Python OOP: Magic Methods & Data Protection

Day 0 - #100DaysOfCode Where I am currently: Python: ✦✦✦✧✧✧ (3/6) I’ve been practicing NumPy and Pandas through isolated problems for ~2 months: - https://lnkd.in/gHU9AkWt - https://lnkd.in/g7Zy6_-h For visualization, I haven’t practiced separately. My knowledge comes from references and usage in projects: - https://lnkd.in/g7A56DqJ I’ve already gone through ML theory once and made notes, so now I just revisit them whenever I need to refresh something. I’ve completed one guided ML project. I relied heavily on guidance and spent too much time going deep into EDA, which slowed my progress. In this project: - No data cleaning (dataset was already clean) - Performed EDA: feature comparisons, correlations, histograms, boxplots - Guided feature selection based on trends and correlations - Reframed problem: good (7–8) vs bad (3–6) wine classification - Trained models: KNN, Naive Bayes, Random Forest, Logistic Regression - Evaluated using precision and recall - No deployment - Conclusion: Models performed similarly. Accuracy was limited due to class imbalance, making exact prediction difficult. Current Project: Predicting response time from NYC 311 service requests (2020+ dataset) - Using ~200k rows for simplicity - Currently in data cleaning phase Rules I follow: Not allowed: - Blindly follow tutorials - Ask "what should I do next?" - Change the problem midway Allowed: - Ask specific questions - Get stuck - Verify reasoning - Ask for code improvements only if I already understand and can implement the logic at some level (I must fully understand any improved version I use) Goal: Make decisions independently and keep the project as unguided as possible.

Python doesn’t just automate tasks. It changes how you think about problems. Example: You receive 20 Excel files every week. Manual approach: Open → Clean → Merge → Repeat Python approach: Write a script once → Process everything automatically But here’s the real shift: You stop thinking: “How do I do this?” You start thinking: “How can this run without me?” That’s the beginning of data engineering mindset Where Python helps: ✔ Data cleaning (pandas) ✔ File automation ✔ API data extraction ✔ Scheduled workflows It’s not about replacing tools. It’s about reducing repetition.

🔧 Building AI Agents from Scratch – Part 10: AI Agent Python Library Packaging is live! In this post, I explore how agents can be packaged and shared like any other Python library: ✨ From Scripts to Libraries – agents move beyond ad‑hoc scripts into structured, reusable packages. ✨ Packaging with setup.py / pyproject.toml – standard Python packaging ensures agents can be installed via pip. ✨ Wheel Files (.whl) – agents are compiled into distributable wheels, making installation fast and dependency‑safe. ✨ Distribution via Git – teams can version, share, and collaborate on agents across repositories. ✨ FastAPI Discovery Integration – packaged agents can register themselves automatically, enabling plug‑and‑play orchestration. This series continues to be based entirely on my work experience. It’s not about frameworks—it’s about learning the fundamentals and understanding what they’re built on. 👉 Read Part 10: https://lnkd.in/gAsxewjw If you’re curious about how packaging transforms agents into modular, reusable components, I’d love for you to follow along. #AI #Agents #Python #Packaging #AgenticAI #LearningByDoing

Stop writing slow Python code. 🛑If you’re still using standard Python lists for heavy data work, you’re leaving massive performance on the table. In 2026, NumPy isn't just a library—it’s the foundation of almost every AI and Data Science breakthrough we see today. From Pandas to PyTorch, it all starts here. Why is it the "Gold Standard"? 🏆1️⃣ Speed (Up to 50x Faster): While Python is easy to read, its loops are slow. NumPy runs on optimized C code, allowing you to process millions of data points in milliseconds. 2️⃣ Memory Efficiency: Unlike Python lists (which store pointers to objects), NumPy uses contiguous memory blocks. Smaller footprint = faster processing. 3️⃣ Vectorization: Forget writing for loops for every calculation. With NumPy, you can add, multiply, or transform entire datasets in a single line of code. 4️⃣ Broadcasting Power: It’s smart enough to handle arithmetic between arrays of different shapes, "stretching" data automatically to make the math work.The Bottom Line:You can't master AI or Scalable Engineering without mastering the ndarray. It’s the difference between a script that "works" and a system that "scales."Standard Python for logic.NumPy for the heavy lifting. ⚡👇 #Python #DataScience #MachineLearning #NumPy #CodingTips #SoftwareEngineering #AI

