"Day 36: Asyncio + Aiohttp for Efficient Python Coding"

🔥 Day 35 — Effective Python Coding Series Today’s focus: Mastering Asyncio — The Heart of Asynchronous Python ⚙️ asyncio is a powerful library in Python that allows developers to write asynchronous code — perfect for applications that handle many I/O-bound operations like API calls, file reads, or database requests. 🌐 ✨ Why Asyncio Matters: When your program performs I/O tasks, it often spends most of its time waiting for responses. Instead of idling, asyncio lets your code switch to another task, efficiently utilizing resources and improving performance. ⚡️ ✨ How It Works: ✔️ Event Loop — The core engine that manages and schedules async tasks. ✔️ Coroutines (async def) — Functions that can pause and resume during execution. ✔️ await keyword — Used to pause execution until an async task finishes. ✔️ Task Switching — When one task waits, another gets CPU time — no blocking. ⚡️ Key Benefits: ✅ Perfect for I/O-bound applications (networking, APIs, file I/O) ✅ Keeps your application responsive ✅ Enables concurrency in a single-threaded program ⚠️ Remember: asyncio improves concurrency — not parallelism. For CPU-heavy work, you still need multiprocessing. In short — Asyncio = concurrency + responsiveness + efficiency 🚀 👉 This series is for Python Developers, Data Engineers, Backend Engineers, and ML Practitioners who want to master efficient and modern Python coding patterns. If this post helped you learn something new today, drop a ❤️ or 🔁 and stay tuned for more Effective Python Coding insights! #Python #Asyncio #AsynchronousProgramming #EffectivePython #CodingSeries #BackendDevelopment #DataEngineering #Developers #ML

They told you, "Learn Python." They lied to your face. You spent six months grinding LeetCode. Your portfolio is full of elegant, complex scripts. Still zero meaningful interview calls. Why? Because you missed the silent truth of corporate power. The truth your coding bootcamp conveniently left out. 🐍 Python is the Shiny Trophy. 🔑 SQL is the Key to the Vault. A Data Manager doesn't care about your algorithm elegance. They care if you can immediately access, validate, and present the $10 million revenue number they need right now. SQL is not just a language. It is the language of immediate access. It is the filter. If you can't join two tables and pull a clean report in five minutes, Python’s theoretical magic is worthless to the hiring committee. They hire the person who can plug the business gap today. That person uses SQL. Every single time. 💡 The Career Ladder Reality Stop chasing the flashy code. Start commanding the data. SQL gets you hired. Python gets you promoted. Is your current job-search portfolio truly SQL-heavy, or are you still relying on Python's promise? What is the one skill you wish you learned first? Share your thoughts below!

You can know Python. You can know Flask. You can even know databases, REST, and Docker... And still not land a backend job. Here’s the hard truth no tutorial tells you 🧵 Most developers confuse consuming information with gaining experience. But recruiters don’t hire what you know — they hire what you’ve done. You can’t “tutorial” your way into a real job. You need projects. Knowing what an API is is not the same as building one that scales Knowing what authentication means is not the same as implementing JWT securely Knowing SQL is not the same as designing schemas that actually work You dont learn those from videos. U learn them by shipping code Why most never make it past this stage Because building from scratch is hard. It’s confusing. It’s messy. You’ll fail a lot. But that’s exactly what prepares you for real backend work. The solution - Build one project a day. - Even small ones. - Each one teaches you something new: databases, routes, errors, and deployment. Do this for 30 days, and you’ll feel the shift. From “learner” → “engineer.” That’s why we built the Python30 Challenge ✅ 30 projects in 30 days ✅ DSA interview prep ✅ Job-ready backend toolkit ✅ Access to resume, mock interviews & community Stop studying like a beginner. Start shipping like a backend engineer https://lnkd.in/dEsaR2bN

