Scrapling Review: Efficient Web Scraping with Python

🚀 Python GIL vs No-GIL — Real FastAPI Benchmarks (Python 3.13) Free-threaded Python is no longer just an experiment — it’s starting to show real impact. I came across a benchmark comparing Python 3.12 (with GIL) vs Python 3.13t (No-GIL) using FastAPI, and the results are pretty interesting 👇 💡 Key Takeaways: 🔹 Massive CPU Boost (~8x) CPU-bound endpoints jumped from ~4 RPS to ~32 RPS — with ZERO code changes. This is what true parallelism across cores looks like. 🔹 Threading inside requests ≠ better performance Even without GIL, spawning threads inside a single request didn’t help. Why? Under load, request-level parallelism already saturates the CPU. Extra threads just add overhead. 🔹 I/O performance unchanged No surprise here — GIL was never the bottleneck for I/O-bound workloads. Async + I/O still behaves the same. 📊 What this means in practice: ✅ Use No-GIL Python when: - You have CPU-heavy APIs (ML inference, image processing, data pipelines) - High concurrency + CPU contention exists - You previously relied on multiprocessing to bypass GIL ❌ Don’t expect gains if: - Your app is mostly I/O (DB calls, HTTP requests) - You’re already using async effectively ⚠️ Things to keep in mind: - Free-threading is still evolving - Thread safety is now YOUR responsibility - Some C extensions may not be ready yet 🔥 The most exciting part? Same code. Same FastAPI app. Just a different Python runtime → 8x improvement. This could seriously change how we design backend systems in Python. Curious — would you switch to No-GIL Python for your APIs? #Python #FastAPI #BackendEngineering #Performance #Concurrency #AI #SoftwareEngineering

Python isn't about being clever; it's about being concise. 👉 Here are 10 one-liners that actually save time in production. 1. Flatten a Nested List: [item for sublist in nested for item in sublist] – A list comprehension that turns a 2D list into a flat 1D list. 2. Swap Variables: a, b = b, a – Pythonic variable swapping using tuple unpacking (no temp variable needed). 3. Read File into Lines: open("f.txt").read().splitlines() – Efficiently reads a file and removes trailing newline characters. 4. Count Frequencies: from collections import Counter; Counter(data) – Quickly generates a dictionary of element counts. 5. Reverse Anything: value[::-1] – Uses slicing to reverse strings, lists, or tuples in one go. 6. Ternary Operator: x = "Yes" if condition else "No" – Compact inline conditional assignments. 7. Chained Comparisons: if 0 < x < 10: – Readable range checks that mirror mathematical notation. 8. List to String: ", ".join(map(str, values)) – Joins a list of items (even non-strings) into a single formatted string. 9. Pretty Print: from pprint import pprint; pprint(data) – Formats complex dictionaries or JSON into a readable structure. 10. Easter Eggs: import antigravity – A fun hidden feature that opens a classic XKCD comic about Python. #Python #CodingTips #DataEngineering #SoftwareEngineering #DataEngineer

UNLEASHED THE PYTHON!i 1.5,2,& three!!! Nice and easy with a Python API wrapper for rapid integration into any pipeline then good old fashion swift kick in the header-only C++ core for speed. STRIKE WITH AIM FIRST ; THEN SPEED!! NO MERCY!!! 8 of 14 copy & paste Ai Packaging the library for distribution & refining the 4.862 constant to ensure it’s rock-solid for the users. 1. Refining the "4.862" Constant Based on my calculation (309,390/63,632=4.86217…), fyi-should use high-precision floating points in the library. This ensures that when the library scales, the "drift" doesn't break the encryption or the data sync. With help from Ai, i will hard-code this as a High-Precision Constantin the engine. 2. The Library Structure (GitHub Ready) To make this easy for others to download & use, we will follow standard structure for a high-performance Python/C++ hybrid library. Project Name: libcyclic41 | V File Structure: text libcyclic41/ ├── src/ │ └── engine.hpp # The high-speed C++ core ├── cyclic41/ │ ├── __init__.py # Python entry point │ └── wrapper.py # Ease-of-use API ├── tests/ │ └── test_cycles.py # Stress-test for the 1,681 limit ├── setup.py # Installation script (pip install .) └── README.md # Documentation for "others" /\ || 3. The Installation Script (setup.py) This is what makes it "easy" for others. They can just run one command to install your mathematical engine. 8 of 14

