Python Context Managers Ensure Resource Cleanup

𝗧𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 "𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲" 𝗧𝗿𝗮𝗽 I noticed something interesting today : changed a value inside a function, and it reflected outside too I didn’t return anything. I didn’t re-assign the variable ➡️ 𝗧𝗵𝗲 𝗖𝗮𝘁𝗰𝗵 : Python functions don't always create a new "𝗰𝗼𝗽𝘆" of your data Instead, they often work with 𝗮 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 to the original object ▪️𝗠𝘂𝘁𝗮𝗯𝗹𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝘀 (Lists, Dicts, Sets) are modified in place. Any change inside the function affects the original data directly. ▪️𝗜𝗺𝗺𝘂𝘁𝗮𝗯𝗹𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝘀  (Integers, Strings,Tuples) are safe because they can't be changed in place. ➡️ 𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 : If you’re working with Lists or Dictionaries and want to keep your original data safe, you must be explicit: update(my_list.copy()) Small detail, but missing it can lead to hours of debugging bugs #Python #30DaysOfCode #SoftwareEngineering #LearningInPublic #Day19

🚀 Day 75 of #100DaysOfCode 🔥 LeetCode 179 – Largest Number 💡 Problem: Given a list of non-negative integers, arrange them such that they form the largest possible number. 🧠 Key Insight: Normal sorting won't work here ❌ We need a custom comparator based on string concatenation. 👉 Compare: - ""a + b"" vs ""b + a"" - Whichever gives a larger value should come first. ⚙️ Approach: 1. Convert numbers to strings 2. Sort using custom comparison logic 3. Join the result 4. Handle edge case (like "[0,0] → "0"") ⚡ Complexity: - Time: O(n log n) - Space: O(n) 🎯 Result: ✅ Accepted ⚡ Runtime: 0 ms (100%) 📌 Lesson Learned: Sometimes sorting logic depends on combination, not value. #LeetCode #Python #CodingJourney #DSA #100DaysOfCode #Sorting #ProblemSolving

Most automation scripts tend to fail when faced with unexpected issues such as timeouts, dropped connections, or configuration changes that necessitate a complete rewrite. Here are three Python patterns that have transformed my approach to building pipelines: 1 - **Retry with backoff**: APIs can fail, and your script should be equipped to handle these failures gracefully, eliminating the need for you to monitor it at 2 AM. 2 - **Context managers**: Keeping connections open or leaving temporary files behind can lead to elusive bugs weeks later. 3 - **Config-driven pipelines**: Hard-coding a URL or selector creates a script that only functions for the present moment. The goal is not to increase the amount of code written but to create code that can withstand the challenges of the real world. What patterns do you rely on most in your automation work? #Python #Automation #SoftwareEngineering #DataEngineering #PythonTips

You write for loops every day. Do you know what actually runs underneath them? Day 03 of 30 -- Generators and Iterators Deep Dive Advanced Python + Real Projects Series Python calls iter() to get the iterator, then next() repeatedly until StopIteration is raised. That is every for loop you have ever written. And yield pauses the function, hands the value out, and resumes from the exact same line next time. Today's topic covers: The lazy vs eager evaluation problem -- why loading 10GB into a list crashes servers The full iterator protocol -- what powers every for loop 3 types -- generator function, expression, async generator Annotated syntax -- basic, yield from, and the send() two-way pattern Real fintech pipeline -- 52GB log file, 4.2MB memory used 5 production mistakes including exhausting a generator twice Generator pipeline architecture -- identical to Unix pipes Key insight: Don't store what you can stream. #Python #PythonProgramming #DataEngineering #BackendDevelopment #LearnPython #100DaysOfCode #PythonDeveloper #SoftwareEngineering #TechContent #BuildInPublic #TechIndia #CleanCode #CodingTips #CodeNewbie #LinkedInCreator #PythonTutorial