State of Python 2025: Web Development Makes a Comeback! The latest Python Developers Survey, a collaboration between the Python Software Foundation and  JetBrains, has captured insights from over 30,000 developers worldwide — revealing some surprising  shifts in how Python is being used in 2025. Key Highlights: 50% of Python devs have less than 2 years of professional coding experience — showing Python’s unmatched accessibility for newcomers. Data science remains dominant, with 51% using Python for data exploration & processing. Web development is back! Usage jumped from 42% in 2023 to 46% in 2025, fueled by frameworks  like FastAPI, now adopted by 38% of developers. Outdated Python versions are costing businesses millions — upgrading could boost performance by up to 42%. Rust is now powering up Python: nearly 1/3 of new native code on PyPI uses Rust for speed and efficiency.  What’s next for Python: Free-threaded Python (v3.14) is coming — removing the GIL and unlocking true parallel processing. AI coding assistants are going mainstream — 49% of devs plan to use them soon.  Native mobile apps with Python are becoming a reality, with official iOS and Android support in the works! “Python’s future is being written by a new generation — one that’s curious, bold, and ready to take Python everywhere.” — Michael Kennedy, Python Software Foundation Fellow From AI and data to web and mobile, Python continues to evolve — proving once again why it remains the heartbeat of modern development. #Python #WebDevelopment #DataScience #AI #Developers #Programming #JetBrains #PythonSoftwareFoundation #Rust #Technology #Innovation #Coding

You can know every Python keyword, pass every online quiz, and still fail backend interviews. Why? Because companies don’t hire Python coders, they hire backend engineers. Here’s what separates them 🧵 Let’s be honest — Python is easy to start, but hard to master in the backend world. Anyone can write: ``` print("Hello World") ``` But not everyone can design an API that handles 100k users without breaking. That’s the gap. A backend engineer is part developer, part architect, part problem solver. You don’t just build endpoints — you build systems that: - Handle scale - Manage security - Stay online under stress - Integrate with other services That’s what real backend jobs demand. Here’s the uncomfortable truth: You can’t learn that from coding tutorials alone. They teach you the “what.” But backend engineering is about the “how” and “why.” Examples: - How does data flow through your app? - Why did you choose REST over GraphQL? - Why use PostgreSQL instead of MongoDB? Those are interview questions — and real-world ones. To think like a backend engineer, u must understand - Databases: schemas, transactions, performance - APIs: authentication, pagination, caching - Servers: concurrency, load balancing - DevOps: CI/CD, containerization, monitoring Each layer matters. Ignore one & ur system breaks You build an API that slows down under load. - A Python learner blames the language. - A backend engineer checks database indexing, caching, and query optimization, etc. See the mindset shift? It’s not about code — it’s about systems. That’s why backend engineering feels intimidating. It forces you to stop thinking in features and start thinking in flows — how every request travels through your system. It’s a new way of seeing code — and it changes everything. So if you’ve been learning Python and still feel stuck... You’re not broken — your approach is. You’ve mastered the language, but not the backend mindset. That’s the missing link between you and your first backend job. The good news? You can learn that mindset by building structured, real backend projects. Not toy projects, but real-world systems with APIs, databases, and deployments. That’s how you bridge the gap. That’s exactly what “Become A Python Backend Engineer” does. It takes you from writing scripts to designing real systems — the kind employers pay for. Stop coding. Start engineering in Python: https://lnkd.in/d5tahN8C

