Dask: Scaling Python Data Processing Beyond Memory Limits

Day 9: Python Functions as First-Class Citizens ⚙️ Mastering neat, organized code is critical for Machine Learning pipelines. Today, I did a deep dive into Python Functions, focusing on how to organize code and how Python uses computer memory: Functional Programming: Functions behave like regular data (numbers or strings). I practiced storing them in variables, giving them as inputs to other functions, and having functions create new functions. This makes processing data in steps much easier. Decomposition & Abstraction: Moving past one giant block of code to build separate "boxes" for specific tasks (like separate sections for loading data, cleaning it, and training the AI model). I focused on writing clear instructions (docstrings) inside each one. Scoping & Frame Stack: Learned exactly how Python keeps track of where variables "live." A variable created inside a function is kept separate from variables outside, preventing accidental mistakes and data mix-ups. ⚡ Arbitrary Arguments (*args): Used *args to create super flexible functions that can accept any amount of inputs. This is crucial when you don't know exactly how much data you will get, ensuring the script doesn't crash. Moving from code that "works" to code that is neat, well-documented, and ready for production. 📈 #Python #LearningInPublic #ArtificialIntelligence #SoftwareEngineering #DataPipelines #Modularity #100DaysOfCode

𝐏𝐲𝐭𝐡𝐨𝐧 𝐜𝐚𝐧 𝐛𝐞 𝐮𝐬𝐞𝐝 𝐟𝐨𝐫 𝐟𝐚𝐫 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐭𝐡𝐢𝐧𝐤. One language. Multiple possibilities. Python Certification Course :- https://lnkd.in/dVDByjuZ Here’s how different libraries expand what you can build: • Python + Pandas → Data manipulation and analysis • Python + Scikit-Learn → Machine learning models • Python + TensorFlow → Deep learning applications • Python + Matplotlib → Data visualization • Python + Seaborn → Advanced statistical visualization • Python + Flask → Web applications • Python + Pygame → Game development • Python + Kivy → Mobile apps • Python + Tkinter → Desktop GUI applications This is why Python is often the first language many people learn in tech.

Exploring Python Syntax: Your Foundation for Data & Automation. Python isn’t just a programming language it’s the language that powers data analysis, machine learning, and automation across industries. 🌐 During my journey learning Python, what's stood out to me: 1️⃣ Variables & Data Types: From integers to strings, every object in Python has a purpose. Naming matters descriptive names like student_name save hours of debugging later. 2️⃣ Functions & Conditional Logic: Reusable blocks of code (def) and conditional statements (if/elif/else) make programs flexible and powerful. 3️⃣ Operators & Expressions: Simple symbols like +, -, %, // carry immense power when you combine them creatively. 4️⃣ The Zen of Python: Beautiful is better than ugly. Readability counts. Simplicity wins. These guiding principles turn messy code into clean, maintainable solutions. 💡 Key Takeaway: Syntax + semantics are the heart of coding. The more you practice, the easier it becomes to communicate instructions to computers and to solve real world data problems efficiently. 📌 Bookmark PEP 8 and keep coding daily. Even small exercises compound into big skills over time. #Python #DataAnalytics #GrowWithGoogle #AI #Coding #LearnToCode #JupyterNotebook #DataAnalysis #TechCareers #LinkedInLearning #OOP #ZenOfPython #PythonTips #CareerGrowth #DataScience

Day 22/30 - Introduction to Numpy in python Today I learned about NumPy, which is a powerful library used for numerical operations in Python. It helped me understand how data can be handled efficiently using arrays instead of normal lists. NumPy makes calculations faster and more optimized, especially when working with large datasets. I explored how arrays work and how they are different from Python lists. One thing I found interesting is that NumPy performs operations on entire data at once without using loops. This makes the code simpler and faster. I also learned about indexing and slicing, which helps in accessing specific data easily. Working with multi-dimensional arrays gave me an idea of how structured data is handled. NumPy also provides useful functions like sum, mean, and max, which are very helpful in data analysis. I realized that it reduces the complexity of writing multiple lines of code. It is widely used in data analytics, machine learning, and scientific computing. Learning NumPy is important because it builds the foundation for advanced tools like Pandas.It helps in handling numerical data more effectively. I understood how powerful and efficient this library is in real-world scenarios. Practicing NumPy improved my understanding of data manipulation. It also helped me think more logically while working with data. Step by step, I am becoming more comfortable with Python for data analysis. This learning is helping me move closer to real-world data problems. I am excited to explore more features and apply them in projects. Fortune Cloud Technologies Private Limited #fortunecloud #BTMLayout #BengaloreStudents #BengaloreIT #DataAnalytics #Numpy Thank you Fortune Cloud Technologies

