Learn Python Fast with this Ultimate Cheatsheet

Every great coder starts with one thing — strong fundamentals. Before building complex projects or learning advanced topics, you need to truly understand how code thinks. Start with the essentials: 🔹 Variables — how your code stores data 🔹 Operators — how logic works behind the scenes 🔹 Strings — how Python handles text 🔹 Control Flow — how decisions are made in your program I’ve created a complete Python playlist (till Control Flow) that walks you through these step-by-step — simple, practical, and beginner-friendly. If you’re just starting your Python journey, this is where you build your foundation. 💪 🎥 Watch the full playlist here → https://lnkd.in/g4gbKJEf 👉 Like to show your support. 🔁 Repost to share with your network. 👥 Follow Shilpa Das to get such more like this. #Python #ProgrammingBasics #ControlFlow #PythonTutorial #DataEngineering #Topmate

Ever feel like your Python code just refuses to cooperate when things get heavy? Imagine you’ve crafted the perfect string matching algorithm. It works fine with small data, but when you scale up, everything slows to a crawl. Frustrating, right? That’s when I remembered an old trick: offloading critical parts to C. Turns out, a little C integration can make your Python programs run up to 150 times faster. I’ve tried subprocess calls, ctypes, and even full C extensions, and the speed gains are staggering. It’s like giving your code a turbo boost without abandoning Python’s simplicity. The key takeaway? 🧩 Use subprocess for quick wins—compile C code to executable and call from Python. 🚀 Load your C functions directly into Python with ctypes for seamless speed. 🎯 For the fastest performance, write Python C extensions to get native-like speed. When your code hits a wall, consider whether a C shortcut can get you across faster. Are we just coding in Python, or starting to think like data scientists with a performance edge? What’s your favorite way to speed up Python? #DataScience #PythonTips #Performance #CodingHacks

🧩 6-Day Python Basics Challenge – Day 2 Completed 🎯 Today’s focus was on String Operations — exploring how Python handles text-based data. I practiced and wrote each program in both normal and function-based methods! 💻 Here’s what I covered today: 1️⃣ Count vowels in a string 2️⃣ Reverse a string 3️⃣ Check if a string is a palindrome 4️⃣ Count the number of words in a sentence 5️⃣ Find the frequency of each character 6️⃣ Replace vowels with “*” 7️⃣ Convert uppercase → lowercase and vice versa ✨ Each program helped me improve my logic, string handling, and understanding of Python’s in-built methods. 🚀 Two days down, four more to go — step by step towards stronger fundamentals! 💻 Explore my complete notes & code here: 🔗 https://lnkd.in/guPs2bfs #PythonBasics #LearningJourney #DataAnalytics

Understanding np.select(): A clean way to handle multiple conditions The numpy.select() function in Python's NumPy library provides a way to create a new array by selecting elements from a choicelist based on a list of conditions. It is particularly useful for implementing complex conditional logic on arrays in a vectorized manner, avoiding explicit loops. How it works: np.select() iterates through the condlist and choicelist simultaneously. For each element position in the array, it checks the conditions in condlist in order. The first condition that evaluates to True for that element position determines which value from choicelist will be placed in the output array at that position. If none of the conditions are True for an element, the default value is used. Why it matters: Helps apply many conditional rules at once Keeps your data transformations organized and readable Works faster than writing manual loops or nested conditions

🚀 10 Python Libraries You’ve Never Heard Of — But Will Change Your Life! 🐍✨ Most developers use Pandas, NumPy & Requests… but Python hides some insanely powerful gems that can 10x your productivity. From Rich (beautiful terminals) to Polars (super-fast DataFrames) and MoviePy (video editing in Python!) — these libraries will upgrade your entire workflow. If you want to level up your Python skills in 2025, this is your sign. 💡🔥 Medium - https://lnkd.in/dz5xy_KW Google Blogs - https://lnkd.in/dDVujqN3 Personal Site - https://lnkd.in/dF6xifBW Medium - https://lnkd.in/dz5xy_KW #Python #Developers #Coding #Programming #PythonLibraries #Productivity #TechCommunity #DataScience #Automation #MachineLearning #SoftwareEngineering

