Polars Tutorial 2: Select Expressions on Polars DataFrames скачать в хорошем качестве

Polars Tutorial 2: Select Expressions on Polars DataFrames

Polars Tutorial (Python) – Select Expressions on Polars DataFrames Welcome to the second video in this Polars for Python tutorial series! 🐻‍❄️ In this lesson, we dive into one of the most important Polars concepts: select expressions and how they power fast, readable, and scalable DataFrame transformations. You’ll learn how to select columns, rename them, perform calculations, create derived columns, and add literal (single-value) columns—all using Polars’ expressive, column-first API. Polars is a high-performance, columnar DataFrame library built on Apache Arrow and written in Rust. Its expression engine and lazy execution model make it a powerful alternative to pandas for modern data workflows. 🔹 Why Polars? Columnar memory layout (Apache Arrow) Lazy execution with automatic query optimization Fast and memory-efficient (Rust backend) Expression-based API for clean, composable transformations Designed for scaling from small to large datasets ⏱️ Video Timeline 0:00 – Intro 1:15 – Basic select 2:00 – Renaming columns with select 4:10 – Performing calculations on columns 5:25 – Creating derived columns with select 7:35 – Creating literal (one-value) columns By the end of this video, you’ll understand how Polars select expressions work and why they’re central to writing fast, idiomatic Polars code. 📌 Next up in the series: filtering, conditional logic, and lazy execution in Polars. #polars #python #dataanalysis #dataengineering #dataframes #apachearrow #pandas #lazyexecution ***Get the ColLab notebook and data here https://github.com/hthomas229/PurpleC...