Upgrade Your Python Skills with Polars, DuckDB, and More

Working on Real World Data Problems Using Pure Python Recently worked on a project focused on handling and analyzing structured data using core Python without relying on libraries like NumPy or Pandas. The goal was to understand the logic from the ground up. Cleaned and structured raw JSON data Built logic for “People You May Know” (mutual connections) Implemented “Pages You Might Like” recommendations Focused on problem-solving using basic data structures This approach helped me strengthen my core data handling and logical thinking, rather than depending on pre-built tools. Late nights after work, but worth it for the growth. #Python #DataProcessing #DataScience #ProblemSolving #CorePython #Algorithms #NumPy #pandas

One thing I’ve come to appreciate about Python in data work is how flexible it is. SQL is great for working with data once it’s structured. But the moment things get a bit messy.... ultiple sources, conditions, edge cases... Python makes it easier to handle. You can: pull data clean it check it test ideas quickly all in one place. It’s not about replacing SQL. It’s about having something that can handle everything around it. #Python #DataEngineering #Analytics #ETL #Tech

🚀 Exploring Python Lists – A Powerful Data Structure Recently, I learned how Python lists work in real-world scenarios, and it completely changed how I think about handling data in Python. 📌 Summary: Python lists allow us to store, manage, and manipulate multiple values efficiently. From basic operations to advanced techniques like list comprehensions, they make coding faster and more readable. 💡 Key Learnings: Lists are dynamic and can store different data types Methods like append(), remove(), and sort() make data handling easy List comprehensions help write clean and efficient code 🌍 Real-world use: Lists are widely used in applications like shopping carts, user data storage, and data analysis. 🔗 I’ve also written a detailed blog on this topic: 👉 https://lnkd.in/gT_FGa97 Excited to share my learning on Python Lists 🚀 Thanks to Mr.Vishwanath Nyathani, Mr.Raghu Ram Aduri, Mr.Kanav Bansal, Mr.Mayank Ghai, Mr.@Harsha M. Also inspired by Innomatics Research Labs learning resources #Python #Learning  #Python #DataStructures #MachineLearning #AI #LearningInPublic #Coding #Tech

This data tweak saved us hours: leveraging Python libraries like Pandas and NumPy can transform your data analysis process. In a fast-paced world, professionals often grapple with massive datasets and must find insights swiftly. The right tools can make all the difference. Pandas, with its intuitive data manipulation capabilities, allows you to clean datasets effortlessly. Imagine reducing hours of manual work to just a few lines of code. Paired with NumPy’s powerful numerical operations, you'll be equipped to handle both simple and complex analyses with ease. Visualization is where the magic happens. By using these libraries, you can quickly turn raw data into impactful visual stories, making your insights not only understandable but also compelling. Data-driven decision-making becomes a breeze. Why limit your potential? The synergy of Python, Pandas, and NumPy is a game-changer for anyone looking to elevate their data skills. Want the full walkthrough in class? Details: https://lnkd.in/gjTSa4BM) #Python #Pandas #DataAnalysis #DataScience #DataVisualization

Your Python Code Consuming Too Much Memory? Today, I explored a fundamental concept in NumPy that many of us often overlook: manual data type (dtype) . While NumPy is naturally more efficient than standard Python arrays, the way we define our data plays a massive role in actual performance. I recently followed a lecture by Respected Sir Zafar Iqbal on this topic, and it changed how I look at memory management in Data Science/ML. Here are my three key takeaways from today's practice: 1. The "Default" Memory Waste When we create an array without specifying a data type, Python often assigns the maximum possible size, such as int64, by default. If your data consists of small numbers (like 1 to 100), using int64 is a waste of resources. By simply defining dtype=np.int8, you can perform the same operations while using significantly less memory. 2. The Out-of-Bounds Trap Every data type has a specific boundary. For instance, int8 can only store values between -128 and 127. If you try to store a number like 130 in an int8 array, you will encounter an "out of bounds" error. In such cases, moving to int16 or int32 provides the necessary range while still being more efficient than the 64-bit default. 3. The Cost of "Object" Flexibility NumPy allows us to mix different types, like strings, integers, and floats, by using dtype=object. While this offers flexibility, it comes at a price: you lose the famous speed advantage that makes NumPy so powerful. For high-performance computing, keeping your data homogeneous is essential. Pro Tip: When working with large datasets, always use the .nbytes attribute to check exactly how much memory your array is consuming. Making small adjustments to your data types can transform a heavy, slow program into a super-efficient one. I am curious to hear from other data professionals: Do you usually stick with the default settings, or do you prefer manual control over your memory usage? Let me know in the comments. #Python #DataScience #NumPy #CodingLife #LearningEveryday #MachineLearning #Efficiency

