Shallow vs Deep Copy in Python: Preventing Data Corruption

Your Python logs are lying to you. 🚩 Most server logs are parsed line-by-line in Python. It’s the industry standard because it's easy. But it’s slow, and more importantly, it can be inaccurate. I just benchmarked a 10M row server log ingestion using standard Python vs. a custom C-Hybrid engine I built. Here are the results: 🚀 Execution Speed: 1.01s (Python) ➡️ 0.20s (Hybrid C) 🛡️ Data Integrity: Detected 180 "Ghost" errors that standard parsing missed. Why the difference? Standard line-by-line readers are "blind" to strings sliced exactly across I/O memory boundaries. If a status code like " 500 " is split between two chunks of data, standard iteration skips it. I solved this by building a Hybrid Engine that uses: 1️⃣ 8KB Binary Buffered I/O: Reading raw bytes directly into RAM. 2️⃣ Boundary Overlap Logic: Ensuring no string is ever "sliced" out of existence. 3️⃣ C-Python Bridge: Bringing C-level speed into a Python workflow using ctypes. The ROI: A 5x speedup and 100% data integrity. At enterprise scale (Netflix/Uber), this is the difference between catching a critical security signal and wasting thousands in unnecessary compute costs. 📂 Source Code: https://lnkd.in/g6Vv7DN2 I’m opening 3 slots for free performance audits on data pipelines this week. If your logs are slow or you suspect your numbers aren't 100% accurate, DM me 'OPTIMIZE'. #Python #CProgramming #DataEngineering #PerformanceOptimization #Backend #SoftwareArchitecture #ZeroLatency

Been building CallFlow Tracer, an open-source Python library that traces function call flows and visualizes them as interactive graphs. Just shipped v0.4.1 with some major improvements: Fixed critical security vulnerabilities (command injection, code injection in the old extension) Added Content Security Policy headers to all webviews What CallFlow Tracer does in this new version: OpenTelemetry export for production observability (Jaeger, OTLP) SLA/SLO tracking with error budgets and canary analysis Framework integrations: FastAPI, Flask, Django, SQLAlchemy Fixed imports and made it more modular and extensible. Would love feedback from anyone working on observability, profiling, or developer tooling. below is the link https://lnkd.in/drUQspvv #Python #OpenSource #DeveloperTools #Observability #OpenTelemetry #VSCode #TypeScript #SoftwareEngineering

🚀 Day 9: File Handling in Python In real-world applications, data doesn’t just live in variables it is stored in files. 👉 That’s where File Handling comes in. Python allows us to create, read, update, and delete files easily. 🔹 Common File Operations: ✔ Read a file ✔ Write to a file ✔ Append data ✔ Close a file 💡 Example: Writing to a file with open("data.txt", "w") as file:     file.write("Hello, Python!") Reading from a file with open("data.txt", "r") as file:     content = file.read()     print(content) 🔹 File Modes: ✔ "r" → Read ✔ "w" → Write (overwrites file) ✔ "a" → Append ✔ "b" → Binary mode 📌 Why it matters? File handling is used everywhere: ✔ Saving user data ✔ Logging system activities ✔ Working with reports (CSV, JSON) Without file handling, building real-world applications would be nearly impossible. 💡 Data is valuable knowing how to store and manage it is a key developer skill. 📈 Step by step, moving closer to real world development. #Python #Programming #Coding #Developers #BackendDevelopment #FileHandling #LearningJourney #Django

Multithreading vs Multiprocessing in Python — When to Use What? 👉 Choosing the wrong one can actually make your program slower. 🧠 The Core Difference 🔹 Multithreading Runs multiple threads within the same process Shares memory Best for I/O-bound tasks (waiting time) 🔹 Multiprocessing Runs multiple processes (separate memory) True parallel execution Best for CPU-bound tasks ⚠️ The Catch: GIL (Global Interpreter Lock) Python has a limitation 👉 Only ONE thread executes Python bytecode at a time So even with multiple threads: ❌ CPU-heavy tasks don’t run in parallel ⚙️ Example 🔸 Multithreading (I/O Tasks) import threading def task(): print("Running task") t1 = threading.Thread(target=task) t2 = threading.Thread(target=task) t1.start() t2.start() 🔸 Multiprocessing (CPU Tasks) from multiprocessing import Process def task(): print("Running process") p1 = Process(target=task) p2 = Process(target=task) p1.start() p2.start() 🔥 When to Use What? ✅ Use Multithreading for: API calls File handling Database operations ✅ Use Multiprocessing for: Data processing Image/video processing Machine learning workloads 👉 Threads improve efficiency (waiting time) 👉 Processes improve performance (true parallelism) #Python #Multithreading #Multiprocessing #BackendDevelopment #Performance #SoftwareEngineering

