From R to Python: DataFrames
I had to learn R as part of a class, but my coworkers keep telling me to learn Python! If you have a similar experience, you know the challenge: Python has many versions, a huge ecosystem of packages, and so much tooling that it can be overwhelming to figure out where to start. This webinar is designed to help researchers who know R make the leap to Python confidently, focusing on working with polars.DataFrame, Python’s equivalent of R’s data.frames.
We will begin by showing you how Python’s ecosystem maps to what you already know from R, so you can quickly feel at home with familiar operations such as filtering, grouping, and summarizing data. You will learn which libraries are essential for data manipulation and analysis, and how to set up your environment to avoid confusion with conflicting packages or versions.
Then we will explore DataFrames in Python in practice. You will see how to load, clean, and transform data, compare workflows with R, and understand the differences and advantages of Python tools for research data. We will also highlight strategies to make your code reproducible and easy to share with collaborators.
Agenda
In this 60-minute session, we will
- Set up Python for DataFrames: Learn how to install the right packages, manage environments, and avoid version conflicts.
- Map R skills to Python: Translate familiar R concepts such as data frames, dplyr verbs, and pipelines to Python equivalents.
- Explore and clean data: Load CSVs or other tabular data and perform filtering, grouping, and transformations efficiently.
- Analyze data with Python: Learn how to summarize and manipulate data for research workflows.
By the end of this webinar, you will feel confident applying your R experience in Python and using DataFrames effectively for research data analysis.
