Uncovering Python's Hidden Layers

Most Python code works. Very little Python code scales. The difference? 👉 Object-Oriented Programming (OOPS). As part of rebuilding my Python foundations for Data, ML, and AI, I’m now focusing on OOPS — the layer that turns scripts into maintainable systems. Below are short, practical notes on OOPS — explained the way I wish I learned it 👇 (No theory overload, only what actually matters) 🧠 Python OOPS — Short Notes (Practical First) 🔹 1. Class & Object A class is a blueprint. An object is a real instance. class User: def __init__(self, name): self.name = name u = User("Anurag") Used to model real-world entities (User, File, Model, Pipeline) 🔹 2. __init__ (Constructor) Runs automatically when an object is created. Used to initialize data. def __init__(self, x, y): self.x = x self.y = y 🔹 3. Encapsulation Keep data + logic together. Control access using methods. class Account: def get_balance(self): return self.__balance Improves safety & maintainability 🔹 4. Inheritance Reuse existing code instead of rewriting. class Admin(User): pass Used heavily in frameworks & libraries 🔹 5. Polymorphism Same method name, different behavior. obj.process() Makes systems flexible and extensible 🔹 6. Abstraction Expose what a class does, hide how it does it. from abc import ABC, abstractmethod Critical for large codebases & APIs OOPS isn’t about syntax. It’s about thinking in systems, not scripts. #Python #OOPS #DataEngineering #LearningInPublic #SoftwareEngineering #AIJourney

Python Data Structures and Their Tradeoffs In Python, data structures are not interchangeable. Each one reflects a deliberate tradeoff between performance, memory usage, and correctness. Here’s a breakdown of common Python data structures and their core tradeoffs: 📌 Lists (list) - Strengths: Dynamic, iterable, excellent for ordered collections and sequential access. - Tradeoffs: O(n) membership checks (in operator) and mid-list insertions/deletions. - When to use: Maintaining order, iterating over items, or when you need a simple, mutable sequence. 📌 Sets (set) - Strengths: O(1) average-time membership tests, enforce uniqueness, optimized for set operations (union, intersection). - Tradeoffs: Unordered, higher memory overhead, not indexable. - When to use: Removing duplicates, testing membership, or mathematical set operations. 📌 Dictionaries (dict) - Strengths: Key-value mapping with O(1) average lookup time, highly versatile. - Tradeoffs: Memory usage (overhead per key-value pair), keys must be hashable. - When to use: Associating data, frequency counting, caching (e.g., memoization), or fast lookups by key. 📌 Tuples (tuple) - Strengths: Immutable, memory-efficient, hashable (if all elements are hashable), thread-safe. - Tradeoffs: Cannot be modified after creation; less flexible than lists. - When to use: Fixed collections, dictionary keys, returning multiple values from functions, or when you need data integrity. Strong Python code is less about knowing what to use and more about knowing why to use it. #Python #Programming #DataStructures #CodeQuality

We are excited to introduce a framework to run reliable, accurate and reproducible 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 at scale through 𝗣𝘆𝘁𝗵𝗼𝗻 and 𝗟𝗶𝗻𝘂𝘅 𝗸𝗲𝗿𝗻𝗲𝗹 𝘁𝘂𝗻𝗶𝗻𝗴. The framework allows to organize and measure reproducible performance comparisons between popular libraries like 𝘱𝘢𝘯𝘥𝘢𝘴 and 𝘗𝘰𝘭𝘢𝘳𝘴 in a stable and objective fashion, helping teams make informed decisions based on their specific use cases. Running and measuring benchmarks is based on 𝗽𝘆𝗽𝗲𝗿𝗳, executed in Conda environments for reproducibility and to manage dependencies beyond Python. Benchmarks can be run across several independent, fully reproducible Conda environments with locked dependencies, allowing to assess performance for alternative libraries and their versions. Besides using statistical measurements over repeated execution, the benchmarking framework targets several kernel-level settings to tune the system for stable and accurate benchmarking, in order to reliably assess the raw performance of calculations. Our framework is available on GitHub as https://lnkd.in/eC2_XGH7, and you can read more in our news post https://lnkd.in/e7n-A43t #python #benchmarking #performance #polars #softwaredevelopment

