Meet Kasia Rachuta: Python, SQL, and Dealing with Big Data
This post is part of a series introducing the speakers at the PyBay2018 conference in San Francisco this August. It’s a great chance to learn and connect with an engaged and diverse community of Python developers. We hope you’ll join us!
What are you going to be speaking about at PyBay2018, and why are you excited to give this talk?
I’m going to talk about taking first steps in pandas and how to ‘translate’ some of the SQL functions into Python, from simple aggregations to more complicated window functions. I’m very excited to give this talk because I know that people are often afraid to take the leap from SQL into Python as they are worried the initial learning curve will slow them down.
How did you get into programming and Python?
I was forced to learn some C++ during my physics degree but I hated that — it’s only after graduation that I felt more excited about programming. I ended up learning Python out of necessity — I was dealing with some very big data files during my data analysis internship and Excel couldn’t cope so I had to find a different way of dealing with them.
What’s your favorite Python library (core or third-party), and why?
I’ve been playing around with different visualization libraries and I’ve really been enjoying using Bokeh. Besides the fact that it can produce some really pretty, interactive visuals, the Bokeh community has been incredibly helpful and responsive whenever I got stuck (that is… a lot).
What can you be found doing when you’re not writing code?
I enjoy travelling, scuba diving and eating! I’m also a PyLadies organizer for the San Francisco, meaning I’m involved in putting together all sorts of events, such as MiniPyCon, or mentoring during our weekly hands-on nights.
What’s the best advice you’ve received as a Python developer?
When I started out in data science, I was trying to learn everything — I wanted to learn both Python and R and I couldn’t streamline my learning. One of the best pieces of advice I was given is to pick one thing and get really good at it — there’s no point in being mediocre at a couple of languages!
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