Python Performance: GIL and Optimized Libraries

GILs aren't just for fish. 🐟 Sometimes they're for snakes too. 🐍 Python's Global Interpreter Lock prevents parallelism. Python is slow. Python is single-threaded. So why does Python dominate big data? 🔻 Machine learning? PyTorch, TensorFlow (Python) 🔻 Data analysis? pandas, NumPy (Python) 🔻 Big data pipelines? PySpark, Dask (Python) This makes no sense! 😶🌫️ Big data demands massive parallelism, yet Python's GIL prevents exactly that. Here's the uncomfortable truth: Python doesn't process your data. NumPy, pandas, Polars, and PySpark do - and they don't have the GIL's limitations. When you write df.groupby('category').sum(), you're not running Python loops. You're calling optimized C/Rust code that releases the GIL and runs across all your CPU cores in parallel. 🗂️ What's inside: 🔹 How the GIL works (and why it exists) 🔹 The orchestration layer pattern 🔹 How NumPy, pandas, and Polars bypass the GIL 🔹 Fanout-on-write vs fanout-on-read strategies 🔹 When the GIL actually matters (and workarounds) 🔹 Python 3.13's experimental no-GIL mode  The pattern is simple: Python coordinates. C/Rust/JVM executes.  This isn't a workaround, it's architectural brilliance.  📚 Read the full article: https://lnkd.in/gWRuqg74  ❔ Have you encountered GIL-related performance issues in your Python projects? How did you solve them? ❔  #Python #DataEngineering #BigData #SoftwareEngineering #GIL #Performance #NumPy #Pandas #PySpark

Python Fundamentals Part 5: Dictionaries Just published: Building a movie showtime lookup system! What you'll learn: Dictionaries (key-value pairs) The .get() method explained Safe lookups without errors When to use dictionaries vs lists Read here: https://lnkd.in/gnby3V9G Part of my Python basics revision journey. Perfect for beginners learning data structures! #Python #Programming #LearnToCode #DataStructure

Announcing Orbital for Python 0.3.0: Accelerated Tree-Based Models in SQL We are pleased to announce the release of Orbital for Python 0.3.0, a significant update to our library designed to streamline the deployment of machine learning models for Python and Scikit-learn users. Orbital for Python allows you to transform Scikit-learn pipelines directly into native SQL queries, enabling model inference to execute within your database and eliminating the need for separate Python environments for production scoring. For those familiar with the R ecosystem, Orbital for R provides a similar capability that allows you to predict in databases using tidymodels workflows. Version 0.3.0 optimizes tree-based models, addressing the challenge of long, complex SQL queries that can be difficult for database optimizers to parse and execute efficiently. This release specifically enhances the performance and compatibility of Decision Trees, Random Forests, and Gradient Boosted Trees. Learn more about Orbital 0.3.0 and its new capabilities: https://lnkd.in/gGZqw8sA

Dictionaries are the "VLOOKUP" of Python. 📖 If Lists are columns, Dictionaries are Rows. And if you work with APIs, JSON, or NoSQL data, Dictionaries are your entire world. A lot of analysts treat them like lists, but they are much more powerful. They are "Hash Maps" - meaning they can find data instantly without looping. I built the Dictionary & JSON Data Playbook to help you master Key-Value pairs. What’s inside: ✅ Safety First: Why you should use .get() instead of brackets [] (Stop your code from crashing!). ✅ JSON Parsing: How to handle nested data (Dicts inside Lists inside Dicts). ✅ Frequency Counters: The 3-line code snippet that solves 50% of interview questions. ✅ Dict Comprehensions: Creating lookup tables in a single line. Master the Dictionary, and you master the API. Want the .ipynb file? Here you go: https://lnkd.in/gHChzGBq #DataAnalytics #Python #JSON #DataScience #Coding #APIsdr

Check out my latest article published on Towards Data Science. It gives a walkthrough on how the Py-spy profiler can be used to diagnose inefficiencies in python code, helping make your code run much faster. https://lnkd.in/gEaZ7smH

The Hidden Cost of Python Dictionaries (And 3 Safer Alternatives) This article compares and contrasts: dictionaries, named tuples, dataclasses and Pydantic. #python #pythonhowtos #programming #pydantic #pythondataclasses #pythondicts https://lnkd.in/e2NqHfCB

Day 4 of Python. Pandas begins. Today I started working with Pandas. Not to learn functions. But to understand how data behaves inside Python. The moment it clicked: Pandas is SQL-like thinking inside Python. Rows are records. Columns are attributes. Indexes define identity. What I focused on today: Series vs DataFrame Reading CSV files Understanding index and column structure Exploring data using head(), info(), and describe() This is where Python becomes useful for data work. With Pandas, I can: Clean data before it hits a database Apply business logic programmatically Prepare datasets for pipelines and ML Combine SQL thinking with Python control The goal isn’t analysis yet. The goal is structure and understanding. Next: filtering, transformations, and chaining operations. If you work with Pandas: What confused you the most when you first started — indexing or filtering? #datawithanurag #dataxbootcamp

