When you take interesting data from public sources, when you install a highly efficient Python library for data analysis (polars), and you start asking fundamental questions, what can possibly go wrong?

In this course, we will grab public data about the countries of the world and while playing with them, we will explore (a small subset of) what the polars library has to offer. While traditionally the first steps into data analysis are taken with pandas (a true hero in the Python data world), polars is a fresh newcomer that boasts high efficiency and clearly designed API - so why not start with it?

You will learn how to:

• load, manipulate and clean your data;
• gather insights using grouping, aggregation and joining data from various sources;
• understand and present you data visually.

You should possess:

• a modest knowledge of Python (functions, basic containers)
• some familiarity with Jupyter (or some other) notebooks (recommended)
• optionally some knowledge of pandas (not required at all)
• a computer with a pre-installed virtual environment

If possible, come with your laptop prepared - you will find all the installation instructions and notebooks we will be using in the tutorial here: https://github.com/janpipek/eda-polars-way

When you take interesting data from public sources, when you install a highly efficient Python library for data analysis (polars), and you start asking fundamental questions, what can possibly go wrong?

In this course, we will grab public data about the countries of the world and while playing with them, we will explore (a small subset of) what the polars library has to offer. While traditionally the first steps into data analysis are taken with pandas (a true hero in the Python data world), polars is a fresh newcomer that boasts high efficiency and clearly designed API - so why not start with it?

You will learn how to:

• load, manipulate and clean your data;
• gather insights using grouping, aggregation and joining data from various sources;
• understand and present you data visually.

You should possess:

• a modest knowledge of Python (functions, basic containers)
• some familiarity with Jupyter (or some other) notebooks (recommended)
• optionally some knowledge of pandas (not required at all)
• a computer with a pre-installed virtual environment

If possible, come with your laptop prepared - you will find all the installation instructions and notebooks we will be using in the tutorial here: https://github.com/janpipek/eda-polars-way