Understanding Iterators in Python

Topic 4/100 🚀 🧠 Topic 4 — Generators Processing large data and running out of memory? 😵 This concept solves that problem. 👉 What is it? Generators are functions that return values one at a time using yield instead of returning everything at once. 👉 Use Case: Used in real-world applications for: Reading large files Data streaming Handling APIs with pagination 👉 Why it’s Helpful: Saves memory Improves performance Enables lazy evaluation (data generated only when needed) 💻 Example: def count_up_to(n): for i in range(n): yield i for num in count_up_to(5): print(num) 🧠 What’s happening here? Instead of storing all values in memory, the function generates them one by one when needed. ⚡ Pro Tip: If you're working with large datasets, always think “generator” before using lists. 💬 Follow this series for more Topics #Python #BackendDevelopment #100TopicOfCode #SoftwareEngineering #LearnInPublic

🚀 Stop killing your CPU with Python loops. I recently refactored a data transformation pipeline that was crawling because it processed 5 million rows using a standard row-by-row iteration. Moving from native loops to vectorized operations changed everything. Before optimisation: results = [] for i in range(len(df)): val = df.iloc[i]['price'] * df.iloc[i]['tax_rate'] results.append(val) df['total'] = results After optimisation: df['total'] = df['price'] * df['tax_rate'] Performance gain: 45x faster execution time. Vectorization offloads the heavy lifting to highly optimised C code under the hood. When you use Pandas or NumPy native methods, you stop fighting the interpreter and start leveraging memory alignment. If you are still writing loops for data manipulation, you are leaving massive amounts of compute time on the table. It is the easiest performance win you can claim this week. What is the biggest speed boost you have ever achieved by swapping a loop for a built-in vectorised function? #DataEngineering #Python #Pandas #Performance #Optimization

Topic 7/100 🚀 🧠 Topic 7 — Lambda Functions Want to write a quick function in just one line? ⚡ 👉 What is it? Lambda functions are small anonymous functions defined using the lambda keyword. 👉 Use Case: Used in real-world applications for: Quick operations inside map(), filter() Sorting with custom logic Short, throwaway functions 👉 Why it’s Helpful: Reduces boilerplate code Makes code concise Useful for functional programming 💻 Example: # Normal function def square(x): return x * x # Lambda version square = lambda x: x * x print(square(5)) 🧠 What’s happening here? We replaced a full function definition with a single-line lambda expression. ⚡ Pro Tip: Use lambdas for small logic only — avoid them for complex functions. 💬 Follow this series for more Topics #Python #BackendDevelopment #100TopicOfCode #SoftwareEngineering #LearnInPublic

I had the chance to delve into the Noise2Void codebase from zero and I must say I absolutely adored the way it was organized. The project has the workflow decoupled into very specific parts such as data handling, masking, and model training, thus making it easier for the reader to grasp the contents of the code. What I found to be the most remarkable part is the trick that the code employs through the use of custom functions and training logic to hide pixels and let the network predict from surrounding context. This is an excellent project that exhibits how well clean Python structure and TensorFlow-based modeling can work together in a smart self-supervised learning environment. Another thing I found useful was the codebase with its logic being modular, which gives the pipeline a practical and well-engineered feeling instead of being too complicated. Repo: https://lnkd.in/eF7nZXxX

Topic 3/100 🚀 🧠 Topic 3 — Context Managers Ever forgotten to close a file or database connection? 😅 That’s where this concept saves you. 👉 What is it? Context Managers allow you to manage resources automatically using the with statement. 👉 Use Case: Used in real-world applications for: File handling Database connections Managing locks in concurrent systems 👉 Why it’s Helpful: Prevents memory leaks Automatically cleans up resources Makes code cleaner and safer 💻 Example: with open("file.txt", "w") as f: f.write("Hello, World!") 🧠 What’s happening here? Python automatically opens the file and ensures it gets closed after the block executes — even if an error occurs. ⚡ Pro Tip: Always use context managers when working with external resources — it’s a best practice in production code. 💬 Follow this series for more Topics #Python #BackendDevelopment #100TopicOfCode #SoftwareEngineering #LearnInPublic