🐍 "Python Is Slow" Is a Skill Issue 🐍 Everyone complains about Python being 𝕊~𝕃~𝕆~𝕎 and single-threaded. Yet Python dominates big data processing. The uncomfortable truth: When you write df.groupby().sum() in pandas, you're not running Python. You're running optimized C code that releases the GIL and executes across all your CPU cores in parallel! 🔻 NumPy? C + BLAS/LAPACK. 🔻 pandas? Cython + C++. 🔻 Polars? Pure Rust. 🔻 PySpark? JVM cluster. Python is the 𝒐𝙧𝒄𝙝𝒆𝙨𝒕𝙧𝒂𝙩𝒊𝙤𝒏 𝒍𝙖𝒚𝙚𝒓! The 𝐥𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 do the heavy lifting in languages without the GIL!  🗂️ The pattern everyone misses: 🔹 Python provides the API (clean, expressive) 🔹 C/Rust/JVM does the computation (fast, parallel) 🔹 The GIL forced this architecture 🔹 You can't be lazy with Python—use the right abstractions "Python is slow" means "I wrote for loops instead of using NumPy." Wrote a full breakdown of the GIL, why it exists (reference counting isn't thread-safe), how libraries bypass it, and why Python won despite having the worst parallelism story of any major language. 📚  Link: https://lnkd.in/gWRuqg74 ❔What's your take: is Python slow, or are we writing slow Python code?❔  #Python #GIL #BigData #DataScience #Performance #HotTake #NumPy #pandas #Programming

Python’s Global Interpreter Lock (GIL) — the most misunderstood “feature” in programming When I first started using Python, I thought: “If my CPU has 8 cores… my Python program should run 8x faster with threads, right?” Well… not exactly. What is the GIL? The Global Interpreter Lock (GIL) is a mutex (a lock) inside CPython that ensures only ONE thread executes Python bytecode at a time , even on a multi-core processor. Yes… even if you create 10 threads. Why does Python even have it? Because Python prioritizes: - memory safety - simpler memory management (reference counting) - avoiding race conditions in objects Without the GIL, Python objects (lists, dicts, etc.) could get corrupted when accessed simultaneously by multiple threads. So the GIL actually makes Python: - safer - easier to implement - stable for beginners Then why do people complain? Because of CPU-bound tasks. Example: - Image processing - Large mathematical computations - ML preprocessing - Data transformations In these cases, multiple threads do NOT run in parallel ,they take turns holding the GIL. Result , No real performance gain. But here’s the interesting part : For I/O-bound tasks (network calls, APIs, DB queries, file reading): Python releases the GIL while waiting. That means: Threading in Python works GREAT for: - web scraping - API services (FastAPI/Flask) - database calls - async applications So what should you use? CPU bound > multiprocessing / joblib / numpy (C extensions) I/O bound > threading / asyncio The Realization > Python is not slow. It is optimized for developer productivity, not raw parallel CPU execution. And once you understand the GIL, many “Python performance mysteries” suddenly make sense. Next time your threaded program doesn’t speed up… It’s not your code. It’s the lock. #Python #GIL #Programming #BackendDevelopment #SystemDesign #FastAPI #Multithreading #SoftwareEngineering

3 rules to Every Python script. Handle errors where they happen. ⚡ I write Python every single day. Pipelines. Automations. Integrations. Tools. Most engineers take hours. Not because I type faster. Because I follow 3 rules religiously. Rule 1: Start with the output. Most engineers start writing code immediately. I start with the end: → What does the final result look like? → What format? What schema? What destination? → Work backwards from there 80% of wasted code comes from unclear outputs. Rule 2: Steal structure. Write logic. I never start from a blank file. Every script follows the same skeleton: → Config at the top → Functions in the middle → Execution at the bottom → Logging everywhere Pandas. NumPy. Requests. PySpark. The libraries change. The structure never does. The structure is copy-paste. The logic is the only original work. Rule 3: Handle errors where they happen. Never raise. Catch at the source. What I avoid: → Exceptions that travel 5 layers before crashing → try/except blocks that hide problems instead of solving them → raise as the first instinct → Pipelines that explode at 3am with no context What I do instead: → Log with context — what failed, why, what input → Return gracefully or skip the row → Let the pipeline continue → Fix the root cause tomorrow with full visibility Boring code ships. Clever code stalls. The principle: Speed comes from constraint. Not from creativity. The broader point: Productivity is not talent. It is system. The engineers who ship fast are not smarter. They just eliminated decisions. What rules do you follow every time you open a new Python file? #Python #Pandas #NumPy #DataEngineering #Productivity #Programming

🐍 Ever wondered how Python actually works behind the scenes? We write Python like this: print("Hello World") And it just… works 🤯 But there’s a LOT happening in the background. Let me break it down simply 👇 🧠 Step 1: Python compiles your code Your .py file is NOT run directly. Python first converts it into: ➡️ Bytecode (.pyc) This is a low-level instruction format, not machine code yet. ⚙️ Step 2: Python Virtual Machine (PVM) The bytecode is executed by the PVM. Think of PVM as: 👉 Python’s engine that runs your code line by line This is why Python is called: 🟡 An interpreted language (with a twist) 🧩 Step 3: Memory & objects Everything in Python is an object. • Integers • Strings • Functions • Even classes Variables don’t store values. They store references 🔗 That’s why: a = b = 10 points to the SAME object. ⚠️ Step 4: Global Interpreter Lock (GIL) Only ONE thread executes Python bytecode at a time 😐 ✔ Simple memory management ❌ Limits CPU-bound multithreading That’s why: • Python shines in I/O • Struggles with heavy CPU tasks 💡 Why this matters Understanding this helped me: ✨ Debug performance issues ✨ Choose multiprocessing over threads ✨ Write better, scalable backend code Python feels simple on the surface. But it’s doing serious work underneath. Once you know this, Python stops feeling “magic” and starts feeling **powerful** 🚀 #Python #BackendDevelopment #SoftwareEngineering #HowItWorks #DeveloperLearning #ProgrammingConcepts #TechExplained