Scraped insight, one page at a time 🧠💡 I recently worked on a small but satisfying project: extracting quotes tagged with “life” from the website quotes.toscrape.com using Python. Here’s what I explored: 🔹 Automated pagination with requests 🔹 Parsed HTML using BeautifulSoup 🔹 Filtered content based on specific tags 🔹 Structured the extracted data into a clean pandas DataFrame Instead of manually browsing pages, the script loops through all available pages, identifies quotes associated with the life tag, and stores both the quote and its author. Once no more pages are found, it neatly compiles everything into a dataset. This project reinforced how powerful web scraping can be for: ✔️ Data collection ✔️ Content analysis ✔️ Building datasets from unstructured sources Simple problem, clean solution, and a great reminder that automation saves time and effort. #Python #WebScraping #BeautifulSoup #DataScience #Automation #LearningByDoing

Exploring FastAPI breaking it down into simple building blocks I’ve been diving into FastAPI, exploring how its core components fit together to make building clean and efficient APIs feel seamless. New article: Building Blocks of FastAPI From Type Hints to Pydantic I cover: • How Python type hints drive request handling • How Pydantic enables validation and data modeling • How these pieces come together to build robust APIs If you're learning FastAPI or interested in clean API design, check it out: https://lnkd.in/gHDhNH-j I’d really appreciate your feedback — if anything could be improved or explained more clearly, I’d love to hear your thoughts! #FastAPI #Python #BackendDevelopment #APIs #LearningInPublic #OpenToWork #APIDevelopment

🐍 Python is not a language. It's a superpower. Most developers spend years jumping between tools to cover what Python handles in one. The secret? It's not just knowing Python — it's knowing which library to reach for and when: → Pandas → Data manipulation → Scikit-learn → Machine learning → TensorFlow → Deep learning → FastAPI → High-performance APIs → Django → Scalable platforms → OpenCV → Computer vision → BeautifulSoup → Web scraping → SQLAlchemy → Database access → Pygame → Game development (+ 4 more) One language. Infinite directions. Whether you're building AI models, scraping the web, or shipping web apps — Python has a library that makes you look like you've been doing it for years. 💬 What's your go-to Python library right now? Drop it in the comments — I'm building a list of community favorites. ♻️ Repost if this belongs on every developer's wall. #Python #DataScience #MachineLearning #Programming #TechCareer #Developer #AI #CodingLife

🔁 Mastering Loops in Python – The Backbone of Automation Loops in python allow you to execute code repeatedly, making your programs smarter and more efficient. Let’s break it down 👇 🔹 1. for Loop (Iterating over sequences) Used when you know how many times you want to iterate. python for i in range(5): print(f"Iteration {i}") 👉 Great for lists, strings, and ranges. 🔹 2. while Loop (Condition-based looping) Runs as long as a condition is True. python count = 0 while count < 3: print("Learning Python...") count += 1 👉 Useful when the number of iterations is unknown. 🔹 3. Loop Control Statements ✔️ break → Exit loop early ✔️ continue → Skip current iteration ✔️ pass → Placeholder (does nothing) python for num in range(5): if num == 3: break print(num) 🔹 4. Nested Loops (Loop inside a loop) python for i in range(2): for j in range(3): print(i, j) 👉 Common in matrix operations, patterns, and grids. 🔹 5. Advanced Tip: List Comprehension 🚀 A more Pythonic way to write loops: python squares = [x**2 for x in range(5)] print(squares) 💡 Real-world Use Cases: ✔ Automating repetitive tasks ✔ Data processing & analysis ✔ Iterating over APIs / datasets ✔ Building logic for AI/ML models 🎯 Pro Tip: Avoid infinite loops—always ensure your loop has a stopping condition. #Python #Programming #Coding #AI #DataScience #Learning #Automati