🧠 Python Concept You MUST Know in 2026: dataclasses If you’re still writing long __init__ methods… Python has already moved on 🚀 Let’s make it super simple 👇 🧒 Simple explanation Imagine you have a student card 🪪 It has: ✨ Name ✨ Age ✨ Grade You don’t want to rewrite the card format every time. So you tell Python: “This is what a student looks like.” That’s a dataclass. ❌ Before (Old Python Way – Too Much Writing) class Student: def __init__(self, name, age, grade): self.name = name self.age = age self.grade = grade 😵 Too much typing 😵 Easy to mess up 😵 Hard to maintain ✅ After (Modern Python – dataclass) from dataclasses import dataclass @dataclass class Student: name: str age: int grade: str ✨ That’s it. Python auto-creates: ✔ __init__ ✔ __repr__ ✔ __eq__ You get clean code for free 🎁 🤯 Real-World Use Cases Dataclasses are everywhere in 2026: ✔ API request/response models ✔ Config files ✔ ML & data pipelines ✔ Backend systems Most modern Python projects expect this. 🎯 Interview Gold Line “Dataclasses reduce boilerplate and make data-focused classes clean and readable.” Short. Sharp. Senior-level. 💼 🧠 One-Line Rule If a class mainly stores data, it should be a dataclass. ✨ Final Thought (2026 Reality) In 2026, clean code is not optional. Dataclasses are no longer “nice to have” — they’re standard Python. 📌 Save this post — your future self will thank you. #Python #ModernPython #Python2026 #CleanCode #SoftwareEngineering #DeveloperLife #LearnPython

Python with Machine Learning — Chapter 9 📘 Topic: Python Class 🔍 Today, we're diving into a core concept: the Python Class. Think of a class as a blueprint for creating objects. It helps us organize our code in a clean, reusable way—like a recipe for making cookies! 🍪 **Why it matters in real-world learning:** In machine learning and data science, classes help us structure complex models and data pipelines. They make our code modular and easier to debug. Learning this now builds a strong foundation for advanced topics later. You've got this! 💪 **Constructor: Your Object's First Step** A constructor is a special method inside a class that runs automatically when you create a new object. Its job is to set up the object's initial state—like adding ingredients when you bake a cookie. In Python, the constructor is always named `__init__`. Let's see a simple example: [CODE] class Cookie: def __init__(self, flavor, color): self.flavor = flavor # Attribute set by constructor self.color = color print(f"A new {self.color} {self.flavor} cookie is ready!") # Create a cookie object choco_cookie = Cookie("chocolate", "brown") [/CODE] Here, `__init__` takes parameters `flavor` and `color` and assigns them to the object's attributes using `self`. When we create `choco\_cookie`, the constructor runs and prints a welcome message. Key takeaway: Every class can have one `__init__` constructor to initialize objects. It's your go-to tool for setting up data. Practice this in your code! Try creating your own class. Share your thoughts or questions below—I'm here to guide you. 🚀 #Python #MachineLearning #Beginners #Coding

Today’s Python focus was 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀 and 𝗧𝘂𝗽𝗹𝗲𝘀. I spent time understanding how Python handles structured data using key value pairs and fixed collections, and how this differs from lists. 𝗪𝗵𝗮𝘁 𝗜 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝗱 𝘁𝗼𝗱𝗮𝘆: • Creating dictionaries to store related data using meaningful keys • Accessing values using keys and using get() to avoid runtime errors • Updating existing values and adding new key value pairs • Deleting entries and checking for key existence • Iterating through dictionaries using keys and items() • Extracting only keys and only values when needed • Working with nested dictionaries to represent structured data • Iterating through nested dictionaries for multi level data • Using dictionaries to model real examples like contact details and revenue by region 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: • Dictionaries store data as key value pairs, making lookups fast and clear • Dictionaries are mutable, so values can be updated without recreating the structure • get() is safer than direct key access when keys may not exist • Nested dictionaries are useful for representing hierarchical data • Iterating through dictionaries helps process structured datasets efficiently I also revisited 𝘁𝘂𝗽𝗹𝗲𝘀 conceptually and understood where they fit: • Tuples are ordered and immutable • They are useful when data should not change • Often used for fixed records, configuration values, or safe data grouping Working with dictionaries made it clear how real world data like contacts, configurations, and reports are represented in Python. If you are learning Python as well, which data structure are you currently focusing on? #Python #PythonLearning #DictionariesInPython #TuplesInPython #ProgrammingBasics #LearningInPublic #DataAnalytics #Upskilling