs actually behave the way we want. The more I learn Python, the more I realize that small concepts stack up into something powerful. Github Link - https://lnkd.in/gk_fDscP What topic clicked for you only after seeing it in real code? #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

I did not expect a Python topic about “unique items” to feel this useful… but sets changed that fast. 🐍 Day 7 of my #30DaysOfPython journey was all about sets, and this one felt different because it was less about storing data and more about controlling it. A set is an unordered collection of distinct items. It cannot hold duplicates, which makes it super handy in real-world coding. Today I explored: 1. Creating sets with set() built-in function and {} 2. Checking length with len() 3. Using in to check if an item exists 4. Adding items with add() to add a single item and update() for multiple items 5. Removing items with remove() (raise error if item not present), discard() (does not raise error), and pop() (removes a random item) 6. Clearing a set with clear() 7. Deleting a set with del 8. Converting a list to a set to remove duplicates 9. Set operations like union(), intersection(), difference(), and symmetric_difference() 10. Checking issubset(), issuperset(), and isdisjoint() What made sets interesting to me today was how practical they are when you want uniqueness, comparison, or clean data without duplicates. They may look simple on the surface, but they solve a very specific kind of problem really well. Which Python data type has surprised you the most so far: lists, tuples, or sets? Github Link - https://lnkd.in/eJfTX-HQ #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

Today’s Python lesson made the whole language feel more connected. 🐍 Day 12 of my #30DaysOfPython journey was all about modules, and this one felt like learning how Python organizes its tools behind the scenes. A module is basically a file that contains code, functions, or variables that you can reuse in another file. Instead of writing everything from scratch, you can create something once and bring it into your main program whenever needed. Today I explored: 1. What modules are and why they matter 2. Creating a separate file and importing it into another file 3. Importing only specific parts instead of the whole file 4. Renaming something while importing it 5. Built-in modules like os, statistics, math, string, and random What stood out to me today was how modules make Python feel less like a single script and more like a system of connected pieces. That shift matters because it is what makes code easier to reuse, organize, and scale. One more day, one more topic, one more step toward writing code that is cleaner, smarter, and more modular. Which felt more useful to you first: creating your own module or using built-in ones like math and os? Github Link - https://lnkd.in/gVPWQWiS #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

📅 Day 5 of Python — and today was all about putting knowledge to the test! 💪 Instead of learning something new, I took on a full practice session covering everything I've studied so far on Python's core data structures. 🧪 Here's what I worked through: ✅ Lists — creating, slicing, methods like append(), extend(), insert(), pop(), remove(), and sort() ✅ List Comprehensions — squares, filters, tuple pairs, and more ✅ Tuples — declaration, immutability (yes, I triggered the TypeError 😅), unpacking, and zip() ✅ Sets — deduplication, membership checks, add/remove/discard, and set operations like union, intersection, difference & symmetric difference ✅ Dictionaries — key-value access, get(), items(), keys(), values(), nested dicts, and updating/deleting entries ✅ Dictionary Comprehensions — building mappings with filters ✅ Applied Problems — frequency maps, common elements using sets, zip() with conditional logic The practice set had 30+ exercises and solving each one back-to-back really helped solidify the concepts rather than just reading about them. Key takeaway from today: You don't truly understand a concept until you've broken it, debugged it, and fixed it yourself. 🔧 On to Day 6! 🚀 #Python #100DaysOfCode #DataStructures #LearningInPublic #CodingJourney #PythonProgramming

Today’s Python lesson felt like the moment code stopped being just instructions and started feeling reusable, flexible, and kind of smart. 🐍 Day 14 of my #30DaysOfPython journey was all about higher order functions, and this one made Python feel a lot more powerful. In Python, functions are first-class citizens, which means they can: 1. take other functions as parameters 2. return functions as results 3. be modified 4. be assigned to variables Today I also explored: 1. Functions as parameters 2. Functions as return values 3. Closures — where an inner function can use the outer function’s scope 4. Decorators — a clean way to add extra behavior without changing the original function 5. Built-in higher order functions like: i. map() → transforms items ii. filter() → keeps only matching items iii. reduce() → combines items into one value What stood out to me today was how Python lets functions do more than one job. They are not just blocks of code anymore — they can actually shape how other code behaves. One more day, one more topic, one more step toward thinking in cleaner, more reusable logic. Which one feels the most interesting to you right now: map(), filter(), reduce(), or decorators? Github Link - https://lnkd.in/gc-mj8Qi #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

🚀 Day 8/30 of My LeetCode Journey (Python + SQL) Staying consistent and leveling up every day! 💻🔥 🔹 **SQL Problem of the Day** 👉 *Find Customer Referee* Given a `Customer` table, write a query to find the names of customers who are either: • Not referred by customer with id = 2 • OR not referred by anyone 💡 *Key Concept:* Filtering with conditions (`!= 2` OR `IS NULL`). 🔹 **Python Problem of the Day** 👉 *Merge Sorted Array* Given two sorted arrays, merge them into a single sorted array in-place without returning a new array. 💡 *Key Concept:* Two-pointer approach from the end for efficient in-place merging. Every problem is helping me think more efficiently and write better code ⚡ Day 8 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #Learning

environment maintenance isn't "extra" work—it's the work. If you aren't keeping your dependencies updated, you aren't building a product; you're building a ticking time bomb for the next dev who touches it. HERE IS WHY I SAY THAT I was plugging in this spects frame measurement tool, and the client was like, "The code is proven, just drop it in." Wrong. The second I opened the hood, I hit straight-up Dependency Hell. This "proven" code was a total time capsule—ancient versions of MediaPipe, NumPy, and OpenCV that were basically at war with my modern Python setup. I had three choices: Downgrade the entire codebase and live in the past. Rebuild the core logic from scratch. Drag the codebase into the modern stack. -I've dragged the codebase back into the modern stack let me know what would you do if you had such a situation #softwareEngineering #Python #fundamentals