Polars Tutorial 3: Filtering Polars DataFrames скачать в хорошем качестве

Polars Tutorial 3: Filtering Polars DataFrames

Polars Tutorial (Python) – Filtering Polars DataFrames Welcome to the third video in this Polars for Python tutorial series! 🐻‍❄️ In this lesson, we focus on filtering data in Polars, covering both lazy and eager evaluation and showing how Polars optimizes filters for performance. You’ll learn how to apply basic filters, combine multiple conditions, filter string columns, and work with boolean logic—all using Polars’ powerful expression-based API. Polars is a modern, high-performance, columnar DataFrame library built on Apache Arrow and written in Rust. Its lazy execution engine enables predicate pushdown and query optimization, making filtering fast and memory-efficient even on large datasets. 🔹 Why Polars? Columnar data model (Apache Arrow) Lazy execution with automatic query optimization Predicate pushdown for efficient filtering Fast and memory-efficient (Rust-powered) Clean, expressive syntax for complex conditions ⏱️ Video Timeline 0:00 – Setup 1:30 – Lazy evaluation 3:12 – Eager evaluation 5:35 – Basic filtering 6:35 – Filtering with multiple conditions 8:04 – String filtering 10:20 – Boolean filtering By the end of this video, you’ll understand how filtering works in Polars and how to choose between lazy and eager execution for real-world data workflows. 📌 Up next: adding custom columns using with_columns in Polars. #polars #python #dataanalysis #dataengineering #dataframes #lazyexecution #apachearrow ***Get the ColLab notebook and data here https://github.com/hthomas229/PurpleC...