Mastering Python's re Library for Pattern Matching

Let’s test your understanding of Python 👇 a = [1, 2] b = a b.append(3) print(len(a)) What’s the output? 2 3 1 Error 🧠 Take 5 seconds before answering… ✅ Correct Answer: 3 Why? Because this line: b = a Does NOT create a copy. It makes b point to the same list in memory. So when we do: b.append(3) We modify the SAME object. Now the list becomes: [1, 2, 3] And since a and b reference the same object: len(a) = 3 🔥 Key Insight Lists in Python are mutable. Variables store references, not copies (for mutable objects). That’s why small misunderstandings like this cause real bugs in data & AI pipelines. Have you ever been surprised by Python references before? 👇 #Python #AI #DataScience #LearningInPublic #30DayChallenge

The power of agentsAI in performing tasks is impressive. In this example, I use natural language to generate SQL. Tools: - LLM - Python - SQL https://lnkd.in/dUUabqfu #agentAI #SQL #LLM #Python

🚀 DSA Practice – Check if Two Strings are Anagrams Today I solved a classic string problem: Checking whether two strings are anagrams. 📌 Problem Statement: Two strings are called anagrams if they contain the same characters with the same frequency, just arranged differently. 🧠 My Approach: Use a dictionary (hash map) to count character frequency Traverse the first string → increase count Traverse the second string → decrease count If all values in the dictionary are zero, the strings are anagrams ✅ ⚙️ Example: Input: "geeks" and "kseeg" Output: True ✔️ 📊 Complexity: Time Complexity: O(n) Space Complexity: O(1) (fixed alphabet size) 💻 Language Used: Python ✨ Key Learning: Using a single hash map for both strings makes the solution efficient and elegant compared to sorting-based approaches. Consistently practicing DSA to strengthen problem-solving and optimization skills 💪 #DSA #Python #Strings #CodingPractice #ProblemSolving #Placements

I’ve just published my first blog post on Medium, diving into a Python concept I recently explored: 👉 Mutable vs Immutable Objects Before this, I used to think variables simply “store values.” But in Python, they actually reference objects in memory. Having learned C previously really helped me grasp this concept more quickly, especially when it comes to understanding how memory and references work. In this post, I break down: • The difference between == and is • Mutable vs immutable objects (with clear examples) • Why l1 = l1 + [4] and l1 += [4] behave differently • How Python passes arguments to functions Writing about what I learn pushes me to go deeper and truly understand the concepts. If you’re learning Python or preparing for technical interviews, this is definitely a topic worth mastering. 📖 Read here: https://lnkd.in/guVnYN3Q #Python #SoftwareEngineering #Programming #LearningJourney #Tech

Day 23 of my #100DaysOfCode challenge 🚀 Today I implemented Linear Search in Python. Linear Search is the simplest searching algorithm where we check each element one by one until the target element is found. What the program does: • Takes a list and a target value • Iterates through the list sequentially • Returns the index if the target is found • Returns -1 if the target does not exist How the logic works: 1)Define a function linear_search(arr, target) 2)Use a for loop to iterate through the list using indices 3)Compare each element with the target value 4)If a match is found, return the current index 4)If the loop completes without finding the target, return -1 Example: Input: List:[10, 20, 30, 40, 50, 60, 70, 80, 90] Search Target 1: 50 Search Target 2: 100 Output: Target 50 found at index: 4 Target 100 not found in the list. Why Linear Search matters: – Works on both sorted and unsorted arrays – Simple and easy to implement – Time Complexity: O(n) Key learnings from Day 23: – Understanding basic searching algorithms – Comparing Linear Search vs Binary Search – Strengthening fundamentals of iteration – Improving algorithmic thinking #100DaysOfCode #Day23 #Python #PythonProgramming #LinearSearch #Algorithms #DataStructures #ProblemSolving #CodingPractice #LearnByDoing #ComputerScience #InterviewPrep #BTech #CSE #AIandML #VITBhopal #TechJourney