Python Basics Every Al Engineer Must Know If you're starting your Al journey, Python is your best friend Here's what I learned that actually matters 1. Variables & Data Types →int, float, string, boolean → These are the building blocks of every ML model 2. Lists & Dictionaries → Store datasets, features, and labels → df['column'] is just a dictionary in disguise! 3. Loops & Conditions → for loops to iterate over data →if/else to filter and clean data 4. Functions →Write reusable code for preprocessing. →def preprocess(df): your best habit 5. Libraries You Must Know →NumPy - numbers & arrays →Pandas - data manipulation →Matplotlib/Seaborn - visualization →Scikit-learn - ML models 6. OOP (Object Oriented Programming) →Classes & objects power every Al framework → TensorFlow, PyTorch are all built on OOP 7. File Handling →Read CSV. JSON. Excel files → pd.read_csv() is your daily driver. #Python #AIEngineering #MachineLearning #DataScience #Python4Al #LearnPython #AlBeginners

Most data analysts know Python. But not everyone uses it effectively. This image covers some advanced Pandas techniques, and honestly, these are the kind of things that make a real difference in day-to-day work. Not because they’re “advanced", but because they make your code cleaner, faster, and easier to maintain What stood out to me is Instead of writing long, step-by-step transformations, you can chain operations for cleaner pipelines, use vectorized calculations instead of loops, and combine multiple aggregations in a single step. Also, small things matter more than we think: 🔺 selecting only required columns 🔺 handling missing data thoughtfully 🔺 using proper joins instead of manual merges These don’t sound fancy, but they save a lot of time in real projects. 𝐈'𝐦 𝐡𝐨𝐬𝐭𝐢𝐧𝐠 𝐚 𝐰𝐞𝐛𝐢𝐧𝐚𝐫 𝐨𝐧 𝐀𝐩𝐫𝐢𝐥 26. 𝐌𝐨𝐫𝐞 𝐝𝐞𝐭𝐚𝐢𝐥𝐬 𝐡𝐞𝐫𝐞: 👇 https://lnkd.in/gXQZCDV8 Visual Credits: Sohan Sethi 𝑾𝒂𝒏𝒕 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆? 𝘍𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/dTK-FtG3 Follow Shreya Khandelwal for more such content. ************************************************************************ #Python #DataScience #Pandas #Analytics

Python for Everything — Why the Ecosystem Matters Python isn’t just powerful because it’s simple — it’s powerful because of its vast ecosystem. From data analysis to AI and web development, Python provides specialized libraries that make solving real-world problems faster and more efficient. Here’s where Python truly shines 🔹 Data Analysis → Pandas for data cleaning, transformation, and exploration 🔹 Machine Learning → TensorFlow & Scikit-learn for building predictive models 🔹 Data Visualization → Matplotlib & Seaborn for creating meaningful insights 🔹 Automation & Web Scraping → BeautifulSoup & Selenium for extracting and automating data 🔹 APIs Development → FastAPI for high-performance backend services 🔹 Database Integration → SQLAlchemy for seamless database management 🔹 Web Development → Flask & Django for building scalable web applications 🔹 Computer Vision → OpenCV for image and video processing 📌 Key Takeaway: Learning Python syntax is just the first step. Mastering its ecosystem is what transforms Python into a powerful problem-solving tool for Data Science, Machine Learning, and Software Development. #Python #DataScience #MachineLearning #AI #Programming #SoftwareDevelopment #CareerGrowth