Don't flatten what naturally has structure. It's tempting to model everything in a single class. Easy to write, easy to read, at least until your data grows. This is where most codebases start, with just one model. But with model composition, each model has a single responsibility. And Pydantic handles nested validation automatically. Structure your models the way your domain is actually structured. The code gets cleaner, the errors get clearer, and reuse becomes obvious. This and other real-world modelling patterns are covered in Practical Pydantic: 👉 https://lnkd.in/eGiB7ZxU Model your domain. Not just your data. #Python #Pydantic #Data #Models #Patterns

Day 4/30 🔹 Problem: Find the second largest number in a list 🔹 What I focused on today: Thinking beyond the obvious solution and handling edge cases 🔹 My Thinking Process: Take a list of numbers from the user Remove duplicate values Sort the list Pick the second last element 👉 Simple idea, but requires careful steps 🔹 Inputs I used: List of numbers 🔹 Code: numbers = list(map(int, input("Enter numbers separated by space: ").split())) # Remove duplicates numbers = list(set(numbers)) # Sort the list numbers.sort() # Find second largest if len(numbers) < 2: print("Not enough elements") else: print("Second Largest Number:", numbers[-2]) 🔹 Example: Input: 10 20 30 40 Output → 30 🔹 Key Takeaway: Breaking a problem into steps like cleaning data, sorting, and selecting values makes it easier to solve #Day4 #Python #30DaysOfCode #LearningInPublic #DataAnalytics #ProblemSolving

Just shipped a new feature in my VS Code extension, CallFlow Tracer: automatic trace summarization. Performance traces usually give raw data, not answers. This feature turns complex call graphs into clear, actionable insights with one click. Identifies the slowest functions Shows exact time impact (percentages) Highlights bottleneck modules Suggests next optimization steps Provides complete trace statistics No more manually analyzing hundreds of nodes or guessing where the issue is. You get a clean summary of what to fix and why. Available now on the VS Code Marketplace — search “CallFlow Tracer”. What’s one performance insight you wish tools gave you automatically? #Python #VSCode #DeveloperTools #PerformanceOptimization #OpenSource

✅ Day 87 of 100 Days LeetCode Challenge Problem: 🔹 #2840 – Check if Strings Can be Made Equal With Operations II 🔗 https://lnkd.in/gY73RBb5 Learning Journey: 🔹 Today’s problem extended the previous one, allowing swaps between indices where the difference is even. 🔹 I observed that this again partitions the string into two independent groups: • Even indices (0, 2, 4, …) • Odd indices (1, 3, 5, …) 🔹 I extracted characters from even and odd positions separately for both strings. 🔹 Then, I sorted these groups and compared them between s1 and s2. 🔹 If both even-index groups and odd-index groups match, the strings can be made equal. Concepts Used: 🔹 String Manipulation 🔹 Index Grouping (Parity-based) 🔹 Sorting 🔹 Greedy Observation Key Insight: 🔹 Since swaps are allowed only between indices with even distance, characters can only move within their parity group. 🔹 Therefore, the problem reduces to checking if both parity groups have identical character distributions. Complexity: 🔹 Time: O(n log n) 🔹 Space: O(n) #LeetCode #Algorithms #DataStructures #CodingInterview #100DaysOfCode #SoftwareEngineering #Python #ProblemSolving #LearningInPublic #TechCareers

People know me for data analysis. But honestly? I'm just as obsessed with automation. There's something satisfying about writing a script once — and never doing that task manually again. Just finished building an Auto File Organizer in Python as my first automation project. It sorts any messy folder by file type and date. Automatically. With a log file. With duplicate protection. Small project. Big lesson. Data tells you what's happening. Automation handles what keeps happening. Building both. 🚀 #Python #Automation #DataAnalysis #Learning