"Python's performance sucks" - Yes, but... that's not the end of the story. Can python be fast? Yes: Performance engineering in Python is not a niche concern, so it's important to be aware of the "optimization ladder" available to us, and which we can activate to gain real performance optimizations. These are some great options that you can use to drive performance gains: 1) Upgrade CPython to gain non-trivial performance gains. 2) Compile your typed python with mypyc can deliver strong wins if your code is already typed. 3) Leverage NumPy/JAX to drive massive performance gains with vectorizable array math. 4) You can use Numba to accelerate particularly for numeric loops over arrays. 5) If none of these work, then you can go low level and rebuild core components with Cython/Rust/etc. The most practically useful insight is that realistic pipelines often bottleneck on Python object creation and parsing, not just raw compute, so the biggest gains can come from changing data representations or moving parsing and hot paths out of Python objects entirely. This is a great article on practical Python performance optimizations; it's often best to go back to the foundations to drive the most value. Blog: https://lnkd.in/dp9Wm7FS --- If you liked this post you can join 70,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata

The eternal question: can an ecosystem language occupy performance, before a performant language can occupy an ecosystem? For programming languages at least, over the last decade I've gravitated to two approaches for selection. First, pick based on ecosystems. ML/AI? Python. Web? JavaScript/TypeScript. Kubernetes layer? Go. And let the industry guide you on shifts, preferring to late adopt (eg moving to React Native is all about timing). Second, if the company you work in has a dominant house language, use it, until it literally breaks.

Day 2 of learning Pandas for Data Analysis 📊 Today I practiced how to read a CSV file using Pandas in Python. In most data analysis projects, datasets are usually stored in CSV (Comma Separated Values) format, so learning how to load them into Python is one of the first and most important steps. In Pandas, we use the read_csv() function to read a CSV file and convert it into a DataFrame. Simply put, read_csv() reads data from a CSV file and loads it into a table-like structure (DataFrame) so that we can easily explore and analyze it. While practicing this, I also learned: • head() – shows the first few rows of the dataset • tail() – shows the last few rows • shape – tells the number of rows and columns in the dataset • describe() – gives summary statistics like count, mean, min, max, etc. These functions help in quickly understanding the dataset before starting deeper analysis. Here’s the small example I tried: #Python #Pandas #DataAnalytics #LearningInPublic #DataAnalysis

🚀 Day 15 – Learning Python API Integration & REST APIs Today I explored how Python interacts with REST APIs and how web services communicate over the internet. 🔹 What I learned: • Understanding REST architecture and how client-server communication works • Common HTTP methods: GET, POST, PUT, PATCH, DELETE • API endpoints and how they represent resources • Importance of status codes (200, 201, 404, 500, etc.) • How to consume APIs using Python (requests library) • Basics of building REST APIs and designing endpoints 🔹 Key takeaway: APIs are the backbone of modern applications — they allow systems to retrieve, update, and exchange data seamlessly. Working with APIs helped me understand how real-world applications connect and communicate with external services. 📚 Reference: https://lnkd.in/e9UX244q Continuing to build strong foundations in Python, Data Engineering, and AI step by step. 💻 #Python #DataEngineering #API #RESTAPI #LearningJourney #SelfLearning #AI #NewCareer

🚀 Built a Data Cleaning Tool with Python GUI 💻✨ Recently, I worked on developing a Data Cleaning Application using Python, Pandas, and Tkinter — turning raw, unstructured data into meaningful insights. From handling missing values to visualizing data before and after cleaning, this project helped me explore how real-world data preprocessing actually works. 🔹 Key Highlights: ✔ Upload and process CSV datasets ✔ Remove duplicates & handle missing values ✔ Visualize data (before & after cleaning) ✔ Download cleaned dataset with ease What made this project special? 👉 It’s not just about cleaning data — it’s about understanding how raw data transforms into actionable insights. 🔗 Project available on GitHub: https://lnkd.in/g7Kj_duN Excited to keep building, learning, and improving 🚀 #Python #DataScience #MachineLearning #Projects #Coding #StudentDeveloper #GitHub #LearningByDoing

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝗍𝘀 Python is used in software development due to its simplicity and powerful ecosystem. You can build real-world applications quickly and efficiently with Python. You can apply Python in several areas: - Automation: automate tasks like file processing and web scraping - Data Analysis: use libraries like Pandas and NumPy to process large datasets - Web Development: build APIs and backend systems with frameworks like Flask and Django - Machine Learning: use tools like TensorFlow and Scikit-learn for AI systems Source: https://lnkd.in/gxizaR-b Optional learning community: https://lnkd.in/g95CYbP3