Polars Tutorial 4: with_columns: Add Custom Columns to a DataFrame скачать в хорошем качестве

Polars Tutorial 4: with_columns: Add Custom Columns to a DataFrame

Polars Tutorial (Python) – with_columns: Adding Custom Columns to a DataFrame Welcome to the next video in this Polars for Python tutorial series! 🐻‍❄️ In this lesson, we focus on using with_columns to add and modify columns in Polars DataFrames—one of the most common and powerful transformation patterns in Polars. You’ll learn how to add custom columns, use variables to generate multiple columns efficiently, and create conditional columns using pl.when().then().otherwise(), all while taking advantage of Polars’ expression-based API. Polars is a modern, high-performance, columnar DataFrame library built on Apache Arrow and written in Rust. Its lazy execution model and expression engine allow complex column transformations to be optimized and executed efficiently. 🔹 Why Polars? Columnar data model (Apache Arrow) Lazy execution with automatic query optimization Expression-based transformations for clean, composable code Fast and memory-efficient (Rust-powered) Designed for scalable, real-world data workflows ⏱️ Video Timeline 0:00 – Intro & setup 2:05 – Adding a custom column with with_columns 5:25 – Using variables to add multiple columns 9:31 – Adding a conditional column with pl.when().then().otherwise() By the end of this video, you’ll understand how with_columns works in Polars and how to build expressive, efficient column transformations without sacrificing performance. 📌 Up next: sorting and limiting results in Polars. #polars #python #dataanalysis #dataengineering #dataframes #apachearrow #lazyexecution *Get the Colab notebook and data here: https://github.com/hthomas229/PurpleC...