Mastering Python Arrays with Hitesh Choudhary

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

Revising Python fundamentals so far. Covered: --> variables, references, and basic data types --> input/output handling and type conversion --> list creation, indexing, slicing, and shared reference traps --> tuples, immutability, and unpacking behavior --> sets, uniqueness, and hashability rules --> dictionaries, key behavior, and nested structures --> control flow using if / elif / else --> loops (while, for) and iteration mechanics --> loop control using break, continue, and pass

From Installing multiple Python libraries to creating one of my own……… I am excited to share the launch of my very first Python library, json-toon-fmt! What does it do? This library is designed to simplify data formatting by efficiently converting JSON data into TOON format. I built this to be a lightweight and easy-to-use tool for developers handling these specific data transformations. Give it a try: You can install it directly to your machine right now by just typing: "pip install json-toon-fmt" Project Links: PyPI: https://lnkd.in/garppHCf GitHub: https://lnkd.in/gD-tn9-2 Contributions: Open source thrives on community. Whether it's a bug report, a feature request, or a pull request, all contributions are welcomed! I’d love for you to check out the code and let me know your thoughts. #Python #OpenSource #Pip #JsonToon #SoftwareDevelopment #Coding #NewProject #DeveloperLife

𝗤. 𝗜𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗛𝗮𝘀 𝗧𝗵𝗿𝗲𝗮𝗱𝘀… 𝗪𝗵𝘆 𝗗𝗼𝗲𝘀𝗻’𝘁 𝗜𝘁 𝗦𝗰𝗮𝗹𝗲 𝗼𝗻 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗖𝗼𝗿𝗲𝘀? When I first learned about multithreading in Python, I assumed it would use all CPU cores. It doesn’t (for CPU-heavy tasks). The reason is something called the 𝐆𝐥𝐨𝐛𝐚𝐥 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐞𝐫 𝐋𝐨𝐜𝐤 (𝐆𝐈𝐋). 👉 The GIL ensures that only one thread executes Python bytecode at a time. So even if you create multiple threads: CPU-bound tasks won’t run in parallel They take almost the same time as a single thread But here’s the important nuance:- The GIL mainly affects 𝐂𝐏𝐔-𝐛𝐨𝐮𝐧𝐝 𝐭𝐚𝐬𝐤𝐬. For: - I/O-bound work (API calls, file reading, DB queries) - Network operations - Threading still improves performance. So how do we use multiple cores in Python? -> multiprocessing -> ProcessPoolExecutor -> Libraries like NumPy (which release GIL internally) My takeaway: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐆𝐈𝐋 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐝𝐞𝐬𝐢𝐠𝐧 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧. #Python #TechInterview #SoftwareEngineering #LearningInPublic #SystemDesign

Most people use strings every day in Python… but don’t fully understand what they really are. A string is simply a sequence of characters inside quotes. Example: name = "Python" But here’s what makes strings powerful: • You can access characters → name[0] → 'P' • You can slice parts → name[0:3] → 'Pyt' • You can combine strings → "Hello" + " Python" • You can repeat strings → "Hi" * 3 → 'HiHiHi' Important rule to remember: Strings are immutable — you can’t change them directly. This one concept is used everywhere: user input, file handling, APIs, automation, and interviews. Master the basics. Everything else builds on this. What Python concept took you the longest to understand? #Python #Programming #LearnToCode #PythonLearning #Coding #Developer #SoftwareEngineering #100DaysOfCode #Tech #ComputerScience

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

How can we replace getters with Python built in methods for custom objects? The answer lies in how Python Dictionary or List allows us to access data using square brackets like data[key] TL;DR: Python Container Protocol Two special methods make this possible: 1. __getitem__: Allows to use square brackets data[key] 2. __contains__: Allows to use the in keyword If __getitem__ is implemented correctly, object can even support Slicing. Python simply passes a slice object into your method instead of an integer. This is how libraries like Pandas and NumPy work their magic. These methods can replace getters inside custom Python objects. By using the Container Protocol: -> code becomes cleaner (fewer method calls). -> objects become interchangeable with standard Python dicts/lists. I’m deep-diving into the Python protocols this week and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

Built a Simple CLI Calculator with Python Today I practiced core Python fundamentals by building a small command-line calculator that performs basic operations like addition, subtraction, and multiplication based on user input. ♦️Concepts I reinforced: • User input handling • Type conversion (int) • Conditional logic (if statements) • Clean output formatting Even simple projects like this help strengthen problem-solving skills and build confidence in writing structured, readable code. Next step: expanding this into a more robust version with error handling and division support. #Python #LearningByDoing #DataAnalytics #CodingJourney #Azure #dataengineering #data

Mastering Python: A Quick Tip on lambda and filter() 🐍💡 Ever wondered how to make your code cleaner and more Pythonic for simple list operations? The lambda and filter() functions are your best friends! They are especially useful for concise, one-off functions where defining a full def block might be overkill. Here’s a simple example of how to filter even numbers from a list: python nums = [1, 2, 4, 5, 6, 7, 8, 9, 10] # Using a lambda function with filter() to get even numbers evens = filter(lambda x: x % 2 == 0, nums) print(list(evens)) # Output: [2, 4, 6, 8, 10] Use code with caution. ✅ Why this is great: Concise: Achieves a specific task in a single, readable line. Efficient: filter() returns an iterator, which is memory efficient for large datasets. Functional: Showcases a core concept of functional programming in Python. #Python #LambdaFunctions #CleanCode #PythonProgramming #TechTips #DataScience# Abhishek kumar # Harsh Chalisgaonkar # SkillCircle™