We all love writing documentation 📚, and keeping it up-do-date, don't we 😅?! Well, I have a quick tip to make your life a little easier at least: In Python, __doc__ is a special attribute that holds the docstring of an object. A docstring is a string that provides documentation for a module, class, function, or method, basically a brief description of what the object does. 💡 If you add a docstring to your Python file that defines your Dag, you can re-use it in the doc_md parameter of your Dag by passing __doc__ to it. 🧑💻 When you click on "Dag Docs" within the Dag view, it shows the rendered Markdown documentation right in the Airflow UI. With this approach, you have the documentation next to the code. Your fellow data engineers directly see the purpose of a Dag when opening the Python file, BUT ALSO, it's shown to any Airflow users directly in the UI, without having to maintain separate documentation versions and keeping it very visible next to the implementation, so you don't forget to update it. 🎯 One source of truth = less headache #DataEngineering #Python #Airflow #Documentation

🚀 Excel just got a whole lot smarter — you can now run Python code directly inside Excel! No add-ins. No setup. Just type your Python code right into the formula bar and watch the magic happen. In my latest video, I walk you through: ✅ What a DataFrame is (and why it’s powerful) ✅ How to load Excel data into Python ✅ Summarize data in seconds using describe() ✅ Group by city and calculate totals instantly Create a chart in Python — and view it inside Excel This update bridges the gap between Excel and Data Science, making advanced analytics accessible to everyone. 💬 How do you see yourself using Python in Excel? Watch now -: https://lnkd.in/dekT2Z4S Drop your thoughts below 👇 Join My Data Academy for more Excel Hacks 🔗 www.excelsirji.com #PythonInExcel #ExcelTips #DataScience #Microsoft365 #Productivity #ExcelUpdate #ExcelCommunity

🔥 This one word in Python saves you from hundreds of bugs. 🚀 Inside the Backend — Episode 4: Context Managers in Python — Why with Matters Every time you write with open('file.txt') as f:, something magical happens — Python quietly takes responsibility for you. 🧹 It opens the resource, lets you do your work, and cleans up automatically, even if your code crashes. That tiny with keyword embodies the best of Python: clarity, safety, and elegance. 💡 What It Does A context manager ensures that whatever you open — a file, database connection, or network session — is always closed or released properly. No memory leaks. No dangling connections. No forgotten cleanups. ⚙️ ⚡ Why It Matters Without with, you’d write extra try-finally code to handle errors and cleanup. With it, you write one line — and Python handles the lifecycle perfectly. It’s simplicity that scales — and reliability that saves your time (and sanity). 💫 🧠 Pro Tip Whenever your code starts something that must eventually stop — think context manager. It’s one of those small details that separates clean backend systems from messy ones. 🐍 Next Episode → REST API Best Practices: Designing Endpoints That Scale 🌍 We’ll explore how smart endpoint design makes your APIs intuitive, maintainable, and ready for growth. #Python #BackendDevelopment #SoftwareEngineering #BuildInPublic #CleanCode #ContextManagers #BackendEngineer #SystemDesign #PythonTips

Come hear about various automations I use to alleviate python package maintenance. It'll cover pre-commit, GitHub Actions, and publishing to PyPI in the context of a package I've been developing called `frame-search`. Check it out if you are interested in sharing your own python project on PyPI for the first time or want to pick up some techniques I've found to focus more time on development! Link is below 👇

“Don’t make these Python mistakes! 🚫🐍 Fix them now & code smarter 💻 #PythonTips #CodingShorts”Are you new to Python? 🐍 Avoid these 3 beginner mistakes that almost every coder makes when starting out! In this short video, you’ll learn: ✅ The difference between = and == ✅ Why indentation matters in Python ✅ How to handle strings and numbers correctly These simple tips will save you hours of debugging and help you write clean, professional Python code. 💡 Watch till the end for the bonus tip to level up your Python skills! 👇 Timestamps: 00:00 - Intro 00:06 - Mistake #1: == vs = 00:16 - Mistake #2: Indentation Errors 00:26 - Mistake #3: Mixing Data Types 00:36 - Outro & Bonus Tip 📚 More Python Shorts: [Add your playlist link here] 🎓 Subscribe for daily Python tips and coding tutorials! #PythonTips #PythonBeginners #LearnPython #CodingShorts #PythonMistakes #CodeBetter #Programming #PythonCode #CodingForBeginners #PythonLearning #PythonTutorial