𝗣𝘆𝘁𝗵𝗼𝗻'𝘀 𝗠𝗥𝗢 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗿𝗶𝘃𝗶𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 — 𝗶𝘁'𝘀 𝘁𝗵𝗲 𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗯𝗶𝘁𝗲𝘀 𝘆𝗼𝘂 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘄𝗵𝗲𝗻 𝗮 𝗺𝗲𝘁𝗵𝗼𝗱 𝘀𝗶𝗹𝗲𝗻𝘁𝗹𝘆 𝗿𝗲𝘀𝗼𝗹𝘃𝗲𝘀 𝘁𝗼 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗽𝗮𝗿𝗲𝗻𝘁. Most codebases with deep inheritance chains rely on MRO without knowing the exact resolution order. C3 linearization guarantees consistency, but only if you actually trace it. 🔹 MRO resolves left-to-right, depth-first, with C3 ensuring no base appears before its subclasses. Calling 𝗥𝗲𝗰𝗼𝗿𝗱.𝗺𝗿𝗼() prints the exact chain Python uses. 🔹 The Golden Rule: If you can't determine the resolution order without running the code, your inheritance tree is too implicit. 🔹 Surface it, flatten it, or better yet: favor 𝘤𝘰𝘮𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯 𝘰𝘷𝘦𝘳 𝘪𝘯𝘩𝘦𝘳𝘪𝘵𝘢𝘯𝘤𝘦 to keep your architecture predictable. #Python #SoftwareEngineering #BackendDevelopment #SoftwareArchitecture #CleanCode

Day 2/30 – Building with Python Recently, I worked on a Vehicle Feedback System using Python The idea behind this project was to create a simple system where users can: 📝 Submit feedback about vehicles 📊 Store and manage responses efficiently Through this project, I learned: ✨ How to handle user input and data ✨ Basic logic building and structuring a program ✨ The importance of user-friendly systems This is just the beginning — I’m planning to improve it further by adding: OTP-based authentication for better security Database integration for scalability Possibly a simple UI for better user experience Building projects like this is helping me understand how real-world systems evolve step by step Would love your suggestions or ideas to improve this further! #Day2 #PythonProject #LearningInPublic #StudentDeveloper #BuildInPublic #TechJourney

New blog post! Live Life on the Edge: A Layered Strategy for Testing Data Models This post is about a three-layer testing pattern for complex software systems I've landed on in python: structural coverage with Polyfactory, value-level probing with Hypothesis, cross-field invariants with icontract. Includes a practical example, an honest tradeoffs section, and a note on what schema-first design and consumer-driven contract testing solve instead. Link in comments. #Python #SoftwareTesting #SoftwareArchitecture #Pydantic #PropertyBasedTesting

Manual test vs prod column checks were slowing down releases. So I automated the process. I built a lightweight Python script that compares jqGrid column definitions by simply pasting raw column lists from both environments. What the script validates: Total column count Columns missing in test or prod Exact column order match Position-wise differences when order changes Whitespace and blank-line cleanup before comparison Why this matters: Prevents schema drift from reaching production Reduces manual validation effort Improves release confidence with a repeatable check Small automation, real impact. Cleaner releases. Faster validation. Better quality. If helpful, I can share the reusable template format as well. #Python #Automation #QA #SDET #SoftwareTesting #QualityEngineering #ReleaseManagement #DataValidation #Productivity Automated jqGrid column parity check between test and prod for faster, safer releases.

A 40ms API became a 4ms API. Here's the only thing that changed. We were making 3 separate DB queries to assemble a response. Each was fast in isolation. Together, they were sequential — each waited for the previous. The fix: run them concurrently. In Python (asyncio), this went from: result_a = await get_a() result_b = await get_b() result_c = await get_c() To: result_a, result_b, result_c = await asyncio.gather(get_a(), get_b(), get_c()) That's it. No caching, no infra change, no complex refactor. The mental model that helps: always ask "are these operations actually dependent on each other?" before assuming they need to run in sequence. Most API latency problems aren't hard — they're just unexamined. #BackendDevelopment #PythonAsyncio #APIOptimization #SoftwareEngineering

Built a Python-based Directory Sync Tool to compare and synchronize files between two directories with reliability and control. Instead of relying only on file names or timestamps, the tool uses a combination of metadata and SHA-256 hashing to accurately detect new, modified, and missing files. Key highlights: • Recursive directory scanning with structured metadata (name, extensions, size, hash) • Efficient change detection using size-first filtering followed by hash comparison • Memory-efficient hashing using chunk-based file reading (handles large files) • Synchronization support with metadata preservation using shutil.copy2 • Safe cleanup by optionally removing extra files from the destination While building this, I focused on moving beyond a basic script and treating it like a real tool, structuring the code into clear components, improving output readability, and adding validation and error handling to make it more reliable in real use. GitHub:https://lnkd.in/gt-Ec3rF #Python #CLI #GitHubProjects #SoftwareDevelopment #LearningByBuilding #SystemsThinking