Patrick Fromaget (RaspberryTips)’s Post

🚀 Day 38 of My Python Learning Journey Today I worked on an interesting Python problem that involved evaluating a polynomial expression using user input. 🔹 The task was to read values for x and k, then take a polynomial expression as input and check whether the polynomial evaluated at x equals k. 💡 This problem helped me understand: • How input() works in Python • How eval() can evaluate mathematical expressions dynamically • How to verify conditions and return True/False 🧠 Key Idea: Evaluate the polynomial expression using eval() and compare the result with the expected value. x, k = map(int, input().split()) poly = input() print(eval(poly) == k) ✨ This exercise strengthened my understanding of Python expression evaluation and logical comparison. Every day of practice brings me one step closer to mastering Python and building strong problem-solving skills. 💻 #Python #CodingJourney #ProblemSolving #Programming #Learning #HackerRank

Building a deep research agent with Ivan Leo right now. Python, Pydantic, and APIs are all you need. Thinking of this structure: 1.⁠ ⁠User prompt 2.⁠ ⁠Agent asks clarifying questions if needed 3.⁠ ⁠Planner writes research plan 4.⁠ ⁠Main agent orchestrates subagents with individual questions/topics 5.⁠ ⁠Subagents return answers + sources (the context eng bit) 6.⁠ ⁠Main agent synthesizes into final report Would you add/delete anything? Come build this with us in a few days. Register to join live or get the recording afterwards: https://luma.com/u9osjjbn

Interesting result today. With Jonah we were able to take BGPy (Python BGP Sim), and using AI, rewrite it with C-style Python objects. Went from 170s in Python (50ish in PyPy) to ~6s, with zero loss of extendability/ease of use (everything is still Python). Imo a lot of Python code will move in this direction now that claude code is so good. Loving the research with Dr. Amir Herzberg and Dr. Bing Wang!

Discovered why Python lists kill performance (200ms vs 2ms for 1M elements!) while NumPy delivers 100x speedup + 8x less memory. Explained with a chef analogy: cache misses = running to the store, GIL = one chef rule. Read full blog on medium: https://lnkd.in/dWDGWtuw #NumPy hashtag #Python hashtag #DataScience hashtag #Performance hashtag #GenAi hashtag #DataScience

When I first started using Pandas, I wrote code the same way I wrote normal Python. Lots of loops. Lots of step-by-step logic. And it worked… at first. But then datasets got bigger. And things slowed down quickly. That’s when I learned something important: 👉 Pandas works best when you think in vectorized operations. Instead of: looping through rows You start thinking in columns. Example mindset shift: Instead of processing each row individually, you transform entire columns at once. This small change made my code: ✔ faster ✔ simpler ✔ easier to read Still learning, but it's one of those small mental shifts that really changes how you work with data. #DataEngineering #Python #Pandas

Ever worried unpredictable #LLM outputs could crash your app? Raw AI data can be a major vulnerability. Learn how to build robust guardrails with Python & Pydantic, ensuring your #AIdriven features are reliable and safe. Curious how to fortify your systems? 🔗 https://lnkd.in/dvfywHpU #LLM #AI #Python #DataValidation #AIGuardrails

💡 Python Tip – List Comprehension (Write Cleaner Code) While practicing Python today, I explored List Comprehension, a powerful feature that makes code more readable and efficient. Instead of writing a traditional loop, we can generate lists in a single line. 🔹 Example Traditional way: numbers = [] for x in range(10): numbers.append(x*x) Using List Comprehension: numbers = [x*x for x in range(10)] ✅ Benefits Cleaner code Faster execution More Pythonic approach 📌 Small improvements like this can make Python code simpler and more efficient. #Python #PythonTips #Coding #DataEngineering #LearningInPublic

In Python, if we write: a = b = [ ] Are we creating two lists or just one? Actually, Python creates only one list in memory, and both variables "a" and "b" reference the same object. This happens because assignment in Python doesn’t copy objects. Instead, it simply makes the variables point to the same object in memory. Let’s look at a simple example: a = b = [ ] a.append(1) print(a) print(b) Output: [1] [1] Even though we only modified "a", "b" changed as well. That’s because both variables are referencing the same list. If you want two independent lists, you should create them separately: a = [ ] b = [ ] 📌 Key takeaway: In Python, variables store references to objects, not the objects themselves. #Python #Programming #DataScience #AI #Instant

🐍 10 Python projects that actually make you better (not just busy). Stop getting stuck in tutorial hell. If you want to actually level up your Python skills this year, you need to build things that solve real problems. Here are 10 projects that will teach you core engineering concepts: https://lnkd.in/ge_-4Hby https://lnkd.in/g9HDS32J

Ever feel your Python loops are a bit clunky? You often calculate a value. Then you immediately check it in the next line. This trick lets you assign and check a variable *right inside* your condition. It makes data processing cleaner and more direct for AI/ML tasks. 💡 Do you use the walrus operator? Or what's your favorite Python trick for cleaner loops? #Python #AI #MachineLearning #CodingTips #Tech