Mastering Data Ingestion: Why NumPy is the Standard For anyone working with numerical data in Python, the transition from built-in functions to NumPy is a game-changer. While Python’s open() function handles basics, NumPy arrays offer a level of efficiency and speed that standard lists simply cannot match. Why use NumPy for flat files? The Industry Standard: NumPy arrays are the backbone of the Python data ecosystem. Essential for ML: If you plan to use libraries like scikit-learn, your data needs to be in a NumPy format. Built-in Efficiency: Functions like loadtxt() and genfromtxt() make importing arrays seamless. Pro-Tips for np.loadtxt() When importing data, the real power lies in the customization arguments: delimiter: Remember that the default is whitespace. For CSVs, always specify delimiter=','. skiprows: Perfect for bypassing headers (e.g., skiprows=1) so string labels don't break your numerical array. usecols: Optimization starts at ingestion. Only grab what you need by passing a list of indices, like usecols=[0, 2]. dtype: Control your data types from the start (e.g., dtype='str'). The Catch While loadtxt() is excellent for clean, uniform datasets, it hits a wall with mixed data types (like the Titanic dataset). When your columns vary between strings and floats, it’s time to level up to genfromtxt() or move into the world of Pandas. #DataEngineering #python #Numpy #Learninginpublic

Python Data Types — One Post Cheat Sheet Understanding data types is fundamental to writing efficient Python code. Here’s a quick overview: 🔢Numeric int → 10 float → 10.5 complex → 2+3j 🔤 String (str) Ordered & immutable Example: "Hello Python" 📋 List Ordered, mutable, allows duplicates Example: [10, 20, 30] 📦 Tuple Ordered, immutable Example: (10, 20, 30) 🔁 Set Unordered, no duplicates Example: {10, 20, 30} 📖 Dictionary Key–value pairs, mutable Example: {"name": "Maha", "age": 25} 🧠 Boolean True / False Used in conditions 🔍 Check Type type(variable) Choosing the right data type improves performance, readability, and data handling. #Python #DataTypes #PythonBasics #Programming #LearnPython #Coding #DataAnalytics #PythonForBeginners

Bridging the gap between SQL and Python just got easier 🚀 If you’re transitioning into data analytics or data science, understanding how SQL concepts map to Pandas in Python is a game-changer. From filtering and grouping to joins and aggregations — it’s all the same logic, just a different syntax. Master the concepts once, apply them everywhere. 💡 #DataAnalytics #Python #SQL #Pandas #Learning #DataScience

Python libraries every data analyst needs. The only Python libraries you need to start: 📊 pandas: data manipulation 📈 matplotlib + seaborn: visualization 🔢 numpy: numerical computing 📋 openpyxl: Excel automation 🔌 sqlalchemy: database connections That's it. Master these 5 and you can handle 90% of real-world analytics work. Don't get distracted by ML libraries until the basics are solid. #Python #DataAnalytics #DataTools #Pandas

Most Python classes I've seen in DS projects do too much! They load data, clean it, transform it, run the model, and log results... all in one place. It feels efficient until you need to change one thing and have to re-test everything else. That's the cost of ignoring the Single Responsibility Principle. 🐍 In my latest article, I break down what SRP actually means for Python data pipelines: https://lnkd.in/esKz_ARk This is post 1 of 5 in a series on SOLID principles applied to Data Science code. What's the messiest class you've inherited on a DS project? 👇 #Python #DataScience #SoftwareEngineering #SOLID #DataEngineering