Learn Python Fundamentals with Day 1 Code Execution

Day 22/28 - The Fear of Official Documentation For a long time, I actively avoided official documentation. If I couldn't figure out a Pandas function or a model in Scikit-Learn, I would go anywhere else. I would search Reddit, look for a YouTube tutorial, or read a random blog post. The official docs just looked intimidating. They felt like they were written for experts, not for someone who was just trying to learn. The long list of parameters and the dense text were overwhelming to even look at. But relying only on tutorials has a downside. You are always just reading someone else’s interpretation of the tool. And sometimes, they leave out the exact small detail you actually need. Lately, I’ve been forcing myself to open the official documentation first before searching anywhere else. It is definitely slower. Sometimes I have to read the same paragraph three times just to understand what a specific parameter does. But I’m realizing something important. Reading documentation is a completely separate skill from writing code. And it takes just as much practice. Will reading official documentation ever actually get easier? #28DaysOfProgress #DataScience #Python #DataAnalytics

I wasted 3 hours thinking my code was broken. It wasn't. The GIL was just laughing at me. I had this Python script doing heavy data processing. Added multi-threading to speed it up. Ran it. Expected magic. Got... the exact same speed. Maybe even slower. I checked my logic. Checked my threads. Googled everything. Then finally someone in a forum said — "Bro, that's just the GIL." So what even IS the GIL? In simple words — Python has this rule that says only one thread can run at a time, no matter how many cores your machine has. It's called the Global Interpreter Lock. It exists to keep Python's memory safe, but it also means your threads aren't actually running in parallel when doing heavy computation. I felt cheated honestly. But here's what actually helped me: → Switched to multiprocessing — each process has its own GIL. Problem solved. → For API calls and I/O stuff? asyncio is your best friend. → Libraries like NumPy actually release the GIL during computation. Smart. → Python 3.13 is experimenting with making the GIL optional. The future looks good. The GIL isn't evil. It's just something nobody tells you about when you're starting out — and you only discover it when you're sitting there confused at 2am wondering why your "optimized" code is still slow. If this saves even one person that 3-hour spiral, this post was worth it. Have you hit the GIL wall before? What did you do? Let me know below #Python #PythonDeveloper #Programming #LearnPython #SoftwareEngineering #CodingLife #TechCommunity

Today, I worked on understanding and implementing Linear Regression from scratch using Python and scikit-learn. I focused not just on writing code, but on understanding why the model works. One key insight I learned today is the importance of model evaluation. Metrics like MAE, MSE, and RMSE help quantify how wrong a model’s predictions are, rather than relying on visual plots or intuition alone. This matters because in real-world data science, a model that looks good can still fail if it doesn’t generalize well. Learning to evaluate models properly is just as important as building them. I documented the full process and code here on GitHub: https://lnkd.in/gdM6rcRx Looking forward to building stronger foundations step by step. #MachineLearning #DataScience #LearningInPublic #Python #StudentDeveloper

Hey everyone! Mahdi Shamlou here 🚀 I recently explored durable workflow engines in Python — something a lot of posts online don’t cover well in 2026. Here’s the quick rundown: ✨ Temporal – battle-tested, distributed, perfect for mission-critical workflows ⚡ DBOS Transact – lightweight, Postgres-backed, embedded durability 🐍 Prefect 3.x – Pythonic, open-source, great for data pipelines & automation 🛠️ Custom Python Engine – pure Python, full control, but tricky at scale 💡 My takeaway: For most projects, start with Prefect — durable, Pythonic, and minimal ops overhead. Use Temporal only when you really need ultra-long workflows or multi-language orchestration. Check out the full post here: https://lnkd.in/eCAkJq-M https://lnkd.in/eKYRR3pV Have you used any of these engines? How do you handle durable workflows in Python? Drop your thoughts below 👇 #programming #python #ai #workflows #designsystems

Think of this first step like chopping up your ingredients before you start cooking. When you write a piece of code like r = 1 + 1, Python doesn't swallow the whole sentence at once. Instead, it chops that line of text up into tiny, individual pieces called tokens. It separates everything out: The variable r The equals sign = The numbers 1 The plus sign + Even the blank spaces between them! It’s basically just organizing the raw text into bite-sized pieces so the computer can look at each individual part before trying to understand the whole sentence. wait for part-----------2.

