Python: Analyze Tabular Data from A to Z

Setting Yourself Up to Analyze Tabular Data in Python

Need to use Python but do not know how to get started? Many researchers face the same challenge, feeling stuck between knowing they need programming skills and not knowing where to begin. This session will give you a practical foundation for analyzing data in Python, with a focus on tabular data such as spreadsheets, CSVs, or databases.

We will start by showing you how to get Python running on your machine using Anaconda Navigator, a graphical interface for managing environments and packages, and Pixi, a command-line-based tool, for reproducible environments. You will learn why Python's ecosystem of third-party libraries is important for research workflows and why relying only on basic Python can quickly become limiting.

Next, we will explore the types of data you may encounter in your research. You will learn the differences between structured data, such as tables, hierarchies, and graphs, unstructured data, such as text, images, audio, and video, and semi-structured data, such as logs or JSON records. This will help you understand where your project fits and why tabular data is often a good place to start learning.

Finally, we will cover practical skills for working with tabular data:

• Exploring data with Polars, a fast and modern library for analysis.
• Choosing storage formats for your project, including CSV, Parquet, and SQLite.
• Visualizing your data quickly to understand your results and communicate findings.

By the end of this webinar, you will know how to install Python and start using it productively for the types of data you are most likely to encounter in research.

Agenda

• Getting Python running on your computer: Learn how to download Python and install packages using Anaconda Navigator and Pixi, so you can start working without configuration headaches.
• Why third-party libraries matter: Understand why Python’s ecosystem of libraries is essential for data analysis and why relying only on basic Python can slow you down.
• Understanding tabular data: Explore what tabular data is and how to make sense of it using Polars, a fast and modern library for research workflows.
• Choosing the right storage format: Compare options such as CSV, Parquet, and SQLite, and learn how to pick the best one for your ongoing projects.
• Visualizing tabular data quickly: Discover simple ways to create clear and insightful visualizations so you can understand your results and share them effectively.

BONUS - Office Hours

Office hours (45 minutes) Want more personalized guidance after the session? We're also offering a 45-minute office hours session where you can bring your own notebooks or workflow questions. This is your chance to get tailored feedback, troubleshoot issues, and explore advanced tips that go beyond the live demonstration.