Python __del__ Destructor Explained

Day 4 of 35 — File Handling & Exceptions Day 4 done! Today I learned how Python talks to the outside world — files, errors, and handling the unexpected. Here's what I covered in 1.5 hours: ✅ Reading & Writing files — the right way using with open() ✅ Working with CSV files — reading and writing structured data ✅ try / except / finally — handling errors gracefully ✅ Custom Exceptions — raising meaningful errors in your own code Biggest insight today: 👉 Always use with open() instead of manually calling f.close() Here's why: ```python # ❌ Risky — file stays open if error occurs f = open("notes.txt", "r") data = f.read() f.close() # ✅ Safe — file auto-closes even if error occurs with open("notes.txt", "r") as f:   data = f.read() ``` Also built a mini project today — read a CSV of student scores, found the highest scorer, and saved the result to a new file. Felt like real-world Python! Day 4 ✅ #Python #FileHandling #100DayChallenge #LearningInPublic #CodingJourney #PythonProgramming #SoftwareEngineering #ExceptionHandling

🧠 Python Concept That Makes Objects Behave Like Dictionaries: __getitem__ You can make your own objects work with [] indexing 👀 🤔 The Surprise Normally: data = [10, 20, 30] print(data[1]) # 20 But you can enable the same behavior in your own class. 🧪 Example class Book: def __init__(self): self.pages = ["Intro", "Chapter 1", "Chapter 2"] def __getitem__(self, index): return self.pages[index] b = Book() print(b[0]) # Intro print(b[1]) # Chapter 1 Now your object behaves like a list 🎯 🧒 Simple Explanation 📖 Imagine a book 📖 When someone asks for page 2, the librarian opens the book and gives it. 📖 That librarian = __getitem__. 💡 Why This Is Powerful ✔ Custom containers ✔ Database record access ✔ Pandas / NumPy behavior ✔ Clean APIs ⚡ Fun Fact These operators map to methods: obj[x] → __getitem__ obj[x]=y → __setitem__ del obj[x] → __delitem__ 🐍 In Python, [ ] isn’t just for lists. 🐍 Any object can support indexing 🐍 __getitem__ is the hook behind it. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

Day 6 was the most hands-on day yet. I stopped looking at Python as a collection of rules and started using it as a high-powered filter for data. Here is how Day 6 changed my perspective on Algorithms and Strings: 🔹 The Accumulator Pattern: I learned how to make a loop remember things. Whether it’s counting occurrences, summing up values, or finding the average, it’s all about maintaining a state while the loop churns through data. 🔹 The Search Party: I built logic to find the largest and smallest values in a set. Realizing that Smallest is tricky—you have to be careful with how you initialize your variables, or your starting "zero" might accidentally become your answer. 🔹 Strings are Collections: I used to think of a word as just "text." Now I see it as a sequence. I’ve learned to Slice strings to grab exactly what I need, Strip away the "noise" (whitespace), and use Parsing to extract specific data from a messy block of text. 🔹 The "In" Operator: Python’s readability shines here. Using if 'search_term' in text: feels like writing English, but it’s actually a powerful logical tool for filtering information instantly. Next up: File Handling. I’m moving from typing data manually into the console to letting Python read and analyze entire documents for me. 📂 #Python #DataAnalysis #CodingJourney #BuildInPublic #SoftwareLogic #Algorithms #StringManipulation

Every year there’s that one skill that keeps popping up… In “quick” management requests 😅 etc Python 🐍 And honestly… I used to skip it. Because “Python” sounds like programming… like you need a CS degree just to say hello 😭 But then I discovered Python in Excel and it completely changed the game. For one of my analysis projects, I literally did this: - pulled my data into Excel 📥 - wrote a tiny Python line like it’s an Excel formula ✍️ - and boom… cleaned dataset + charts + quick insights 📊✨ - even ran a simple forecast without building a whole model from scratch 🔮 No switching tools. No “export to somewhere else and hope it works.” Just… Excel doing Excel things — with Python brain power 🧠⚡

📚 𝗙𝗥𝗘𝗘 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗡𝗼𝘁𝗲𝘀 — 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟭 𝗶𝘀 𝗵𝗲𝗿𝗲! I've prepared structured study notes for Python beginners, and I'm giving them away completely free. 📖 𝑾𝒉𝒂𝒕'𝒔 𝒊𝒏𝒔𝒊𝒅𝒆: ▶ print() function — Basic syntax, end= & sep= parameters ▶ Variables & Dynamic Typing — How Python handles memory ▶ Data Types — int, float, bool, str, complex with real examples ▶ Type Conversion — Casting between data types ▶ Literals — Binary, Octal, Decimal & Hexadecimal formats ▶ User Input — Using input() with proper type casting ▶ Key Takeaways — A handy reference table for quick revision Whether you're just starting Python or revisiting the basics — these notes are built for clarity and practice. This is Part 1. More topics dropping soon! 👇 💬 Drop "NOTES" in the comments and I'll send the PDF straight to your DMs! Know someone learning Python? TAG them below — let's grow together. 🤝 🌐 www.codewavesolution.in | 📞 9179719469

