Multithreading vs Multiprocessing in Python: Shared vs Separate Memory

Python : 03 🎯String operation: formatting string Here we'll be introduced how we can combine strings from the variable using formatting string. Let's take a look- variable1 = a variable2 = b lets call a variable called 'combined string' combined_string = f"{a} {b}" [💡# Here "f" stands for 'formatting'] print(combined_string) Result: a b [ 💡 Note: In Python (and most programming languages), quotes are the "boundary" that tells the computer: "Do not process this; just treat it as plain text. "When you omit the quotes, Python looks for a variable with that name. It goes to the memory location where combined_string is stored and grabs the value.] So, that is called a formatted string! ✅ This is the modern and most efficient way to format strings in Python. It evaluates the expressions inside the curly braces and converts them to text. Make sure you follow this account for more! #python #CodingCommunity #PythonDeveloper #coding #TechCommunity #Developers #pythonprogramming

🐍 The most misunderstood line in Python is this: for item in [1, 2, 3]: Most developers think the for loop just "goes through the list". What it actually does: calls iter([1,2,3]) to get an iterator, then calls next() on it repeatedly until StopIteration is raised. That's the entire protocol. Once you understand that, generators click immediately. A generator function with yield IS an iterator — Python implements iter and next automatically. And the magic of yield is that the function pauses at each yield and resumes from there on the next call. Full guide: iterator protocol from scratch, generator functions vs expressions, yield from for delegation, lazy 5-stage file processing pipeline, context managers (enter/exit), @contextmanager, suppress, ExitStack, and send()/throw() for two-way generator communication. A generator expression uses 200 bytes. An equivalent list uses 8MB. For the same data. 📎 Free PDF. Zero pip installs — pure Python standard library. #Python #Generators #Iterators #ContextManagers #PythonProgramming #SoftwareEngineering #CleanCode #BackendDev #Programming

Python Lists vs Tuples (Mutable vs Immutable) Lists = Mutable (can change) list_1 = ['apple', 'banana', 'orange', 'grape', 'mango'] list_2 = list_1 print(list_1) print(list_2) list_1[0] = 'Watermelon' print(list_1) print(list_2) Output: Both lists change (same reference in memory) --- Tuples = Immutable (cannot change) tuple_1 = ('apple', 'banana', 'orange', 'grape', 'mango') tuple_2 = tuple_1 print(tuple_1) print(tuple_2) # This will cause error tuple_1[0] = 'Watermelon' Error: TypeError: 'tuple' object does not support item assignment Key Insights: List → change allowed Tuple → change not allowed Both allow duplicates Both are ordered #Python

🚀 Day 27 of Python Problem Solving!! Today, I worked on the classic Two Sum problem. 💡 What I Practiced Today: Traversing arrays using loops Understanding brute force vs optimized approaches Using hashmaps (dictionaries) for faster lookups Improving time complexity from O(n²) to O(n) Writing clean and efficient Python code 🧠 Problem Statement: Given an array of integers nums and an integer target, return the indices i and j such that: nums[i] + nums[j] == target and i != j. 📌 Example: Input: nums = [2, 7, 11, 15], target = 9 Output: [0, 1] ✨ I explored two approaches: 1️⃣ Brute Force using nested loops (O(n²)) 2️⃣ Optimized approach using a dictionary for constant-time lookup (O(n)) This problem helped me understand how choosing the right data structure can significantly improve performance — an important concept for coding interviews. #Day27 #100DaysOfCode #Python #CodingJourney #ProblemSolving #DataStructures #Programming #LearnToCode #TechJourney

If you've ever written a Python script that loops through a directory checking filenames one by one, the glob module is the solution to replace that. It uses the same wildcard syntax you'd use in a terminal, like *.txt for all text files, data_?.csv for single-character variations, file_[abc].log for a defined set of characters and returns matching paths in a single function call. No manual filtering or brittle string comparisons. This latest article by roadmap.sh walks you through the full picture: how each pattern actually behaves, recursive searches with **, pathlib integration, practical applications across data pipelines and automation scripts, and the common mistakes that make glob behave unexpectedly. Worth bookmarking if you regularly work with files in Python. Link in the comments.

