Python Polymorphism with Method and Operator Overloading

Building Data Science from scratch, one concept at a time! EP03 of Intelevo covers all 4 Python Operators - Arithmetic, Comparison, Logical and Assignment - explained simply by myself. Watch now: https://lnkd.in/gUtXKU6G

Mastering Tuples in Python – Simple yet Powerful! Today’s learning focused on one of the most efficient data structures in Python — Tuples 🔥 📌 Key Concepts Covered: 🔹 Tuple Packing Combining multiple values into a single tuple ➡️ Example: data = ('apple', 10, 3.5) 🔹 Tuple Unpacking Extracting values into variables easily ➡️ Example: a, b, c = data 🔹 Tuple using range() Generating sequences efficiently ➡️ Example: nums = tuple(range(1, 6)) 🔹 Tuple Comprehension (via generator) Creating tuples dynamically ➡️ Example: tuple(x*x for x in range(5)) ✨ Why Tuples? ✔️ Faster than lists ✔️ Immutable (safe & secure) ✔️ Useful for fixed data collections 📊 Learning tuples helps in writing clean, optimized, and professional Python code. Global Quest Technologies #Python #PythonProgramming #DataStructures #Tuples #CodingJourney #LearnPython #ProgrammingLife #DeveloperLife #TechSkills #Coding #PythonBasics #SoftwareDevelopment

📌 Day 08 – Problem Solving, Python Scripts & Logistic Regression Today is all about unblocking you and building real understanding. What we're tackling: 🛠️ Azure Problem-Solving Session – Real issues and errors students are facing. Bring your questions, because this is where things finally click. 🐍 Running Python Scripts in Designer – Including using a zip bundle when you have multiple files or dependencies. 📊 Logistic Regression – Not just the math. What it actually solves in the real world: Business problems that matter: 1. Two-class classification scenarios 2. Why Logistic Regression is often the first stop for binary outcomes ⚙️ Hands-On Implementation – Theory of Prep data for a two-class classification problem and implement Logistic Regression right inside Azure. By the end of Day 08: ✅ You'll troubleshoot Azure issues like a pro ✅ You'll run complex Python scripts (even with multiple files) ✅ You'll understand and implement Logistic Regression for real business problems This is where theory meets practice – and you actually build something that works. 🎥 Watch Day 08 here: https://lnkd.in/dfTDWxpi #AzureML #DP100 #LogisticRegression #PythonScripts #ProblemSolving #TwoClassClassification #AzureDataScientist

Interested in learning how the archives in the Sheridan Libraries at JHU solved our problem of how to ingest file-level metadata from a spreadsheet of over 55,000 rows of complex metadata into a collection finding aid in ArchivesSpace? Kristen Diehl, Michelle Janowiecki, and I just published an article describing our process! Find the article here: https://lnkd.in/eQEAf9pB Find our Python scripts here: https://lnkd.in/en37T-jg

Today I learned about Polymorphism in Python, and it completely changed how I think about writing flexible code. Polymorphism is all about using a single interface to work with different types of objects. In simple terms, the same method can behave differently depending on the object that calls it. For example, a speak() method can return “Woof” for a Dog and “Meow” for a Cat — same method name, different behavior. What I found really interesting is how it works behind the scenes. Python allows this through concepts like method overriding, duck typing, and operator overloading. Instead of writing separate logic for every type, we can write more general and reusable code that adapts automatically. The real-world usefulness is huge. Whether it's handling different types of files, working with multiple payment methods, or building scalable systems, polymorphism helps keep code clean, maintainable, and easy to extend. This is a powerful reminder that writing smart code isn’t about making it complex — it’s about making it adaptable. #Python #Programming #OOP #Learning #SoftwareDevelopment

50% of Python Pandas users do this: df[df['customer_age'] > 50][['cust_id', 'cust_age', 'address']] Instead this : df.loc[df['customer_age'] > 50, ['cust_id', 'cust_age', 'address']] So Which one is better? While Both yield same results Most people stop at: “just use loc, it’s cleaner.” The REAL difference is 1. One indexing operation 2. Row and column selection in a single step 3. No intermediate DataFrame creation 4. Direct reference to the original dataset If your transformation has business meaning, don’t let it be split across implicit steps. Make it explicit. Make it atomic. That’s what .loc really enforces. . . . . . . . . #Python #Pandas #DataEngineering #DataScience #CodeNewbie

Ever find yourself writing extra lines just to add data to a dictionary? Checking if a key exists before adding an item gets old. This Python trick automatically initializes your dictionary values. It cleans up your data aggregation and processing loops. ✨ It's a lifesaver for grouping features or metrics in your AI/ML workflows. What's your favorite Python shortcut for cleaning up data processing? #Python #AIDeveloper #MachineLearning #CodingTips #DataScience

Python makes data cleaning 10x faster. My standard Pandas cleaning workflow: ■ Remove duplicates ■ Handle missing values ■ Fix datatypes ■ Standardize categories ■ Outlier detection Example: ```python df.drop_duplicates(inplace=True) df['date'] = pd.to_datetime(df['date']) df.fillna(0, inplace=True) ``` Clean data = accurate insights. #Python #Pandas #DataCleaning #DataAnalyst #Automation

Python is the world's number one language for AI. It's also how most teams accidentally build their worst technical debt. We've reviewed 50+ Python codebases. The same 4 mistakes appear every time. Swipe to see what to fix before your codebase becomes a liability. → Mistake 1: No type hints → Mistake 2: Notebooks in production → Mistake 3: Unpinned dependencies → Mistake 4: Sync where you need async The best Python codebases we've worked on share one thing: They were written as if the team expected it to still be running in 5 years. Type hints. Tested modules. Pinned deps. Async where it matters. That discipline is the difference between a Python product and a Python project. Bacancy builds Python systems that scale. DM us if you're inheriting one that doesn't. #Python #PythonDevelopment #CleanCode #TechnicalDebt #SoftwareEngineering #BackendDevelopment #EngineeringLeadership #HirePythonDevelopers

Ever wondered how to fetch the maximum and minimum values from a dictionary in Python without explicitly using a for loop? Assume you have a dictionary called bids of type `dict(str, int)`: `Maximum value` max_bid_user = max(bids, key=bids.get) max_bid_price = bids[max_bid_user] print(f"Highest Bid: {max_bid_user} with price: {max_bid_price}") `Minimum value` min_bid_user = min(bids, key=bids.get) min_bid_price = bids[min_bid_user] print(f"Lowest Bid: {min_bid_user} with price: {min_bid_price}") The max and min functions allow you to pass a key parameter, which in this case is bids.get. This tells Python to evaluate dictionary keys based on their corresponding values, making it easy to retrieve the keys with the highest and lowest values. #Python #AIML #AIwithAnishArya