Python Default Args: A Surprising Behavior

⛔ Python Tip: Don’t Use if Statements to Count Items. Use defaultdict You have a list of items, and you want to know how many times each item appears in the list; a common beginner pattern is to write something that looks like this: counts = {} for item in items: if item in counts: counts[item] += 1 else: counts[item] = 1 This works, but it's quite verbose and therefore not very Pythonic. The best way to do it is to use `defaultdict` from the `collections` module. Here is how the code will look using this method: from collections import defaultdict counts = defaultdict(int) for item in items:   counts[item] += 1 A defaultdict is a special type of dictionary from Python’s collections module. The key idea is that it automatically creates a default value for keys that do not yet exist. In this code, when we write: counts = defaultdict(int) We tell Python that if a key is missing, create it and assign it the default value returned by `int()`, which is 0. This means every new key starts at 0, and 1 is added to it if it occurs more than once. No if statement is required. This is much better: it is less verbose, requires no manual checks, and is therefore less prone to error. 🔑 You want more Python tips? Check out the Python Tips and Tricks: A Collection of 100 Basic & Intermediate Tips & Tricks Link: https://lnkd.in/eyq2Yg7F

💡 A small Python detail that can surprise many beginners. When writing: a = b = [ ] Does this create two lists or just one? ➡️ The answer: only one list is created. However, there are two variables (a and b) pointing to the same list in memory. 🔹 Execution steps: 1️⃣ Python first creates one empty list object in memory. 2️⃣ Then the variable b is assigned to reference that list. 3️⃣ After that, the variable a is also assigned to reference the same list. So in memory it looks like this: a → [ ] b → ↑ (same list) Both variables are pointing to the same object. Example: a = b = [ ] a.append(2) print(a) print(b) Output: [2] [2] Why did this happen? • a.append(2) modifies the list object itself. • Since b references the same list, the change appears in both variables. 🔹Creating two independent lists If two separate lists are needed, they must be created individually: a = [ ] b = [ ] Now each variable references a different list object: a → [ ] b → [ ] Other ways to create two independent lists in Python: a, b = [ ], [ ] a = list() b = list() a = [ ] b = a.copy() All these approaches ensure that a and b reference different list objects, so modifying one list will not affect the other. 📌 Understanding how variables reference objects in memory is an important concept when working with lists and other mutable objects in Python. #Python #PythonProgramming #Coding #LearnPython #SoftwareDevelopment

📦 Understanding Functions in Python When learning Python, Functions are very important concepts. Let’s understand them in an easy way. 🔹 What is a Function? A function is a block of code that performs a specific task. It helps us avoid writing the same code again and again. 👉 Think of a function like a machine — you give input, it gives output. ✅ Why we use Functions? 1. Code reusability 2. Makes code easy to understand 3. Reduces code length 🔹 Types of Functions 1️⃣ Predefined Functions These are already available in Python. Example: print("Hello World") len("Python") Here print and len are the predefined functions. 2️⃣ User-defined Functions These are created by the programmer itself. ✨ Syntax: def function_name(parameters): # code return value ✅ Example: def add(a, b): return a + b result = add(5, 3) print(result) 🔹 Types of User-defined Functions ✔ Function without Parameters def greet(): print("Hello Vinayak") greet() ✔ Function with Parameters def greet(name): print("Hello", name) greet("Vinayak") ✔ Function with Return Value def square(num): return num * num print(square(4)) 📚If you’re also learning Python, this concept will be the foundation for writing better programs.