🚀 Python Series – Day 14: File Handling (Read & Write Files) Yesterday, we explored advanced concepts in functions. Today, let’s learn something super practical — how Python works with files 📂 🧠 What is File Handling? File handling allows you to: ✔️ Read data from files ✔️ Write data to files ✔️ Store information permanently 👉 Used in real-world projects like logs, data storage, reports, etc. 📂 Step 1: Open a File file = open("demo.txt", "r") 👉 Modes: "r" → Read "w" → Write (overwrites file) "a" → Append "x" → Create new file 📖 Step 2: Read a File file = open("demo.txt", "r") print(file.read()) file.close() ✍️ Step 3: Write to a File file = open("demo.txt", "w") file.write("Hello, Python!") file.close() ➕ Step 4: Append Data file = open("demo.txt", "a") file.write("\nLearning File Handling 🚀") file.close() 🔥 Best Practice (Important!) Use with statement (auto closes file): with open("demo.txt", "r") as file: data = file.read() print(data) 🎯 Why This is Important? ✔️ Used in data science (CSV, logs) ✔️ Used in real-world applications ✔️ Helps manage large data ⚠️ Pro Tip: Always close files OR use with 👉 Otherwise it may cause memory issues 📌 Tomorrow: Exception Handling (Handle Errors Like a Pro!) Follow me to master Python step-by-step 🚀 #Python #Coding #Programming #DataScience #LearnPython #100DaysOfCode #Tech #MustaqeemSiddiqui

Machine Learning Graph Data using pykeen #machinelearning #datascience #graphdata #pykeen PyKEEN is a Python package for reproducible, facile knowledge graph embeddings. The fastest way to get up and running is to use the pykeen.pipeline.pipeline() function. It provides a high-level entry into the extensible functionality of this package. The following example shows how to train and evaluate the TransE model (pykeen.models.TransE) on the Nations dataset (pykeen.datasets.Nations) by referring to them by name. By default, the training loop uses the stochastic closed world assumption training approach (pykeen.training.SLCWATrainingLoop) and evaluates with rank-based evaluation (pykeen.evaluation.RankBasedEvaluator). The results are returned in a pykeen.pipeline.PipelineResult instance, which has attributes for the trained model, the training loop, and the evaluation. PyKEEN has a function pykeen.env() that magically prints relevant version information about PyTorch, CUDA, and your operating system that can be used for debugging. If you’re in a Jupyter notebook, it will be pretty printed as an HTML table. https://lnkd.in/gMPwqbWQ

🔁 Mastering Loops in Python – The Backbone of Automation Loops in python allow you to execute code repeatedly, making your programs smarter and more efficient. Let’s break it down 👇 🔹 1. for Loop (Iterating over sequences) Used when you know how many times you want to iterate. python for i in range(5): print(f"Iteration {i}") 👉 Great for lists, strings, and ranges. 🔹 2. while Loop (Condition-based looping) Runs as long as a condition is True. python count = 0 while count < 3: print("Learning Python...") count += 1 👉 Useful when the number of iterations is unknown. 🔹 3. Loop Control Statements ✔️ break → Exit loop early ✔️ continue → Skip current iteration ✔️ pass → Placeholder (does nothing) python for num in range(5): if num == 3: break print(num) 🔹 4. Nested Loops (Loop inside a loop) python for i in range(2): for j in range(3): print(i, j) 👉 Common in matrix operations, patterns, and grids. 🔹 5. Advanced Tip: List Comprehension 🚀 A more Pythonic way to write loops: python squares = [x**2 for x in range(5)] print(squares) 💡 Real-world Use Cases: ✔ Automating repetitive tasks ✔ Data processing & analysis ✔ Iterating over APIs / datasets ✔ Building logic for AI/ML models 🎯 Pro Tip: Avoid infinite loops—always ensure your loop has a stopping condition. #Python #Programming #Coding #AI #DataScience #Learning #Automati