Python Dev Heads are Mentally Retarded I can use science to prove that Python software developers are in fact mentally retarded. You see, once you're "mentally challenged", the first thing others starts noticing about you, is that you make "bad decisions". Hence, your inability to make good decisions becomes a good metric to use when trying to measure your cognitive abilities. The more bad decisions you make basically, the larger the statistical probability of that you're a retard becomes ... Since roughly 80% of all software developers in the world reaches for Python "by default" once confronted with a software development problem, this implies all we need to do in order to classify these individuals as imbeciles, is to prove that Python is not the optimal tool for the job at hand. In this video I've got three examples; 1. CRUD read endpoint 2. Send email endpoint 3. Integrate with 3rd party HTTP API In all 3 examples Python produces on average 3 to 4 times as much code as Hyperlambda counting "tokens". Tokens again is a already used by LLMs to measure "cognitive complexity", and is therefore for all practical concerns a very good metric to use to also measure "human resource requirements" to solve some particular software development problem. Hence, if I need 1 week to do something in Hyperlambda, you'll need 3 to 4 weeks in Python, and 6 to 8 weeks in C# to implement a functionally similar solution. Notice, my references are in my video. Since Python seems to be consistently using about 300%+ as many tokens as Hyperlambda, and in addition literally needs roughly 500 to 700 percent the hardware requirements during runtime - It is therefore safe to claim the following ... "All Python software developers are mentally retarded, and should not be allowed to make decisions for obvious reasons" ... Hence, if you've got a Python software developer in your software development department, you should prevent him from being able to influence your tool choices in the future - At least until he "grows up" and starts using Hyperlambda ... Alternatively simply fire him, and sue him for damages claiming "gross negligence" ...

Key Features Making #Python Ideal for #ML Development 🤖 1. Simplicity Python's clean and readable syntax makes it easy for developers to understand and write code, reducing the time and effort required for development and maintenance. Its simplicity allows for rapid prototyping and experimentation, enabling data scientists to iterate quickly and explore different ML algorithms and techniques.  2. Extensive Ecosystem of Libraries Python has a rich ecosystem of libraries and frameworks designed explicitly for ML and AI, such as TensorFlow, PyTorch, Scikit-learn, and Keras. These libraries provide pre-built functions and modules for various ML tasks, including data preprocessing, model training, and evaluation, allowing developers to accelerate their development process and focus on solving complex problems.  3. Community Support and Documentation Python has a large and active community of developers, data scientists, and researchers who contribute to its development and maintenance. This vibrant community provides valuable resources, tutorials, and documentation, making it easier for newcomers to learn Python and ML concepts and troubleshoot issues encountered during development.  4. Flexibility Python's versatility allows developers to integrate ML models into existing software applications or deploy them as standalone services. Its compatibility with other programming languages and platforms, such as Java, C++, and JavaScript, makes it an excellent choice for building scalable and interoperable ML solutions. Curious how expert engineers leverage Python to build high-impact ML solutions? Let’s connect.

💡 Mastering Python List Operations — A Quick Summary 🐍 Lists are one of the most powerful and flexible data structures in Python. They allow us to store multiple items in a single variable and perform various operations efficiently. Understanding how to manipulate lists is essential for any Python developer — from beginners to professionals working with data, automation, or software development. In Python, lists are mutable, meaning their content can be changed without creating a new list. You can easily add, remove, count, sort, or reverse elements using built-in methods and functions. Here’s a summary of some of the most common and useful list operations every Python programmer should know: Adding elements: Using append() to insert items at the end of a list. Removing elements: With remove() (by value) or pop() (by index) for precise control. Finding values: max() and min() quickly return the largest and smallest elements. Summation: sum() allows fast aggregation of numeric data. Counting occurrences: count() helps track how often an element appears. Reversing order: Slicing with [::-1] instantly flips the list for reversed viewing. These operations make Python lists incredibly versatile — whether you’re analyzing data, managing collections, or building logic for real-world applications. Mastering them gives you a strong foundation for writing cleaner, faster, and more efficient Python code. 🚀 Key Takeaway Python’s list methods and built-in functions provide a rich set of tools for working with data dynamically. Knowing when and how to use them not only improves code efficiency but also enhances readability and maintainability. #Python #PythonProgramming #LearnPython #PythonDeveloper #Coding #Programming #CodeNewbie #SoftwareDevelopment #TechCommunity #DeveloperLife #100DaysOfCode #DataStructures #PythonTips #PythonLearning #Developers #Programmers #Automation #MachineLearning #AI #BigData #DataScience #SoftwareEngineer #CodeDaily #TechEducation #ComputerScience #CodingJourney #PythonCode #OpenSource #CodeSnippet #StudyPython #ManojKumarReddyParlapalli