As a Data Science Solutions Engineer, one of my projects was building an internal request intake application using Shiny for Python. Coming in unfamiliar with the tool, I did what I always do. I went straight to the documentation. Most of what I needed was covered. But a few requirements from the team weren't natively supported or well documented in the Python version of Shiny. After a lot of trial and error, I figured them out and decided to write the documentation I wished had existed. The result is a three-part series covering exactly those gaps: 🔵 Part 1 — How to integrate Quill.js with Shiny for Python to enable rich text input 🔵 Part 2 — How to implement multi-page routing using Starlette 🔵 Part 3 — How to add action buttons to a dataframe, including routing to individual record pages If you're building internal tools with Shiny for Python and have hit any of these walls, I hope it saves you the trial and error it cost me. https://lnkd.in/ewY3Ui9x https://lnkd.in/eh_x2SQj https://lnkd.in/eTkK2GPT #Python #ShinyForPython

Python TIP : filter() vs List Comprehension After working with Python in production systems for years, one thing I’ve noticed is how often we need to filter data efficiently.... especially in backend services and data pipelines. A simple example: filter(lambda amount: amount > 800, transactions) What this does: • Iterates through each item • Applies the condition (amount > 800) • Returns only the matching values Example output: [900, 1300, 2200] My take after using this in real projects: • filter() is concise and works well in functional-style pipelines • It’s useful when chaining transformations (especially with map()) • That said, in many production codebases, I still prefer list comprehensions for readability Equivalent using list comprehension: [amount for amount in transactions if amount > 800] Why this matters: • Readability often beats cleverness in team environments • Consistency across the codebase is more important than personal preference • Choosing the right approach depends on context, not just syntax One quick reminder: filter() returns an iterator, so wrap it with list() if needed. After years of writing and reviewing code, I lean toward clarity first, but it’s always good to know both approaches. #Python #Programming #SoftwareDevelopment #Coding #Developer #PythonTips

Why does SQL feel harder than Python? 🤔 → Because it forces you to deal with reality. In Python/R: • Data is often already shaped • You focus mostly on analysis 🛠️📦 In SQL: • Data is fragmented across tables • You have to rebuild it before analyzing 🧩 And more importantly: → You see how your query impacts performance⚡💸 → You think about joins, structure, and efficiency → You start asking the right questions (more business-driven💼) That’s exactly what makes SQL so valuable in industry. It doesn’t just help you analyze data; it helps you understand how data is structured, how systems work, and how to think closer to real business problems. #DataAnalytics #DataScience #SQL #Python #BusinessIntelligence #DataAnalyst #DataScientist #Analytics #DataCareers

4 Python set operations every data analyst should have in their toolkit 👇 1️⃣ Union (A | B) → Combines both datasets and keeps only unique values 2️⃣ Intersection (A & B) → Returns only the common records — perfect for matching datasets 3️⃣ Difference (A - B) → Shows what exists in A but not in B — great for gap analysis 4️⃣ Symmetric Difference (A ^ B) → Finds everything that doesn’t overlap — ideal for data reconciliation I use these regularly for: ✔️ Pipeline validation ✔️ Deduplication ✔️ Quick data audits No heavy libraries. No complex joins. Just clean, efficient Python. Curious — which one do you use the most in your workflow? #Python #DataAnalytics #PythonTips #DataEngineering #DataQuality

Ready to modernize your Python data stack for 2026? ⚡️ Small, focused tooling wins. Swap slow, monolithic workflows for a lean setup: uv for high-performance async servers, Ruff for instant linting and formatting, Typer for ergonomically built CLIs, and Polars for blazing-fast columnar data processing. The result is faster feedback loops, simpler developer experience, and production-ready performance without heavy overhead. Two practical takeaways: adopt Ruff to speed up local feedback and CI, and evaluate Polars when you need parallelism and memory efficiency over pandas. Pairing Typer with an async server like uvonic or uvloop keeps interfaces clean and deployable. If you are refreshing a project template this year, focus on developer productivity first and optimize bottlenecks next. What one change would you make to your Python stack to gain the most velocity? 🧰 hashtag#Python hashtag#Polars hashtag#DevTools hashtag#DataEngineering hashtag#MLOps

One of the most common questions beginners ask is: "I’ve learned Python basics... now what?" The beauty of Python isn't just in the syntax; it’s in the incredible ecosystem of libraries that allow you to pivot into almost any field. Whether you want to build AI agents, automate your boring tasks, or dive deep into data, there is a "formula" for it. Here is a quick breakdown of the Python combinations that power the industry today: For Data Fanatics: Python + Pandas = Data Analysis 📊 For AI Pioneers: Python + LangChain = AI Agents 🤖 For Web Architects: Python + Django/Flask = Web Development 🌐 For Automation Kings: Python + Selenium/Airflow = Workflow Magic ⚙️ For Visual Storytellers: Python + Matplotlib = Data Visualization 📈 Which "formula" are you currently working on? I’m personally diving deep into the data side of things, but the more I see what’s possible with Streamlit and FastAPI, the more I realize the possibilities are endless. Let’s discuss in the comments! What’s your favorite Python library to work with right now? #Python #DataScience #WebDevelopment #Programming #TechCommunity #Automation #LearningToCode #DataAnalytics #SoftwareEngineering