Validating JSON Structure with Python

Have you ever written a perfectly “correct” Python function… that still feels slow and clumsy? Not because the logic is bad, but because it keeps doing expensive work over and over again: • re-reading files • re-parsing data • recomputing values that never change In today’s video, I walk through 10 Python features hiding in the standard library that make your code faster, clearer, and easier to reason about. Things like caching expensive operations, expressing intent more clearly, managing resources safely, and writing logic that scales without turning into spaghetti. 👉 Watch the video here: https://lnkd.in/eXcxPAQe #Python #PythonTips #ArjanCodes #CleanCode #SoftwareDesign #Pythonic

Have you ever written a perfectly “correct” Python function… that still feels slow and clumsy? Not because the logic is bad, but because it keeps doing expensive work over and over again: • re-reading files • re-parsing data • recomputing values that never change In today’s video, I walk through 10 Python features hiding in the standard library that make your code faster, clearer, and easier to reason about. Things like caching expensive operations, expressing intent more clearly, managing resources safely, and writing logic that scales without turning into spaghetti. 👉 Watch the video here: https://lnkd.in/ePuhn3VB #Python #PythonTips #ArjanCodes #CleanCode #SoftwareDesign #Pythonic

Most Python bugs don’t happen because the logic is wrong. They happen because we keep solving common, boring problems in bad ways. Some Python libraries that helped me fix this: cattrs – helps handle structured data instead of messy dictionaries hypothesis – finds bugs by testing cases you didn’t think about pyrsistent – makes shared data safer and more predictable msgspec – shows how slow normal JSON handling can be watchfiles – reliable file watching without random issues datasketch – handles large-data problems in a simple way These libraries don’t make your code fancy. They make it more stable and harder to break. #Python #CleanCode #SoftwareEngineering #ProgrammingTips #DeveloperCommunity

When doing some quick data visualization in Python, matplotlib is often the default. But, it can require quite a bit of set up and lacks default styling. Seaborn is a great Python package that uses matplotlib but is integrated with Pandas dataframes, comes with chart wrapper functions, and has several different styling themes. A few helpful methods I use often: 1. sns.despine() -> Removes extra borders along 2. sns.set_theme(style="white") -> Sets your Seaborn theme with a 2nd parameter for overriding default colors and sizes I have a getting started tutorial covering labels and styling and will post it in the comments. #Python #DataVisualization

What Really Happens When You Pass a Variable to a Function in Python? In Python, variables don’t hold values — they hold references to objects. When you pass a variable to a function: 👉 Python passes the reference, not a copy of the object. This model is often called “pass-by-object-reference” (or call by sharing). Why this confuses people? Immutable objects (int, str, tuple): Reassignment inside a function creates a new object → original stays unchanged. Mutable objects (list, dict, set): In-place modification changes the same object → caller sees the change. So it feels like: Immutable → pass by value Mutable → pass by reference But that’s just an illusion. The real rule Python always passes a reference to an object. What you do with that reference determines the outcome. #Python #ProgrammingConcepts #PythonInternals #Mutable #Immutable #CleanCode

🚀 Day 2 of #100DaysOfCode – Python Full Stack Journey Today at #Codegnan, I explored one of the most important core concepts in Python – Type Conversion & Data Types 📌 Day 2 Learnings: 🔹 Understanding input() and its default string type 🔹 Difference between Implicit and Explicit Type Conversion 🔹 Handling TypeError and ValueError 🔹 Converting between data types: int → float, str, bool float → int, str, bool str → list, tuple, set list ↔ tuple ↔ set Understanding truth values (bool() behavior) 💡 Key Takeaway: Python treats user input as a string by default. Proper type conversion is essential to avoid runtime errors and ensure smooth execution. Hands-on practice helped me understand how different data structures behave when converted into one another — especially how set() removes duplicates and how bool() evaluates values. 🔗 GitHub Link: https://lnkd.in/gchcwW95 📈 Small steps daily. Strong foundations tomorrow. #100DaysOfCode #Python #FullStackDevelopment #LearningJourney #Codegnan #Day2 #KeepLearning

🚀 Day 19/30 – Mini Python App Challenge Built a Text Search Tool 🔍 using Python. Features: • Searches word in a file • Counts total occurrences • Shows line numbers Concepts used: File handling, string operations, enumerate() GitHub 👇 🔗 https://lnkd.in/dCSFW_Hd #Python #LearningInPublic #30DaysOfCode #Automation #PythonProjects #github #smallprojects

Day 2🚀 When working with Python, data rarely comes in the format we need. Numbers may arrive as strings, decimals may need to become integers, and sometimes values must simply be displayed as text. This is where data type conversion (type casting) becomes essential. Python provides built-in functions like int(), float(), and str() to convert data from one type to another. ✨Float conversion is more flexible. Python allows both whole numbers and decimal values, even when they are stored as strings, to be converted into floating-point numbers. However, non-numeric strings cannot be converted into numbers at all and result in runtime errors. ✨String conversion is the safest of all. Any value — whether a number, decimal, or word — can be converted into a string without errors. #Python #DataScience #TypeCasting #PythonBasics #LearningInPublic #InternshipJourney

Am I too late? I just discovered match-case in Python! If you have used "switch-case" in other languages, "match-case" is Python’s way of doing something similar, but with more flexibility. It helps handle multiple conditions in a clean, readable way. Where it really comes in handy: 1. Routing logic in applications (choosing actions based on user input). 2. Handling different types of messages or events. 3. Simplifying long if / elif / else chains. 4. Working with structured data like tuples, lists, or dictionaries. Honestly, it makes your code much easier to read and maintain when there are multiple possibilities to consider. If you are just finding out about it like I did, I would definitely recommend checking it out and getting familiar with how it works, you might be surprised. If you have used it before, I’d love to hear your take on it. #Python #BackendDevelopment

Built a tiny Python CLI to check HTTP status + RTT - handy for quick smoke checks and CI sanity tests. Usage: python check.py https://google.com → prints URL STATUS RTT. Code: https://lnkd.in/ga5WBcfq Open to PRs / suggestions. #DevOps #SRE #Python #Monitoring