Data Science
Intermediate
Polars for High-Performance Data Processing
Accelerate Python data transformations using the lightning-fast Polars Rust engine.
Course Overview
Replace sluggish Pandas code with lazy evaluation queries, multi-threaded execution, and memory-efficient streaming operations for gigabyte-scale datasets.
What You Will Learn
Process multi-gigabyte datasets fast using Polars multi-threaded Rust execution
Optimize queries using Polars LazyFrame query planner and predicate pushdown
Master Polars expression syntax for complex aggregations and transformations
Execute out-of-core streaming queries that exceed available system RAM
Migrate existing Pandas codebases to idiomatic Polars for 10x–50x speedups
Tools & Technologies Used
Polars
Python 3.11
PyArrow
Jupyter Notebooks
VS Code
Structured Curriculum
2 Modules · 4 Lessons · 135 Minutes Total
Module 1
Module 1: Polars Expressions & Eager vs. Lazy Execution
2 lessons
Module 1: Polars Expressions & Eager vs. Lazy Execution
Master expression contexts and query optimization.
-
Polars Expression Syntax & Column Selection
Write vectorized, composable Polars expressions without lambda functions.
-
The Lazy API & Query Optimizer
Inspect query execution plans, predicate pushdown, and projection pruning.
Module 2
Module 2: Streaming & Out-of-Core Processing
2 lessons
Module 2: Streaming & Out-of-Core Processing
Process datasets larger than memory and execute complex joins.
-
Streaming Engine for Out-of-Core Data
Process 20GB Parquet files on an 8GB RAM machine using `streaming=True`.
-
High-Speed Joins & GroupBy Windowing
Execute parallel joins, rolling window calculations, and pivot operations.
Practical Project & Capstone Outcome
🚀 Capstone Project
High-Throughput Analytics Pipeline Migration
Refactor a legacy 100-line Pandas ETL script into a high-speed Polars LazyFrame pipeline, reducing execution runtime from 5 minutes to under 8 seconds.
Prerequisites
- Python intermediate proficiency
- Experience with Pandas or SQL
Intended Audience
- Data Analysts and Engineers struggling with slow Pandas dataframes
- Data Scientists handling large tabular datasets locally
Instructor Information
S
Sofia Rossi
Course Author & Industry Expert
Sofia Rossi is a High-Performance Data Engineer who specializes in memory-efficient Python data infrastructure and Rust integrations.
Frequently Asked Questions
Is Polars fully compatible with Pandas syntax?
Polars has an intentional, cleaner API designed for speed rather than 1:1 Pandas syntax clone.