Python dict: Fast Lookups and How Collisions Are Handled The "dict" is one of the most powerful and frequently used data structures in Python. It provides average O(1) time complexity for lookups, insertions, and deletions. But how does it stay so fast? Under the hood, a Python dictionary is implemented as a hash table. When you insert a key into a dictionary, Python first computes a hash value using the "hash()" function. This hash determines where the key–value pair should be placed in the internal table. However, different keys can sometimes produce the same hash index. This situation is called a hash collision. Python handles collisions using a technique called open addressing. Instead of storing multiple elements in the same bucket (like chained hash tables), Python searches for the next available slot in the table using a probing sequence. The simplified process looks like this: 1. Compute the hash of the key 2. Map the hash to an index in the table 3. If the slot is occupied, probe another position 4. Continue probing until an empty slot or the key itself is found Python dictionaries also maintain performance by resizing the table when it becomes too full. When the load factor increases, the dictionary allocates a larger table and rehashes the existing keys. This design allows Python dictionaries to remain extremely fast even with millions of entries. Understanding how collisions are handled helps explain why "dict" is both efficient and reliable for real-world applications. What’s the most interesting thing you’ve learned about Python dictionaries?

Python typing may be entering a very different phase. PEP 827 is interesting not because it adds “more types”. It introduces ways to operate on types themselves. Today Python typing works well for describing structure. But once you try to express transformations, it becomes much harder. For example, we often know what a function returns: get_field(user, "username") clearly returns `str`. But expressing that cleanly and generically in Python typing is still difficult. This proposal moves in a different direction: • conditional types • type member access • variadic generics • type-level operators • type introspection In practice, this could change how frameworks and libraries are designed. Instead of duplicating models: User UserPublic UserCreate You could define transformations once and let the type system carry them forward. That matters beyond tooling. Type systems are not only about static checks. They shape what abstractions are easy to express. I have not gone deep into every detail yet. But directionally, this looks like one of the more interesting discussions around Python typing in recent years. Curious how others see this evolving.

Python debugging that should have been the task for the day, but instead it was phrased as subdomain enumeration 😂 let me explain, the task today was in fact subdomain enumerator using python, I think that is an obvious case, or I could be wrong but anyway, the script was written as given BUT there was an issue. The original script had a valuable that was placed in the wrong place, not on purpose obviously, I think they did not have enough space so they just placed it on the line beneath it (ill explain this further) , little did they know that it would literally drain me and id be spend 3hrs trying to debug something that didn't really need me to spend that much time on it, but as the saying goes "you dont learn by doing the same thing you know, you learn by doing something new". Here is what I learned today: - Python takes variables seriously, one mistake and you will be stuck trying to fix something for 3hrs, ask me I know - Indentation happens in the beginning - Do not be afraid to ask -line breaks are also important to know (The script) try: ip = socket.gethostbyname(full_domain) At this point I kept on asking myself why is it that there is no IP address given. After writing the script, I tried running it but it kept talking about line 17, syntax error and then showed; ip = ^ at this point I knew that there was supposed to be something there but I had no idea what it was supposed to be. Keep in mind this is my first encounter with such an issue and the debuggin had begun. After numerous attempts (failing dismally at that) I decided to do the only logical thing and that is to close the laptop and go for a stroll. The Solution try: ip = socket.gethostbyname(full_domain) Little did I know that the solution to my problem was staring at me straight at my face. After numerous attempts, endless failing but relentless spirit, the answer was finally found and the task was completed. One can say at the expense of my mental health 😂 but one must focus on the lessons learned and the task completed

Flattening a list in Python can be achieved through various methods. Here are two effective techniques: **Method 1: List Comprehension** This concise approach allows you to flatten a list of lists in a single line of code. ```python lists = [[1, 2], [3, 4], [5, 6], 7, 8] flatten_list = [item for sublist in lists for item in sublist] print(flatten_list) ``` Output: ``` [1, 2, 3, 4, 5, 6, 7, 8] ``` **Method 2: Using `extend()`** This method is practical for handling lists that may contain non-list elements. It checks each item and extends the flattened list accordingly. ```python flatten_list = [] for item in lists: if isinstance(item, list): flatten_list.extend(item) else: flatten_list.append(item) print(flatten_list) ``` Output: ``` [1, 2, 3, 4, 5, 6, 7, 8] ``` Both methods effectively flatten the original list, demonstrating the flexibility of Python in handling different data structures.