I recently discovered assert_never in Python's typing module and it's one of those small things worth knowing about. Say you add a new status to your workflow, like "𝙘𝙖𝙣𝙘𝙚𝙡𝙡𝙚𝙙", and forget to handle it somewhere in the code. Nothing warns you. It runs fine until that status hits in production. The common fix is a raise 𝚅̲𝚊̲𝚕̲𝚞̲𝚎̲𝙴̲𝚛̲𝚛̲𝚘̲𝚛̲("𝚄̲𝚗̲𝚎̲𝚡̲𝚙̲𝚎̲𝚌̲𝚝̲𝚎̲𝚍̲ ̲𝚟̲𝚊̲𝚕̲𝚞̲𝚎̲") at the end. It works at runtime, but the type checker stays silent until something actually breaks. 𝗮𝘀𝘀𝗲𝗿𝘁_𝗻𝗲𝘃𝗲𝗿 does the same thing at runtime, and mypy and pyright will flag the branch statically, right in your IDE, before you ever run the code. Available in 𝘁𝘆𝗽𝗶𝗻𝗴 from Python 3.11+, or via 𝘁𝘆𝗽𝗶𝗻𝗴_𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻𝘀 for older versions.

If you work with Python, have you ever wondered:   • What are magic methods?   • Are they the same as special methods?   • What about “dunder methods”? First thing is, magic methods are not really magic.They are just special methods defined by the Python Data Model. And, guess what, magic methods, special methods and dunder methods are all the same thing. Amazing, right? Let’s look at a simple example: 𝗰𝗹𝗮𝘀𝘀 𝗠𝘆𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻:   𝗱𝗲𝗳 __𝗹𝗲𝗻__(𝘀𝗲𝗹𝗳):     𝗿𝗲𝘁𝘂𝗿𝗻 𝟰𝟮 Now, when you call: 𝗹𝗲𝗻(𝗼𝗯𝗷) You’re NOT calling your method directly, Python is. This is the key insight and it is very important as we are going to see on another post. Takeaway: “Magic methods” are not magic. They are contracts with the Python interpreter. Implementing such methods are going to open a lot of new doors and it is a very pythonic whay of implementing your code. #python #magicmethods #dundermethods #specialmethods

Python Empty Collections — Common Mistake [] # List () # Tuple {} # Dictionary (NOT a set) set() # Set Trick to Remember: 👉 {} = key:value → dictionary 👉 set() = only way to make empty set Example type({}) # dict type(set()) # set If you remember just this: “Curly braces empty = dict, not set” You’ll never make this mistake again. Source: https://lnkd.in/d8sbr7gc #Python

Python Strings & Formatting String Formatting f-strings (Python 3.6+, recommended – cleanest): print(f"My name is {name} and I am {age}") # My name is Joy and I am 30 print(f"Next year, I will be {age + 1}") # Next year, I will be 31 String Methods phrase = "Hello World" print(phrase.lower()) # hello world print(phrase.upper()) # HELLO WORLD print(phrase.count('l')) # 3 print(phrase.find('World')) # 6 print(phrase.replace('World', 'Universe')) # Hello Universe Key Takeaways: - Multiple ways to format strings - f-strings = cleanest & recommended - Strings are immutable - .find() gives index, .count() counts chars #Python

🔥 THIS PYTHON QUESTION LOOKS ILLEGAL BUT RUNS 🤯 Question: a = (1, 2, [3, 4]) a[2] += [5, 6] print(a) 💡 What will be the output? A. (1, 2, [3, 4, 5, 6]) B. TypeError C. (1, 2, [3, 4]) D. TypeError BUT list still gets modified