An introduction to Python for statisticians and data scientists who already work in R. The focus is on where the two languages differ, the pitfalls R users tend to hit, and the core Python data-science libraries, with each concept related back to its R equivalent.
The workshop assumes you're already comfortable in R, so it doesn't start from scratch. Each concept is introduced alongside its R equivalent, keeping the focus on what actually differs in Python rather than on the fundamentals of data analysis.
Every idea is shown side by side with its R or tidyverse counterpart, from vectors to group_by.
0-based indexing, 2**3 vs 2^3, copy-vs-reference, and mutable defaults: the differences that most often catch R users.
NumPy, pandas, Matplotlib, seaborn, StatsModels, and scikit-learn, the main libraries for data analysis in Python.
Code along in JupyterLab or Colab using the workshop notebooks, with full solutions provided.
The material runs from the language fundamentals through the data-science workflow, split roughly evenly across the two sessions.
The language itself, then array computing.
From wrangling to modeling and prediction.
Slides, both hands-on notebooks with solutions, and a printable R to Python cheat sheet. Open the notebooks in Colab for zero setup, or view them on GitHub. To work on your own computer, download everything in one file.
The full deck presented across both sessions.
Concept overviews for each topic, shown ahead of the live-coding portions.
Ten sections of side-by-side R and Python syntax for quick reference.
Syntax & operators, data types and structures, control flow, functions, NumPy, pandas vs dplyr, Matplotlib & seaborn vs base R and ggplot2, statistical modeling, and the classic Python pitfalls for R users.
Python fundamentals, NumPy & Matplotlib.
The hands-on notebook we work through together. Solutions in a separate file.
pandas, seaborn, StatsModels & scikit-learn.
The second hands-on notebook, covering the full data-science stack.
Doing these ahead of time means we can start on the material right away.
A short survey on your background and what you'd like to get out of the day. It helps me set the pace and choose relevant examples. Please complete it when you have a moment.
If you cannot reach Google Forms, you can email me your answers instead.
Nothing to install. From the Materials section, click Open in Colab on either notebook and start coding in your browser. A free Google account is all you need.
No typing required. Download one file, unzip it, and double-click the launcher for your computer. It installs Python and everything else for you.
python4r-main folder and double-click the launcher:
START-HERE-Windows.batSTART-HERE-Mac.commandThat is all. The first run downloads Python and the course packages, so give it 5 to 10 minutes and leave the window open. JupyterLab then opens in your browser. Whenever you want to work on the material again, just double-click the same file.
Install uv and git, then:
# clone the workshop repo
git clone https://github.com/emeyers/python4r.git
cd python4r
# install everything from pyproject.toml + uv.lock
uv sync
# launch JupyterLab
uv run jupyter lab
Workshop instructor · JSM 2026 Continuing Education
Ethan created the workshop material. Feel free to get in touch before the workshop with any setup questions, or with topics you'd like to see covered.