Python pathlib for File Operations Simplified

A tricky Python concept 🔍 ✅ Why does this work: t = (1, 2, [3, 4]) t[2].append(5) ➡️ After this operation, the tuple becomes: t = (1, 2, [3, 4, 5]) ❌ But this fails: t[0] = 10 🔹 Explanation In Python, tuples are immutable, meaning their structure cannot be changed. However, they can contain mutable objects. In this example: • The tuple itself is fixed • But the list inside it is mutable So: ✔ You can modify the list ❌ But you cannot reassign elements of the tuple 📌 Core Idea A tuple stores references, not the actual values. That means: • You can’t change what the tuple points to • But you can modify the object if it’s mutable 💡 The “immutability” of a tuple means that the references it holds cannot be changed after creation. You cannot change which object a specific index in the tuple points to, nor can you add or remove elements from the tuple. 📌 Key Point A tuple is immutable, but it can contain mutable objects. #Python #PythonProgramming #CodingTips #DataStructures #LearnPython

Now that Python has the clean, stripped-down blueprint (the AST) from Part 2, it needs to figure out exactly what kind of data it's dealing with before it takes action. In this video, you see Python closely examining the two 1s from your code. It has a "lightbulb" moment: it recognizes that both of these are whole numbers (which programmers call integers). This is a super important step. If those 1s were text words instead of numbers, Python would handle them completely differently. But because it confirms they are both solid numbers, it reaches into its internal toolbox and prepares the exact "math tool" designed specifically for adding integers together. Basically, Part 3 is just Python confirming the type of ingredients it has, so it knows exactly which cooking tool to use!

Most Python developers use descriptors every day without realizing it. If you've ever used: • @property • Django model fields • SQLAlchemy attributes • cached_property • or even regular Python methods …then you've already used Python descriptors. Special thanks to Haarshh Biyani for suggestions. But here’s the surprising part: 👉 Methods in Python are just functions implementing the descriptor protocol. That’s literally how self appears during method calls. In this deep dive, we unpacked: ✅ What Python descriptors actually are ✅ Data vs Non-Data descriptors ✅ The attribute lookup algorithm ✅ Why methods are descriptors ✅ How __set_name__ works ✅ How to build type validators with descriptors ✅ Avoiding memory leaks with WeakKeyDictionary ✅ The real mechanism behind method binding This article is part of a series where I’m unpacking Python internals step by step, without skipping the details that make Python such an elegant language. If you're interested in: - Python internals - Language design - Understanding how frameworks work under the hood then this one is for you. Read the full article here: https://lnkd.in/gcK3Nemb #pythonprogramming #softwareengineering #python-internals #learnpython

Python often looks simple on the surface, but its internals can still surprise even experienced developers. They definitely surprised me while writing this series. Explore the full series here https://lnkd.in/gQst2N5B #pythonprogramming #learnpython #seniorpythondeveloper

Most Python code gets harder to read as the logic gets smarter. But not 𝗧𝗼𝗼𝗹𝘇 for Python. It gives you utility functions for iterators, functions, and dictionaries that work together naturally. Toolz feels especially relevant for data work, where clean transformations can save more time than any micro optimization. What I like most is that it stays pragmatic. Just simple Python functions that help you write code that is easier to understand without giving up performance. 🔗 Link to repo: github(.)com/pytoolz/toolz --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

🐍📰 How to Flush the Output of the Python Print Function In this tutorial you'll explore output stream buffering in Python using code examples. You'll also learn the differences between block-buffered and line-buffered. #python

Python type annotations let you attach type information directly to variables, function parameters, and return values. They do not change how Python runs your code at runtime—but they transform what your editor and your teammates can understand about it before a single line executes. https://lnkd.in/g4JXgCgi #python

🐍 Python Term of the Day: soft keyword (Python Glossary) A special kind of keyword that retains its meaning only in specific contexts, while in other contexts, it behaves like a regular identifier. https://lnkd.in/gQB2Upz4

🐍 Python Challenge – Tuple Concept Check While practicing Python, I found a small but tricky question related to tuples. At first it looks simple, but many beginners make mistakes here. 📌 Question: Which of the following is NOT a tuple in Python? A) ("a", "e", "i", "o") B) (1, 2, 3, 4, 5) C) () D) (1) 💬 Comment your answer below (A / B / C / D) Don’t guess — think about how tuple syntax works. This question checks your understanding of: ✔ Tuple syntax ✔ Single element tuple ✔ Difference between tuple and integer ✔ Python basics I will reveal the answer after some responses. Let’s see who gets it right 👇 #Python #PythonBasics #CodingChallenge #Programming #DataAnalytics

For web scraping, in python we use two main libraries: " requests " and " BeautifulSoup " . requests : Used to send HTTP requests and retrieve the webpage content. > requests.get(url) BeautifulSoup: Used to parse the HTML content of the webpage and extract specific elements. > soup.find("div") #datascience #python #pandas