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Polars from Zero

This course is part of Rust for Data Engineering Specialization

Instructors: Noah Gift

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Configure and use the Polars Rust crate with the lazy and csv feature flags to build typed DataFrames over Apache Arrow memory
Apply Polars expressions to clean and aggregate the wine-ratings dataset, with documented null-handling, normalization, and predicate-filter rules
Build wine-pipeline, three Rust CLI binaries that realize a bronze, silver, gold medallion architecture over a shared SQLite database

Skills you'll gain

Tools you'll learn

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This course is part of the Rust for Data Engineering Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

Polars is a fast columnar DataFrame engine built on Apache Arrow, and this course teaches you to use it from Rust to do real data-engineering work. You will configure a Cargo project with the lazy and csv feature flags, load wine-ratings.csv into a typed DataFrame, and learn the difference between eager DataFrames for exploration and lazy LazyFrames for production. You will compose select, filter, slice, sort, group_by, agg, and join expressions, then read explain output to see predicate pushdown and projection pushdown rewrite your query before it runs. Module 2 puts the API to work cleaning a real wine-ratings dataset with documented drop, fill, and normalize rules. Module 3 wires everything into wine-pipeline, three Rust CLI binaries that implement a bronze, silver, gold medallion architecture over a shared SQLite database and export a top-10 grape leaderboard as CSV and JSON. By the end you will have a complete, runnable Rust pipeline you can adapt to any tabular dataset.

Module details

Polars in Rust over the Apache Arrow columnar memory layout, set against pandas as a reference. Cargo setup with the lazy and csv feature flags, the DataFrame and Series types, the col expression, CSV reading with header inference and schema overrides, and the eager versus lazy execution model with predicate and projection pushdown.

What's included

16 videos6 readings1 assignment

16 videosTotal 40 minutes

Introduction1 minute
Introduction to Polars4 minutes
Polars vs Pandas6 minutes
Setting Up Polars with Cargo4 minutes
Conclusion1 minute
Introduction0 minutes
Basics of the Polars API4 minutes
Reading and Loading CSV Data3 minutes
Inferring and Casting3 minutes
Selecting Columns and Slicing Rows3 minutes
Conclusion1 minute
Introduction1 minute
Eager vs Lazy Evaluation3 minutes
Lazy DataFrame API4 minutes
Inspecting the Plan Before Collecting3 minutes
Conclusion1 minute

6 readingsTotal 60 minutes

Key Terms: Why Polars on Rust10 minutes
Reflection: Why Polars on Rust10 minutes
Key Terms: DataFrames, Series, and the CSV Loader10 minutes
Reflection: DataFrames, Series, and the CSV Loader10 minutes
Key Terms: Expressions and the Lazy API10 minutes
Reflection: Expressions and the Lazy API10 minutes

1 assignmentTotal 5 minutes

Polars Foundations5 minutes

Apply Polars expressions to wine-ratings.csv. Detect and drop nulls with null_count and drop_nulls, normalize text with str.to_lowercase and str.strip_chars, filter by rating bands, sort with sort_by_exprs and SortMultipleOptions, group_by and agg for averages and counts, and join two frames with inner, left, and outer join types.

What's included

12 videos6 readings1 assignment

12 videosTotal 29 minutes

Lesson 2.1 Introduction1 minute
Null Handling Concepts4 minutes
Detecting and Dropping Nulls3 minutes
Normalizing and Filtering5 minutes
Lesson 2.1 Conclusion1 minute
Lesson 2.2 Introduction1 minute
Filtering by Conditions and Columns4 minutes
Sorting by Multiple Columns2 minutes
Grouping and Aggregations4 minutes
Lesson 2.2 Conclusion1 minute
Lesson 2.3 Introduction1 minute
Join Types and Enrichment3 minutes

6 readingsTotal 60 minutes

Key Terms: Data Cleaning and Null Handling10 minutes
Reflection: Data Cleaning and Null Handling10 minutes
Key Terms: Sorting, Filtering, and Aggregation10 minutes
Reflection: Sorting, Filtering, and Aggregation10 minutes
Key Terms: Joining and Reshaping Data10 minutes
Reflection: Joining and Reshaping Data10 minutes

1 assignmentTotal 5 minutes

Cleaning and Transforming Wine Data5 minutes

Wire the cleaning and aggregation primitives into wine-pipeline, three Rust CLI binaries that share a Cargo workspace and a single SQLite database. Bronze writes raw_wines from CSV with an ingested_at timestamp. Silver applies the cleaning contract and writes clean_wines. Gold filters by min-rating, groups by grape, and exports a top-10 leaderboard as gold_wines.csv and gold_wines.json.