Tired of slow CSV loading and memory-hungry pandas operations? Discover Polars—the blazing-fast DataFrame library that's revolutionizing Python data analysis! In this hands-on tutorial, you'll learn how to efficiently read, filter, and visualize a real Kaggle dataset using Polars' powerful lazy evaluation engine. We'll explore the Python Programming Questions Dataset while building a memory-efficient workflow that scales to millions of rows. What you'll learn: ✅ How to lazily load large CSV files with scan_csv() (process data without loading it all into memory) ✅ Filtering and selecting specific columns using expressive Polars syntax ✅ Converting lazy queries into actionable DataFrames with collect() ✅ Pretty-printing formatted output for better data inspection ✅ Key differences between Polars and pandas for modern data pipelines Why Polars? - 10-100x faster than pandas for most operations - Lazy evaluation optimizes entire query plans before execution - Parallel processing automatically utilizes all CPU cores - Zero-copy operations minimize memory overhead - Same friendly API you already know from pandas, but supercharged Perfect for data scientists, analysts, and engineers who want to future-proof their Python skills and build production-ready data pipelines. 🔗 Dataset: 📚 Polars Docs: Prerequisites: Basic Python knowledge recommended. No prior Polars experience needed! If this tutorial helps you, hit the like button and subscribe for more cutting-edge Python data science content!