Polars for Power Users Course

Transform Your Data Analysis Game

Unlock the full velocity of modern data science with Polars, the blazing-fast DataFrame library built in Rust and delivered in Python. In this hands-on course you’ll move from spreadsheet limitations and sluggish pandas workflows to lightning-quick analyses that handle millions of rows in the blink of an eye.

Whether you’re wrangling CSVs, taming messy Excel files, or joining multi-gigabyte datasets, "Polars for Power Users" shows you exactly how to translate everyday data tasks into clean, Pythonic code that runs 10-100× faster than traditional approaches. Through a pragmatic mix of short lectures, live REPL sessions, and a capstone case study on global GDP, you’ll master Polars’ expressive syntax, lazy evaluation engine, and productivity-boosting features such as pivot tables, joins, and Excel-style date accessors.

What's this course about and how is it different?

Polars is a newcomer that combines the ergonomics of pandas with the raw speed of a Rust-powered backend. But switching libraries can feel daunting, documentation alone rarely answers how to migrate your day-to-day workflow.

This course bridges that gap by repeatedly showing an "Excel or pandas way" next to the Polars way, so you see concrete savings in typing, memory, and runtime with every lesson. You’ll learn not just what to type, but why Polars’ design, columnar storage, eager vs. lazy execution, and expression objects lead to simpler, safer code.

Each chapter builds toward a real-world case study: Merging GDP, population, and R&D spending to create per-capita and percent-of-GDP insights. Along the way you’ll confront (and fix) dirty data, inconsistent schemas, and multi-sheet spreadsheets, the headaches analysts battle every day.

Finally, you’ll leave with a GitHub repo of REPL sessions and finished scripts you can use straight in your own projects, plus performance timings that prove why Polars earns its "power user" title.

What You Will Learn

• Install & configure Polars with virtual environments (uv or pip) and a ready-to-clone GitHub repo.
• Build DataFrames from dictionaries, lists, and external files (CSV, Excel).
• Navigate row slicing, negative indexing, head(), and tail() without an explicit index column.
• Use expressions as first-class objects to reuse calculations across DataFrames.
• Add, rename, drop, and concatenate columns & rows with one-liner methods like with_columns() and concat().
• Filter data the Excel-quick-filter way using Boolean masks, chained conditions, AND/OR combinations, and date accessors.
• Perform aggregations & group_by() to replace Excel's SUMIF, COUNTIF, and pivot tables.
• Handle nulls and duplicates: fill_null(), drop_nulls(), is_unique(), value_counts().
• Clean messy types (percent strings, ZIP codes, floats-as-ints) with casting and string/regex helpers.
• Join and merge disparate files with inner, left, and custom key joins, no more VLOOKUPs.
• Export results to styled Excel workbooks via xlsxwriter and to super-large CSVs far beyond Excel’s 1-M-row limit.
• Unlock lazy evaluation (scan_csv(), lazy(), collect()) and read query plans with explain() for serious speed-ups.
• Optimize real workflows in the GDP/Population/R&D case study, producing KPIs like GDP per capita and R&D % of GDP.
• Adjust polars.Config to preview huge tables safely (set_tbl_rows(), glimpse()).
• Benchmark eager vs. lazy runs and understand when Polars outperforms pandas by orders of magnitude.

Take the course

Take the full course at Talk Python.