🚀 Python Tip: List Comprehensions Writing clean and efficient code is an important skill for every Python developer. One powerful feature in Python is List Comprehension, which allows you to create lists in a shorter, more readable way. 🔹 Traditional Method (Using Loop): Python Copy code squared_numbers = [] for num in numbers: squared_numbers.append(num * num) print(squared_numbers) 🔹 Using List Comprehension: Python Copy code squared_numbers = [num * num for num in numbers] ✅ Why use List Comprehension? • Makes code short and clean • Improves readability • Often faster than traditional loops Example: Copy code numbers = [1,2,3,4,5] Output → [1, 4, 9, 16, 25] 💡 Small Python tricks like this can make your code clean, simple, and powerful. #Python #PythonProgramming #CodingTips #100DaysOfCode #SoftwareDevelopment #LearningPython If you want, I can also give: ✅ 10 Python image-post topics for LinkedIn ✅ Viral-style LinkedIn coding posts (which get more likes).

FUNDAMENTALS OF PYTHON It’s safe to say that Python fundamentals go far beyond just assigning variables or understanding iterations. ➠ Iteration, as the name implies, is the process of repeatedly executing a block of code using loops under specific conditions. From what I’ve learned so far: ➠ While loops handle indefinite iteration — they keep running as long as a condition remains true. ➠For loops handle definite iteratio — they run based on a sequence or a fixed range. Now here’s a real question: ➤ How many months do people spend learning functions, commands, operators, and loops… only to later call them “non-fundamentals”? Well… I’m that “someone.” And I moved past all of these about a week ago. Not because they don’t matter—but because this is my way of pushing myself forward and staying motivated for the journey ahead. To anyone on the same path as me: You’re doing well—but we can do even better. April 1st, 2026 marks the first draft on this journey. Hopefully, it won’t be the last. My Current Focus: Algorithm Building & Checking Right now, I’m diving into algorithm design and validation, which is honestly one of the most interesting parts of learning Python. It blends: ➼Basic algebra ➼ Binary thinking ➼Logical problem-solving The math itself is simple enough but improvisation using codes is where the fun begins ➤ First Concept: Guess-and-Check Algorithm (Also known as Exhaustive Enumeration) ➠ This algorithm works when: ➮ You can guess possible solutions, and ➮ You can **check if those guesses are correct** It keeps trying values until: ➮ A solution is found, or all possibilities are exhausted ➤ Simple Applications: ➠Finding square roots and cube roots of integers ➠Solving basic word problems ➠Building number guessing games and simple logic-based games This is just the beginning. More concepts, more challenges, more growth on the road to becoming a Python guru 🐍 Stay tuned. 👨💻👨💻

🖥️ Day 1 of python journey What I learned today: Variables and the 4 core data types. Python has four fundamental types that appear in every program ever written: int — whole numbers. Every user ID, every age, every count, every loop index in every application is an integer. float — decimal numbers. Every price, every percentage, every measurement is a float. One important thing I learned: 0.1 + 0.2 in Python equals 0.30000000000000004, not 0.3. This is a floating-point precision issue that causes real bugs in financial applications. Professional developers use Python's Decimal module for money calculations. str — text. Every API response your application receives, every database field it reads, every message it displays is a string. bool — True or False. The entire logic of every program — every condition, every decision, every filter — is powered by boolean values. The insight that changed how I think about Python: input() always returns a string. Always. Even if the user types 100, Python gives you "100" — the text, not the number. If you try to do arithmetic on it without casting, you get a TypeError. The fix: int(input("Enter your age: ")) — convert the string to an integer immediately. This is the very first thing that trips up beginners. I learned it on Day 1. What I built today: A personal profile program that takes 5 inputs — name, age, city, is_employed, salary — stores them in correctly typed variables, and prints a formatted summary using f-strings: f"Name: {name} | Age: {age} | City: {city}" Simple? Yes. But this exact pattern — collect input, store in typed variables, format and display output — appears in every data entry form, every registration page, every dashboard in every Python application. #Day1#Python#PythonBasic#codewithharry#w3schools.com