I wasted 3 hours thinking my code was broken. It wasn't. The GIL was just laughing at me. I had this Python script doing heavy data processing. Added multi-threading to speed it up. Ran it. Expected magic. Got... the exact same speed. Maybe even slower. I checked my logic. Checked my threads. Googled everything. Then finally someone in a forum said — "Bro, that's just the GIL." So what even IS the GIL? In simple words — Python has this rule that says only one thread can run at a time, no matter how many cores your machine has. It's called the Global Interpreter Lock. It exists to keep Python's memory safe, but it also means your threads aren't actually running in parallel when doing heavy computation. I felt cheated honestly. But here's what actually helped me: → Switched to multiprocessing — each process has its own GIL. Problem solved. → For API calls and I/O stuff? asyncio is your best friend. → Libraries like NumPy actually release the GIL during computation. Smart. → Python 3.13 is experimenting with making the GIL optional. The future looks good. The GIL isn't evil. It's just something nobody tells you about when you're starting out — and you only discover it when you're sitting there confused at 2am wondering why your "optimized" code is still slow. If this saves even one person that 3-hour spiral, this post was worth it. Have you hit the GIL wall before? What did you do? Let me know below #Python #PythonDeveloper #Programming #LearnPython #SoftwareEngineering #CodingLife #TechCommunity

🚀Day 12 of #75DaysofLeetcode LeetCode #11 – Container With Most Water (Medium) Today I solved the classic Two Pointer problem: Container With Most Water. 🔎 Problem Summary: Given an array height, each element represents a vertical line. The goal is to choose two lines such that together with the x-axis they form a container that holds the maximum amount of water. 💡 Key Insight: Instead of checking all pairs (O(n²)), we can use the Two Pointer Technique: ✔ Start with one pointer at the beginning and one at the end ✔ Calculate the container area using area = min(height[left], height[right]) * (right - left) ✔ Move the pointer pointing to the smaller height ✔ Keep track of the maximum area ⚡ Optimal Complexity: ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) 💻 Python Implementation: from typing import List class Solution: def maxArea(self, height: List[int]) -> int: left, right = 0, len(height) - 1 max_water = 0 while left < right: area = min(height[left], height[right]) * (right - left) max_water = max(max_water, area) if height[left] < height[right]: left += 1 else: right -= 1 return max_water 📚 This problem reinforced how two pointers can reduce a brute force problem from O(n²) to O(n). Consistency in solving problems daily is slowly improving my problem-solving skills and algorithmic thinking. 💪 #LeetCode #DSA #Python #TwoPointers #ProblemSolving #CodingPractice #100DaysOfCode

Coding agents for data analysis This post walks through the complete materials from a hands-on workshop on agent-assisted data workflows, covering querying, cleaning, scraping, and visualization with Claude Code and Codex. The focus is on end-to-end interaction patterns, grounded in Python and SQLite. https://lnkd.in/db3iJSJb

Stop treating Python variables like boxes. They are actually labels. Understanding this architectural shift is the difference between writing robust code and chasing "ghost" bugs for hours. Here is how to master your data architecture in Python: Know your materials: Immutable types (like int, str, and tuple) are like stone—they cannot be changed in place. Mutable types (like list and dict) are like clay—they can be reshaped without creating a new object. The "Hashability" Price: Only immutable objects are hashable, meaning they can serve as dictionary keys or set elements. If you try to use a mutable list as a key, Python will throw a TypeError. The Default Argument Trap: Never use a mutable type (like []) as a function's default argument. These are created only once at definition, meaning every call to that function will share and modify the same list. Copy with Caution: When dealing with nested structures, a "shallow copy" only creates a new outer container while sharing the inner objects. Always be explicit and use copy.deepcopy() if you need a completely independent version. The Golden Rule: Use is None for identity checks rather than == None to ensure faster, more reliable null handling. Are you building with stone or clay today? #Python #DataArchitecture #CodingTips #SoftwareEngineering

 Day 9/100: Mastering Python Dictionaries & Data Nesting! Today was all about organizing complex data. I moved from simple Lists to Dictionaries, which allow for much more powerful data management using Key-Value pairs. What I covered today: Dictionary Basics: Creating, editing, and wiping dictionaries. The "Secret" of Nesting: Placing Lists inside Dictionaries and even Dictionaries inside Dictionaries! Looping: How to efficiently iterate through keys and values. Daily Project: Secret Auction (Bidding System) I built a blind auction program where multiple users can enter their names and bids. The program keeps the bids secret and automatically identifies the highest bidder at the end. This project was a great test of my ability to manage user input within nested data structures. Check out the "Secret Auction" code here: https://lnkd.in/edbJz2bW #Python #100DaysOfCode #DataStructures #Dictionaries #CodingJourney #BackendDevelopment