"Performance tips in Python: vectorization & memory (Part 4)" At small scale, almost any Python code “works.” Once you’re dealing with millions of rows, the difference between a loop and a vectorized operation can mean minutes vs hours. Here’s how I think about performance in real data work: 1️⃣ Stop looping over rows when you don’t have to Row-by-row for loops feel intuitive, but they’re usually the slowest option. Vectorized operations in pandas or NumPy apply logic to entire columns at once, leveraging optimized C under the hood instead of pure Python. 2️⃣ Watch your data types like a hawk Memory issues often come from heavier types than necessary: float64 when float32 is enough, or long strings where categories would work. Downcasting numeric columns and converting repeated text to category can dramatically reduce memory usage and speed up operations. 3️⃣ Process large data in chunks (or scale out) If a dataset doesn’t fit comfortably in memory, reading and processing it in chunks is often better than loading everything at once. At larger scales, pushing transformations to distributed engines (like Spark) lets Python focus on orchestration and specialized logic. 4️⃣ Measure, don’t guess Simple timing and memory checks — timing a cell, inspecting DataFrame. info(), or sampling before and after changes — turn performance from guesswork into an experiment. Over time, this builds intuition about which patterns are “cheap” and which are “expensive.” These habits don’t just make code faster — they make it more reliable when datasets grow or when a proof-of-concept script needs to become a production pipeline. 👉 If you’re working with growing datasets, start by replacing one loop with a vectorized operation and one wide numeric column with a more efficient type. You’ll feel the difference quickly. #Python #Pandas #Performance #DataEngineering #BigData #AnalyticsEngineering

🐍 Exception Handling in Python – Write Crash-Free Code! ⚠️💻 Errors happen — wrong input, missing files, division by zero… Instead of letting your program crash, Python gives you a smart way to handle errors gracefully using try–except blocks 🚀 🔹 1️⃣ What is an Exception? An exception is an error that occurs while the program is running, interrupting its normal flow. Examples: ❌ File not found ❌ Division by zero ❌ Invalid input type 🔹 2️⃣ Basic Try–Except Block Wrap risky code inside try and handle the error in except. try: x = 10 / 0 except: print("Something went wrong!") 📝 Output: Something went wrong! 🔹 3️⃣ Catch Specific Exceptions 🎯 Always try to catch specific errors instead of generic ones. try: num = int("abc") except ValueError: print("Invalid conversion!") 🔹 4️⃣ Using Else Block Runs when no exception occurs ✅ try: result = 10 / 2 except ZeroDivisionError: print("Cannot divide by zero") else: print("Result:", result) 🔹 5️⃣ Finally Block – Always Executes 🔚 Used for cleanup actions like closing files or releasing resources. try: file = open("data.txt") except FileNotFoundError: print("File missing!") finally: print("Operation completed.") 🔹 6️⃣ Why Exception Handling is Important? ✔️ Prevents program crashes ✔️ Improves user experience ✔️ Makes debugging easier ✔️ Essential for production systems ✔️ Used heavily in Data Science & automation pipelines ✨ Takeaway: Exception handling helps your program stay stable, secure, and professional even when things go wrong. If you want to write real-world Python applications — mastering try–except is a must! 🚀🐍 #Python #Programming #ExceptionHandling #TryExcept #CodingBasics #DataScience #Automation #LearningJourney #CareerGrowth #DataEngineering #Data Ulhas Narwade (Cloud Messenger☁️📨) Rushikesh Latad

Python GIL explained in simple words Python has something called the Global Interpreter Lock (GIL). It means: only one thread can execute Python code at a time inside a single process. Now, why does Python do this? 🧠 The reason Python manages memory automatically (garbage collection, reference counting). If multiple threads modified memory at the same time, it could cause crashes and corrupted data. So the GIL: Protects memory Keeps Python simple and stable Makes single-thread execution very fast Yes, this safety comes with extra memory overhead, because Python needs bookkeeping to manage threads safely. ⚡ What about performance? Here’s the important part many people miss: I/O-bound tasks (API calls, database queries, file reads): 👉 Performance is excellent because threads release the GIL while waiting. CPU-bound tasks (heavy calculations, loops): 👉 Threads won’t scale — but Python gives alternatives: Multiprocessing Async programming Native extensions (C/C++) ✅ The takeaway The GIL is not a performance bug. It’s a design trade-off: Slight memory overhead In exchange for simplicity, safety, and great real-world performance Most backend systems are I/O-heavy — and for those, Python performs just fine 🚀 #Python #GIL #Concurrency #BackendEngineering #SoftwareDevelopment