Polars Tutorial 8: Basic Data Types and Casting скачать в хорошем качестве

Polars Tutorial 8: Basic Data Types and Casting

Polars Tutorial (Python) – Data Types and Casting in DataFrames 🐻‍❄️ Welcome back to the Polars for Python tutorial series! In this lesson, we dive into Polars basic data types, creating DataFrames with multiple types, and casting between them. You’ll learn how to define schemas for your data, handle missing values safely, and convert columns between types using Polars’ fast, Rust-powered, expression-based API. Polars is a modern, high-performance, columnar DataFrame library built on Apache Arrow, offering lazy execution, memory efficiency, and blazing-fast operations—perfect for both small and large datasets. 🔹 Why Polars? Columnar data model (Apache Arrow) Lazy execution with automatic query optimization Fast type casting and null-safe operations Memory-efficient and Rust-powered Clean, expressive syntax for data manipulation 🔹 Topics Covered / Timeline 0:00 – Intro & setup 1:40 – Creating a DataFrame schema with multiple types 2:37 – Creating a multi-type DataFrame 3:53 – Casting columns: safe vs strict 4:48 – Handling nulls: safe vs strict 🔹 Polars Basic Data Types Int32 / Int64 – integers Float32 / Float64 – floating-point numbers Utf8 – strings Boolean – True/False Date / Datetime – date & time Categorical – optimized string-like type 🔹 Casting Methods .cast(pl.Int32) – cast to integer .cast(pl.Float64) – cast to float .cast(pl.Utf8) – cast to string .cast(pl.Categorical) – convert string to categorical Safe casting avoids errors for nulls; strict casting raises exceptions By the end of this video, you’ll know how to define DataFrames with multiple data types, safely cast columns, and handle null values, laying the foundation for more advanced Polars operations like group_by, aggregation, and window functions. 📌 Up next: string methods and parsing Polars DataFrames #polars #python #dataanalysis #dataengineering #dataframes #apachearrow #lazyexecution #casting #datatypes Get the Colab notebook and data here: https://github.com/hthomas229/PurpleC...