Polars Tutorial 6: Grouping and Aggregation скачать в хорошем качестве

Polars Tutorial 6: Grouping and Aggregation

Polars Tutorial (Python) – group_by and Aggregating DataFrames Welcome to the next video in this Polars for Python tutorial series! 🐻‍❄️ In this lesson, we focus on grouping and aggregating data in Polars, one of the most powerful patterns for summarizing and analyzing datasets. You’ll learn how to use group_by with Polars expressions to compute common aggregations like sums, means, counts, and multiple aggregations at once—while taking advantage of Polars’ fast, columnar execution engine. Polars is a modern, high-performance, columnar DataFrame library built on Apache Arrow and written in Rust. Its lazy execution model allows aggregations to be optimized time stam[s 0:00 intro & setup 2:05 simple group_by 5:04 multiple group_by,9:10 using len for count 10:20 group in tiers please include a list of common agg functions and my github line Here’s the full, polished description with timestamps, common aggregation functions, and your GitHub line, matching the style of the rest of your series 👇 Polars Tutorial (Python) – group_by and Aggregating DataFrames Welcome to the next video in this Polars for Python tutorial series! 🐻‍❄️ In this lesson, we focus on grouping and aggregating data in Polars, one of the most powerful techniques for summarizing and analyzing DataFrames. You’ll learn how to perform simple and multi-column group_by operations, count rows efficiently using len, and group data into tiers—all using Polars’ fast, expression-based API. Polars is a modern, high-performance, columnar DataFrame library built on Apache Arrow and written in Rust. Its lazy execution engine optimizes aggregations for speed and low memory usage, making it ideal for large datasets. 🔹 Why Polars? Columnar data model (Apache Arrow) Lazy execution with automatic query optimization Fast group_by and aggregation operations Memory-efficient (Rust-powered) Clean, expressive syntax for data summarization 🔹 Common Aggregation Functions in Polars sum() – total values mean() – average min() / max() – minimum and maximum count() / len() – row counts median() – median value std() / var() – standard deviation and variance n_unique() – number of unique values first() / last() – first or last value in each group ⏱️ Video Timeline 0:00 – Intro & setup 2:05 – Simple group_by 5:04 – Multiple group_by columns 9:10 – Using len() for counts 10:20 – Grouping data into tiers By the end of this video, you’ll understand how to group and aggregate data efficiently in Polars and how to build fast, scalable summaries for real-world analytics workflows. 📌 Up next: window functions and advanced aggregations in Polars. #polars #python #dataanalysis #dataengineering #dataframes #apachearrow #lazyexecution *Get the Colab notebook and data here: https://github.com/hthomas229/PurpleC...