Python List pop(0) Performance Issue and Deque Solution

The only Python tool you need in 2026? 🐍 The Python ecosystem has been fragmented for years. We used pyenv for versions, venv for isolation, pip for packages, and pip-tools for locking. uv changes everything. Here is your quick command guide: 📦 Start a project: uv init ➕ Add a library: uv add requests 🏃 Run a script: uv run main.py 🐍 Install Python 3.13: uv python install 3.13 🛠 Run a tool (like Ruff): uvx ruff check It’s a drop-in replacement for pip, so you can even use uv pip install -r requirements.txt for an immediate speed boost. The Verdict: If you value your time, uv is a non-negotiable upgrade to your workflow. #PythonProgramming #CodingTips #DevOps #BackendDevelopment #uvPython

Python List Methods Tip: append() and extend() Most Python Beginners Don’t Realize This List Mistake, append() and extend() look almost the same… But using the wrong one silently changes your data structure. Here’s the real difference: - append() adds the entire object as ONE element. - extend() adds each element individually. That means this: - append() → Creates nested lists - extend() → Keeps list flat Why This Matters: - This small mistake often causes unexpected bugs while looping, filtering, or processing data. - Many developers only notice it when their logic suddenly stops working. Simple Rule To Remember: - If you want to add one item → append() - If you want to merge items → extend() Small concepts like this make your Python code cleaner and easier to debug. Have you ever accidentally created a nested list using append()? #Python #LearnPython #PythonTips #Programming #Coding #SoftwareEngineering #PythonDeveloper

🚀 Python 3.14 — Back to the Interpreter. Back to the Basics. Today I went back to where everything starts: An Informal Introduction to Python. https://lnkd.in/d4NN7cmG # Launch Python 3.14 explicitly (Windows launcher) C:\Users\John> py -3.14 # This is a comment → ignored by Python # Remember. This is a comment. # This is NOT a comment because it's inside quotes text = "# This is not a comment." # Addition 7 + 4 # Subtraction 50 - 37 # Order of operations (multiplication first) (100 - 5 * 7) # True division → float 17 / 3 # Floor division → integer 17 // 3 # Modulo → remainder 17 % 3 # Exponentiation 2 ** 10 # Store resolution values width = 1920 height = 1080 # Calculate total pixels (Full HD) width * height 💥 Fail Fast # Access undefined variable size → NameError 🔁 REPL Superpower: _ # `_` holds the last result in interactive mode width - _ 🎯 My Take Deep systems aren’t built on complexity. They’re built on mastery of fundamentals. Whether you’re building: A Django backend A distributed system An AI-powered application It all starts here — with clean thinking. “If you want to fly high, take a deep dive.” #Python #Django #Backend #SoftwareDevelopment #DeepDive

🧠 Python Concept That Changes Instance Checks: __instancecheck__ & __subclasscheck__ You can redefine what isinstance() means 👀 🤔 The Surprise Normally: isinstance(obj, MyClass) Python checks inheritance. But classes can override this logic. 🧪 Example class Even: def __instancecheck__(self, instance): return isinstance(instance, int) and instance % 2 == 0 even = Even() print(isinstance(4, even)) # True print(isinstance(5, even)) # False Now “Even” behaves like a virtual type 🎯 🧒 Simple Explanation 🎟️ Imagine a club 🎟️ Guard doesn’t check family. 🎟️ He checks: “Are you even?” 🎟️ That rule = __instancecheck__. 💡 Why This Is Powerful ✔ Virtual types ✔ Flexible APIs ✔ Type systems ✔ Plugin interfaces ✔ Advanced frameworks ⚡ Related Hook __subclasscheck__(cls, subclass) Controls issubclass(). 🐍 In Python, type checks aren’t fixed 🐍 Classes can redefine what “instance of” means. 🐍 __instancecheck__ turns types into behavior rules. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

