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Polars 101: Efficient Data Handling That Outperforms Pandas

BeginnerGuided Project

Polars is a fast and memory-efficient DataFrame library built in Rust, making it ideal for handling large-scale data. It uses lazy evaluation & multi-threading to outperform tools like Pandas in both speed and scalability. It supports expressive data transformations, scales effortlessly in production environments, and comes backed by robust documentation—making it a powerful choice for building efficient, modern data pipelines. You will analyse a real-world weather dataset and perform a variety of data manipulation tasks.

Language

  • English

Topic

  • Data Science

Skills You Will Learn

  • Machine Learning, Python, Polars, Exploratory Data Analysis, Data Science, Data Analysis

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • July 3, 2025
About this Guided Project

A Look at the Project Ahead

After completing this guided project, you will be able to do:
  • Learn Polars from scratch—ideal for beginners to advanced users.
  • Explore and clean real-world weather data efficiently.
  • Master key operations: filtering, sorting, grouping, and joining.
  • Use advanced techniques like rolling averages, ranking, and conditional logic.
  • Understand lazy vs. eager execution and optimize performance.
  • Gain hands-on practice with powerful, scalable data transformations.
Polars is a high-performance DataFrame library built in Rust, engineered for speed, scalability, and efficient memory usage. With lazy evaluation and multi-threading at its core, Polars is ideal for handling large-scale datasets—making it a compelling alternative to traditional tools like Pandas. 

In this hands-on guided project, you'll learn how to use Polars to analyze real-world weather data through a progression of techniques, starting from beginner-friendly tasks like filtering rows, selecting columns, and basic aggregations. As you move forward, you'll dive into more advanced concepts such as creating conditional columns, handling missing values and outliers, and applying rolling window functions to observe temperature trends over time. You'll also explore powerful features like chained transformations using Polars’ expression API, time-based grouping, and ranking methods to uncover insights in the data.

In the latter half of the project, you'll master operations like joining multiple DataFrames, performing anti and outer joins, and horizontally concatenating datasets to enrich your analysis. The project wraps up with exercises and mini-challenges designed to reinforce your learning, making sure you gain both conceptual clarity and practical skills.

By the end, you'll be equipped to build fast, expressive, and scalable data pipelines using Polars—ready to apply them in real-world scenarios, whether you're working on analytics, reporting, or building production-grade systems.


What You'll Need

Let your learners know what technology and skills they'll need prior to starting this guided project. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.

Instructors

Jigisha Barbhaya

Data Scientist

I am a Data scientist at IBM and Lead instructor at Skills network. I love to learn and educate. I have completed my MSc(Computer Application) specialisation in Data science from Symbiosis University.

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Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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Contributors

Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.

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