Polars for Power Users

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.
• View the full course outline.

Who Should Take This Course?

• Python developers and analysts who feel their pandas or Excel workflows are hitting size or speed ceilings.
• Data-curious software engineers who prefer readable, type-checkable code over magic strings and overloaded operators.
• Scientists, journalists, and finance pros who routinely clean CSV/Excel data and want production-grade performance on commodity hardware.
• Learners who enjoy practical, example-driven teaching and appreciate having every demo in a cut-and-paste REPL file.

Key Chapter Highlights

Chapter	Why It Matters
Welcome & Setup	Clone the repo, spin up a virtual env, install Polars in minutes.
Polars Introduction	DataFrames vs. Series, expressions, contexts, the mental model for everything that follows.
Data Types Deep Dive	Understand bits, bytes, int, floats & dates so casting errors never derail you again.
Excel & Polars	Replace everyday spreadsheet moves, read, filter, sort, SUMIF, with pythonic one-liners.
More Excel Tricks	Date and string accessors, multi-condition filters, chaining power users swear by.
Aggregation & GroupBy	Pivot tables, roll-ups, null handling, business analytics at warp speed.
File I/O & Joins	Read multi-sheet workbooks, fix dirty data, merge schemas, write styled Excel.
Lazy Frames	Scan 1-million-row CSVs in <1 s and see why lazy evaluation is a game changer.
Case Study	Put it all together on real UN data: GDP trends, per-capita metrics, R&D spend.
Conclusion	Pandas interop, NumPy, Matplotlib, Narwhals, where to grow next.

View the full course outline.

Follow along with subtitles and transcripts

Each course comes with subtitles and full transcripts. The transcripts are available as a separate searchable page for each lecture. They also are available in course-wide search results to help you find just the right lecture.

Who am I? Why should you take my course?

My name is Christopher and I'm happy to be delivering this course for you. I've been coding and teaching Python for over a decade. I've consulted for a variety of companies both small and large, providing advice on both software architecture and development processes.

I've taught thousands of students in person and virtually on a variety of topics, including Agile methodologies, Testing, Requirements management, and DevOps. My Python screencasts cover Django, PyGame, SQLAlchemy, Data structures, Unicode, cryptography, and more.

You may also know me from The Real Python Podcast, where twice a month I cover recent articles and news items in the Python world, or from my book Django In Action, available at Manning.com.

This course is delivered in very high resolution

This course is delivered in 1440p (4x the pixels as 720p). When you're watching the videos for this course, it will feel like you're sitting next to the instructor looking at their screen.

Every little detail, menu item, and icon is clear and crisp. Watch the introductory video at the top of this page to see an example.

Money-Back Guarantee Details

We want every student to feel 100 % confident enrolling. If "Polars for Power Users" doesn’t radically improve your data-analysis workflow, let us know within 15 days of purchase and we’ll refund you in full, no forms, no hassle, no hard feelings.

Simply email our support team (contact@talkpython.fm) with your registered email address, and your refund will be processed within several business days. We’re certain the speed gains and clean-code patterns you’ll learn are worth far more than the tuition, but you get to decide.

Corporate / Team Licenses

Empower your whole data or engineering team to adopt Polars best-practices in one cohesive curriculum.

• Volume discounts start at 5 seats and scale up for larger groups.
• Centralized billing and a single invoice for easy reimbursement.
• Private progress dashboard so team leads can track completion.

Interested? Email sales@talkpython.fm with the number of learners and your preferred billing currency, and we’ll craft a package that fits your organization.

Tech Requirements & Setup

This course keeps prerequisites light so you can focus on mastering Polars itself. Every tool and library used in this course is 100% free and open source.

Requirement	Details
Python	3.10 + (all demos recorded on 3.13, older LTS versions work fine)
Operating System	Windows, macOS, or Linux, anything that runs modern Python
Memory / CPU	4 GB RAM recommended; Polars’ Rust core shines even on laptops
Developer Tools	Git for cloning the course repo; a terminal or command prompt
Packages Installed During Setup	polars, xlsxwriter, and fastexcel optional: matplotlib, graphviz (for query-plan graphs)
Optional IDE	VS Code, PyCharm, Jupyter Lab, or any terminal with REPL support, videos use the standard Python prompt so you can follow along anywhere.

The very first lesson walks you through creating a virtual environment, installing Polars with pip/uv, and cloning the sample-code GitHub repo, so you’ll be up and running in minutes, even if it’s your first time using a virtualenv.

Ready to Transform Your Data Analysis Game?

Don’t let slow tools and messy spreadsheets hold you back. Join "Polars for Power Users" today and start writing faster, cleaner, and more reliable data pipelines, with skills that transfer directly to your real-world projects.

Click "Enroll Now" to get instant, lifetime access to every lesson, REPL demo, case-study notebook, and future course updates, all backed by the Talk Python 100 % satisfaction guarantee.

Your data deserves the speed of Polars. Let’s unleash it together.