Python fundamentals made simple. Starting Python becomes much easier when the basics are clear. I’m sharing a carousel that walks through the core building blocks step by step, so the logic behind the code is clear. Part 1 covers: - How to use print() for output - How to take user input with input() - What variables are and how to name them correctly - Basic data types: int, float, str, bool, and None - Type conversion and type casting - Arithmetic, relational, assignment, and logical operators - Operator precedence (which operation runs first) - A small practice program to calculate the average of two numbers. Each slide focuses on one idea with simple examples so it’s easy to revise and apply. These slides are for anyone who wants strong foundations without confusion. More parts coming next. Right now... If you were starting Python again, would you focus more on basics or jump into advanced tools? ♻️ Repost & help others

Python Fundamentals Made Simple by Fahad Farooq— an essential guide for anyone taking their first steps into the world of programming.

A circular reference happens when two or more objects reference each to other. For example: list A contains a reference to list B and B contains ref back to list A. This creates a cycle. The problem is that Python mainly uses reference counting to delete Objects. An object is removed only when it's reference count becomes 0. In a circular reference, each object still has at least one reference form the other, so their reference count never reaches 0, and they are automatically deleted. This can cause memory to stay used longer than needed. How can python delete this circular reference ? the solution is Python's Garbage collector (GC). It can delect groups of objects that are no longer reachable from the program, even if they reference each other, and it removes them to free memory very simple example : import GC a = [] b = [] a.append(b) b.append(a) del a del b GC.collect() #python #garbageCollector #(GC) #dev #deeplearning #programmation #pythonDev

I’ll admit it: early in my Python journey, I spent hours debugging code that looked fine. Functions returning the wrong value, variables mysteriously “disappearing,” and weird side effects… all because I didn’t fully understand Python variable scope. Once I got it, my code became cleaner, easier to debug, and way more predictable. I turned that hard-earned lesson into a short, practical guide that walks you through local, global, and nonlocal variables with real examples. 👉 Check it out here: https://lnkd.in/djp6HJdD If you’re serious about improving your Python fundamentals, this guide is a simple way to save hours of frustration. #Python #LearnPython #CodingTips

🐍 Python Concept I Use Often: Dictionary vs Defaultdict One small choice in Python can make your code cleaner, faster, and less error-prone. Problem Counting occurrences in a list using a normal dictionary usually looks like this: counts = {} for item in data: if item in counts: counts[item] += 1 else: counts[item] = 1 It works—but it’s verbose and easy to mess up. Better Approach Using defaultdict from collections: from collections import defaultdict counts = defaultdict(int) for item in data: counts[item] += 1 Why this matters ✔ Removes conditional checks ✔ Improves readability ✔ Reduces chances of KeyError ✔ Scales well in data processing pipelines Curious—what’s your go-to Python feature that instantly improves code quality? #Python #PythonDeveloper #CleanCode #BackendDevelopment #DataEngineering #ProgrammingTips #SoftwareEngineering

🐍 Ever wondered what REALLY happens when you run a Python program? You type: 👉 python app.py But behind the scenes… a LOT is happening 👀 🔹 1. Python Interpreter Starts Python launches the interpreter (CPython for most of us). It reads your file line by line. 🔹 2. Code → Bytecode Your Python code is converted into **bytecode** (.pyc files). 👉 This makes execution faster next time. 🔹 3. Python Virtual Machine (PVM) The bytecode runs inside the PVM. This is where loops, conditions, functions actually execute. 🔹 4. Memory Management Python automatically: ✔ allocates memory ✔ tracks object references ✔ clears unused objects (Garbage Collection ♻️) 🔹 5. C Under the Hood Most Python operations are powered by **C code** That’s why Python feels simple but still powerful 💪 ✨ That’s the magic: Simple syntax on the surface, serious engineering underneath. 💡 Knowing this helps you: • Write faster code • Debug better • Understand performance issues #Python #BackendDevelopment #SoftwareEngineering #Programming #LearnPython #TechSimplified