“Why not just use SciPy?” is a question I’ve been asked several times after mentioning that I developed tea-tasting, a Python package for A/B test analysis. Yes, you can use SciPy. But the real question is how many A/B-test-specific methods you would need to implement manually (or with a coding agent) without making an error. To illustrate the difference, I wrote an article comparing tea-tasting with general purpose statistical packages such as Pingouin, statsmodels, and SciPy 🔗👇

🚀 Python Journey — Day 6 | Deep Dive into Loops & Strings Today I solved a variety of problems using both for and while loops, focusing on numbers as well as strings. Problems I solved : • Count of factors of a number • Prime number check • Even & odd numbers in a given range • Count of even and odd numbers • Reverse a string (using loop logic) • Palindrome string check • Sum of digits of a number • Product of digits • Armstrong number check I implemented most problems using both for loop and while loop to strengthen my understanding. Today's learnings: ✅ Using loops with conditions effectively ✅ Working with digits using % and // ✅ Understanding factor and prime logic ✅ Basic string manipulation using loops ✅ Improving logical thinking through practice Today helped me connect loops with real problem-solving in both numbers and strings. Thanks to Rudra Sravan kumar sir for the guidance and continuous support. Learning step by step and getting more confident 📌 Consistency > Motivation On to Day 7 💻 #PythonJourney #Day6 #PythonFullStack #10000Coders #LearningInPublic #Loops #ProblemSolving #CodeEveryDay #FutureDeveloper #KeepLearning #PythonDeveloper

🚀 Day 49 of #100DaysOfCode LeetCode #83 – Remove Duplicates from Sorted List Today I solved a classic Linked List problem: 👉 Given the head of a sorted linked list, delete all duplicates such that each element appears only once. 👉 Return the linked list sorted as well. Since the list is already sorted, duplicates will always appear next to each other — which makes the solution efficient and elegant. 💡 Key Insight: Instead of using extra space (like a set), we can simply: Traverse the linked list Compare the current node with the next node Skip the next node if it’s a duplicate 🧠 Python Implementation: class ListNode: def __init__(self, val=0, next=None): self.val = val self.next = next def deleteDuplicates(head): current = head while current and current.next: if current.val == current.next.val: current.next = current.next.next else: current = current.next return head ✅ Why This Works: Time Complexity: O(n) Space Complexity: O(1) (no extra memory used) Efficient because the list is already sorted Problems like this strengthen understanding of: ✔️ Linked List traversal ✔️ Pointer manipulation ✔️ Writing clean and optimal code Consistency in solving small problems builds strong fundamentals. 💪 #LeetCode #Python #DataStructures #LinkedList #ProblemSolving #CodingJourney

🚀 Day 41 / 200 Days of Code Solved the “Majority Element” problem on LeetCode today! 🔹 Problem: Find the element that appears more than ⌊n/2⌋ times in an array. 🔹 Constraint: Majority element is guaranteed to exist. 🔹 Approach Used: Python built-in optimization (max(set(nums), key=nums.count)) 🔹 Status: ✅ Accepted 🔹 Runtime: 0 ms 🔹 Memory: 21.07 MB (Beats 84.28%) While a simple built-in solution works, I also explored the Boyer–Moore Voting Algorithm, which solves it in O(n) time and O(1) space — a powerful technique frequently asked in interviews. 💡 Key Learning: Efficient problem-solving isn’t just about passing test cases — it’s about understanding optimized approaches and improving algorithmic thinking. Consistency > Motivation. One problem a day. Steady progress toward mastery. #Day41 #200DaysOfCode #LeetCode #Python #DataStructures #Algorithms #CodingJourney #ProblemSolving

Just published a beginner-friendly guide on the Chi-Square Test — one of the most practical tools for analysing categorical data. When I first saw a table of counts, I remember thinking… “Is this pattern meaningful, or just random numbers?” This guide walks through Chi-Square step-by-step — covering: ✔ Core intuition ✔ Types of Chi-Square tests ✔ Formula breakdown ✔ Real example ✔ Python implementation #DataScience #Statistics #Python #LearningJourney #DataAnalytics If you’re learning statistics or data science, this is a concept worth mastering. Read here 👇