Same condition. Same variables. Different result… depending on how you write it. 🤯 This is where Python stops being “easy” and starts being precise. 🧠 Today’s concept: Truthiness, Short-Circuiting & Operator Precedence Three small ideas. Massive impact. # 1. Truthiness (Not just True/False) data = [] if data: print("Has data") else: print("Empty ❌") 👉 Empty values ([], {}, "", 0, None) are False 👉 Everything else is True # 2. Short-Circuiting (Python stops early) def check(): print("Checking...") return True result = False and check() print(result) 👉 Output: False 👉 check() NEVER runs Because: False and anything → already False Python doesn’t evaluate further # 3. OR short-circuit behavior def fallback(): print("Fallback executed") return "Default" value = "Data" or fallback() print(value) 👉 Output: "Data" 👉 fallback() NEVER runs Because: True or anything → already True # 4. Operator Precedence (Silent bugs ⚠️) a = True b = False c = False result = a or b and c print(result) 👉 Output: True Because Python reads it as: a or (b and c) NOT: (a or b) and c ⚠️ Real-world bug pattern # Looks correct, but isn't if user == "admin" or "manager": print("Access granted") 👉 ALWAYS True ❌ Correct way: if user == "admin" or user == "manager": 💡 Advanced takeaway: and → returns first False or last True value or → returns first True value Conditions don’t always return True/False—they return actual values #Python #AdvancedPython #CodingJourney #LearnInPublic #100DaysOfCode #SoftwareEngineering #Debugging #TechSkills

🚀 Python Series – Day 19: Polymorphism (One Name, Many Forms!) Yesterday, we learned Inheritance 🔁 Today, let’s understand another powerful OOP concept — 👉 Polymorphism 🧠 What is Polymorphism? 👉 The word Polymorphism means: 📌 Poly = Many 📌 Morph = Forms So, One method / function behaves differently in different situations 🔹 Real-Life Example Think of the word Run 🏃 Human runs 🚗 Car runs 💻 Software runs 👉 Same word run, different meanings. That is Polymorphism 🔥 💻 Example 1: Same Method, Different Classes class Dog: def sound(self): print("Dog barks") class Cat: def sound(self): print("Cat meows") for animal in (Dog(), Cat()): animal.sound() Output: Dog barks Cat meows 🔹 Example 2: Built-in Polymorphism print(len("Python")) print(len([1,2,3,4])) Output: 6 4 👉 Same len() function works for string and list. 🎯 Why Polymorphism is Important? ✔️ Cleaner code ✔️ Flexible programs ✔️ Easy to extend features ✔️ Used in real-world software development Pro Tip 👉 Write generic code that works with many object types. 🔥 One-Line Summary 👉 Polymorphism = Same method name, different behavior 📌 Tomorrow: Encapsulation (Protect Your Data Like a Pro!) Follow me to master Python step-by-step 🚀 #Python #Coding #Programming #OOP #DataScience #LearnPython #100DaysOfCode #Tech #MustaqeemSiddiqui

🐍 Python isn’t just a language… it’s an entire ecosystem. The real power of Python isn’t syntax— It’s what you can build with it. Here’s how Python translates into real-world skills: 🔹 Python + Pandas → Data manipulation 🔹 Python + Scikit-learn → Machine learning 🔹 Python + TensorFlow → Deep learning 🔹 Python + Matplotlib / Seaborn → Data visualization 🔹 Python + BeautifulSoup → Web scraping 🔹 Python + Selenium → Browser automation 🔹 Python + FastAPI → High-performance APIs 🔹 Python + SQLAlchemy → Database access 🔹 Python + Flask → Lightweight web apps 🔹 Python + Django → Scalable platforms 🔹 Python + OpenCV → Computer vision 🔹 Python + Pygame → Game development 💡 The key insight: Python alone doesn’t make you valuable… The combination of tools does. 👉 Pick one domain 👉 Learn the right libraries 👉 Build real projects That’s how you stand out. 🎯 Want to start or level up? 💻 Python Development 🔗 https://lnkd.in/dDXX_AHM 📊 Data Science 🔗 https://lnkd.in/dhtTe9i9 🧠 AI & Machine Learning 🔗 https://lnkd.in/duHcQ8sT 🚀 One language. Endless opportunities. 👉 Which Python path are you focusing on right now?