How I learned Python

About me

Hi! I’m Federica Gazzelloni — a statistician and data scientist with a background in actuarial science, public health metrics, and reproducible research. I collaborate with organizations like the Institute for Health Metrics and Evaluation (IHME), The Carpentries, Bioconductor, and the R Consortium.

For years, I’ve been working primarily in R, especially for statistical modeling, data visualization, and public health research. But as my work evolved to involve more machine learning, web apps, and cross-platform collaboration, I knew it was time to add Python to my toolkit.

Why I wanted to learn Python

Python stood out for its wide adoption in machine learning, versatility in general-purpose programming, and strong community support. I wanted to better collaborate with interdisciplinary teams, extend my work to new tools and APIs, and bridge my R skills with Python’s ecosystem.

Rather than viewing R and Python as competitors, I saw the value in knowing both—and that motivated me to dive in.

How I approached learning Python

My approach was practical: I wanted to start using Python to solve real problems, not just read about it. Here’s how I got started:
• I followed The Carpentries Python materials, which I loved for their beginner-friendly pacing.
• I took part in data challenges and translated some of my R scripts into Python to understand differences in syntax and libraries.
• I explored libraries like pandas, matplotlib, and scikit-learn to perform familiar tasks—wrangling, visualizing, and modeling data.

I also started teaching what I was learning—organizing community workshops with R-Ladies Rome to help others transition from R to Python too!

Challenges I faced

•	Python felt verbose compared to the tidyverse, especially early on.
•	Getting used to Jupyter vs RStudio required a shift in mindset.
•	Understanding Python’s package ecosystem took some time, especially figuring out when to use what (e.g., numpy vs pandas, or plotting options).

But with each challenge came a deeper understanding—and a growing excitement as things clicked.

Key takeaways

•	Translating real workflows (e.g., data summaries, visualizations) helped build confidence quickly.
•	Python and R can work side by side—using Quarto and interoperable notebooks was a game-changer.
•	The Python community is very welcoming, and there’s no shortage of tutorials and forums to get help.

Tips and advice

If you’re coming from R or another language:
• Start with tasks you already know how to do.
• Learn by building—not just following tutorials.
• Join a community (like R-Ladies, PyLadies, or local meetups)—it makes a huge difference.

Final thoughts and next steps

Learning Python has expanded the way I think about data and tools. I now help others make this transition—whether you’re an R user wanting to integrate Python or someone starting fresh.

If you’re looking to get started, need a mentor, or want hands-on support for your project, I’d love to help. Let’s work together!