Python Multiplication Table Generator with Loops and Input Handling

Python Basics Cheat Sheet – From Me print("Hello") -> Display output len(data) -> Get data length type(x) -> Check data type int(), str(), float() -> Type conversion for i in range(5): -> Loop iteration if x > 10: -> Conditional statement def function(): -> Define a function list.append(x) -> Add item to list list.remove(x) -> Remove item from list dict["key"] -> Access dictionary value import math -> Import library Currently learning Python fundamentals and creating simple cheat sheets to stay consistent. Still learning, but enjoying the process. #Python #Programming #LearningJourney #DataAnalytics #CareerGrowth

Day 2 of Learning Python Most people don’t fail in Python… They fail because they ignore the basics. Here are 4 things you MUST know 👇 1. Data Types Everything in Python has a type: int, float, str, bool 🎥 👉 https://lnkd.in/gDNAyz6E 2. Data Structures Store multiple values efficiently: ✔ List → ordered, changeable ✔ Tuple → ordered, fixed ✔ Set → unique values ✔ Dictionary → key-value pairs 🎥 👉 https://lnkd.in/gqWWihBJ 3. Indexing & Slicing Access data like a pro: list[0] → first element list[-1] → last element list[0:3] → slice 🎥 👉 https://lnkd.in/g7QVQFzK 4. Operators Perform actions: ➕ Addition ➖ Subtraction ✖ Multiplication ➗ Division 🤔 Logical , Comparison 🎥 👉https://lnkd.in/g_7gZcUZ 💡 Reality Check: You can’t become a Data Scientist just by watching tutorials… Just like you can’t become a cricketer 🏏 by watching IPL. 👉 You need practice. #Python #Coding #DataScience #MachineLearning #LearnToCode

🚀 Python Learning Journey – Day 26 Today, I learned about PDBC (Python Database Connectivity) and how Python interacts with databases. Here’s what I explored: ✅ What PDBC is and why it is used ✅ Connecting Python with a database ✅ Executing SQL queries using Python ✅ Performing operations like create, insert, update, delete ✅ Using cursor and connection objects This helped me understand how Python works with real-world data stored in databases. Step by step, moving towards building data-driven applications 💪 #Python #LearningJourney #Day26 #PDBC #Database #SQL #Coding #KeepLearning

🚀 Automated My Downloads Folder Using Python Today, I built a simple yet useful File Organizer script using Python that automatically sorts files into folders. We often download many files, and our Downloads folder becomes messy. So I created a script to organize it automatically 👇 ✨ What This Script Does • Scans all files in the Downloads folder • Moves .jpg files into an Images folder • Moves .pdf files into a PDFs folder • Helps keep files clean and organized ⚙️ Technologies Used • Python • os module (for file handling) • shutil module (for moving files) 🧠 What I Learned • Working with file systems in Python • Automating real-world tasks • Writing efficient and reusable scripts • Importance of automation in daily life 💡 Key Insight Even small automation scripts can save time and improve productivity. If you have suggestions to improve this script (like handling more file types), I’d love to hear them! 😊 #Python #Automation #Programming #LearningInPublic #DeveloperJourney #Productivity #10000Coders #BuildInPublic

🚀 Python Practice – Standard Library Modules Continuing my Python learning journey by exploring the powerful Standard Library 🐍 In this session, I worked with some commonly used modules: ✔️ array – handling collections of elements ✔️ math – mathematical operations ✔️ random – generating random values ✔️ os – file & directory operations ✔️ json – data conversion (dict ↔ JSON) ✔️ csv – reading and writing CSV files ✔️ datetime & time – working with date and time ✔️ re (Regular Expressions) – pattern matching in text Practiced using these modules to perform real-world tasks like file handling, data conversion, and basic data processing. Understanding the Standard Library is helping me write more efficient code without relying on external libraries 📊 A big thanks to Krish Naik for his amazing guidance and clear explanations 🙌 Documented all my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Learning how to use built-in tools to solve real problems step by step 💡 #Python #StandardLibrary #DataAnalytics #LearningJourney #Coding

I spent hours writing this by hand so you don't have to spend days googling 🐍✍️ Here's your complete Python Cheatsheet — from beginner to intermediate — all in ONE page: ✅ Variables & Data Types ✅ Lists, Tuples, Dicts & Sets ✅ Control Flow (if/for/while) ✅ Functions & Lambda ✅ List Comprehension ✅ Exception Handling ✅ OOP Basics ✅ File Handling ✅ 12+ Useful Built-ins Save this post 🔖 — you'll thank yourself later. If you're learning Python or brushing up before an interview, this is all you need to get started. Drop a "🐍" in the comments if this helped you! Follow me for more handwritten tech notes every week 👇 #Python #PythonProgramming #LearnPython #CodingTips #Programming #100DaysOfCode #TechTwitter #Developer #SoftwareEngineering #PythonCheatsheet #CodeNewbie #LinkedInLearning

🚀 Day 11 – File Handling in Python Today I learned how Python works with files — one of the most practical concepts in real-world applications. 🔹 Files help store data permanently 🔹 We can read, write, and update data anytime 📌 File Modes: • 'r' → Read • 'w' → Write (overwrites file) • 'a' → Append (adds data) • 'x' → Create new file 💡 Best Practice: Using with statement automatically closes the file. 📌 Example: with open("data.txt", "r") as file: content = file.read() print(content) 🔥 Key Learning: File handling is used in logs, reports, user data storage, and real-world applications. Ajay Miryala 10000 Coders #Python #FileHandling #CodingJourney #100DaysOfCode

🚨 Most developers use Python lists when they should be using NumPy arrays. I made the same mistake in my project. I was handling large data using lists everywhere. Everything worked… but performance was slow 🐢 Then I switched to NumPy. That’s when I saw the real difference ⚡ 👉 Lists store mixed data types (flexible but slower) 👉 NumPy arrays are faster and memory efficient 🚀 👉 Operations are vectorized (no manual loops needed) Example: List → you write loops → slower ⛔ NumPy → bulk operations → faster ✅ Lesson: If you are working with large data or heavy calculations, don’t rely only on lists. Use NumPy arrays. It will improve speed and efficiency. What do you use more in your projects — list or NumPy array? 🤔 #Python #NumPy #DataScience #MachineLearning #Programming #Coding #TechLearning #Developers

Starting my journey into databases with Python 🐍 One of the first things I’m learning is how to connect Python to a database and begin interacting with data using SQL. To make this easier, I’m using SQLite a simple and lightweight database alongside SQLAlchemy, which helps Python communicate with different types of databases. Here’s what I’ve learned so far: Import create engine from SQLAlchemy Create a database engine by specifying the database type and name. Use the engine to connect and interact with the database. Explore the database by retrieving table names using engine.table_names() It’s a small step, but an important foundation for querying and analyzing data. Small steps, big growth 🚀 #Python #SQL #DataEngineering #LearningJourney #TechGrowth

Humbled to share, seeking wishes and blessings 🙏 My book "Python - Powered Excel" is now released! This book offers a practical guide to Excel (not necessarily 365) and Python users to automate tedious tasks, analyze data, and create dynamic reports. Whether you are a data professional versed in Python or a seasoned Excel user, this book will arm you with tools to harness Python's potential and Excel’s convenience. Combining the strengths of both Python and Excel will allow you to build scalable workflows that are accessible to everyone, whether you start in Excel or Python. Book Link in comments! #Excel_python #Data #Book