🐍 Python Challenge What will be the output of the following code? funcs = [lambda x: x * i for i in range(3)] print(funcs) Options: A) 0 B) 2 C) 4 D) TypeError 🧠 Many developers expect the answer to be 2 because funcs[1] seems like it should use i = 1. But the correct answer is actually: ✅ 4 💡 Why? This happens because of a concept in Python called Late Binding. Inside the list comprehension, the lambda functions do not store the value of i at the time they are created. Instead, they reference the same variable i, whose final value after the loop finishes is: i = 2 So all functions in the list behave like this: lambda x: x * 2 When we execute: funcs it becomes: 2 * 2 = 4 🎯 Key Lesson When using lambda inside loops or list comprehensions, Python captures the variable itself, not its value at creation time. 💬 Question for Python learners: How would you fix this code so that each lambda keeps its own value of i? #Python #AI #DataAnalytics #LearningInPublic #PythonTips #30DayChallenge

🚀 𝐓𝐞𝐱𝐭 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧:  Python allows storing text in variables using either double quotes (" ") or single quotes (' ') — both behave the same way. 🔹  𝐒𝐢𝐧𝐠𝐥𝐞 𝐪𝐮𝐨𝐭𝐞𝐬 𝐯𝐬 𝐃𝐨𝐮𝐛𝐥𝐞 𝐪𝐮𝐨𝐭𝐞𝐬 - Both can be used to store strings. text = "Hello" print(text) text = 'Hello' print(text) 𝘖𝘶𝘵𝘱𝘶𝘵: Hello Hello 🔹 𝐌𝐮𝐥𝐭𝐢-𝐥𝐢𝐧𝐞 𝐭𝐞𝐱𝐭 𝐮𝐬𝐢𝐧𝐠 𝐭𝐫𝐢𝐩𝐥𝐞 𝐪𝐮𝐨𝐭𝐞𝐬 text = """ Hello Python """ print(text) 𝘖𝘶𝘵𝘱𝘶𝘵: Hello Python 🔹 𝐔𝐬𝐢𝐧𝐠 \𝐧 𝐟𝐨𝐫 𝐥𝐢𝐧𝐞 𝐛𝐫𝐞𝐚𝐤𝐬 text = """ Hello\nPython """ print(text) 𝘖𝘶𝘵𝘱𝘶𝘵: Hello Python Simple features like these make Python very convenient when formatting text and printing structured output #Python #Coding

Python Concept: Shallow Copy a = [[10,20],[30,40],[50,60]] b = a.copy() print(id(a)) # Address 1000 print(id(b)) # Address 2000 a[0][0] = 200 a[0][1] = 100 print(a) # [[200,100],[30,40],[50,60]] print(b) # [[200,100],[30,40],[50,60]] Even though a and b have different memory addresses, changes in nested elements affect both. This happens because copy() makes a shallow copy, meaning the inner objects are still shared. Be careful when working with nested lists.

Python Question: # Find two indices in 'nums' whose values sum to 'target' nums = [2,7,11,15,3,6,4,5, -2] target = 9 o/p : 0 1 4 5 6 7 2 8 lookup = {}  # Dictionary to store value:index pairs for i, num in enumerate(nums):     diff = target - num  # Calculate the difference needed to reach target     if diff in lookup:  # If the difference is already in the dictionary         print(lookup[diff], i)  # Print indices of the two numbers         lookup[num] = i  # Store the current number and its index #data_engineering #python_coding 😊

In Python, everything is an object. That one fact broke my mental model this week. I'm transitioning from Software engineering to AI engineering. Week 1, Day 7 🗓️ Here's what clicked 😲. Since everything in Python is an object, every object has a unique memory address. Dict uses this to store and find values in O(1). It hashes the key, jumps directly to the slot. No scanning. No searching. But this only works if the key never changes. A mutable key changes its hash, changes its slot, and your value becomes permanently unreachable. Silent corruption 🤯 . So dict keys must be immutable. And immutable means hashable. Hashable objects carry two methods that work as a pair: __hash__() generates the slot number & __eq__() confirms the exact key at that slot. Override one without the other and Python punishes you: ``` class BrokenDog: def __eq__(self, other): return self.name == other.name # forgot __hash__ hash(BrokenDog("Rex")) # ❌ TypeError: unhashable type ``` But Python's built-in types like dict and str already override __eq__() to compare contents and not addresses. That's why two dicts with identical keys and values return True on `==`. One more thing that surprised me. Since every object in Python carries this overhead of creation, hashing, and storage, even a simple `int` is not free. That's exactly why NumPy exists to escape Python's object model and work directly with raw memory for numerical computation. For more details, refer: https://lnkd.in/gZCGFRBB What's the Python concept that surprised you most when you first learned it? #AIEngineering #Python #LearningInPublic #CareerTransition #SoftwareEngineering