I recently presented at PyData Seattle, a conference hosted by Microsoft. It was focused on doing data analysis in the Python programming language — an emerging popular language among data analysts and scientists.I presented a tutorial on pandas. It’s a popular open source library for processing data and analytics, which I help maintain along with dozens of other contributors. It’s also an important tool for us on the Data and Analytics team at MITTERA.
You might think that a tech conference like this would be centered around tools and technologies — the algorithms and pieces of software that help you extract meaning from piles of data. To an extent, that is true. My own tutorial was one such presentation.
However, if I only went to hear about the latest and greatest technologies, I would have missed out on the heart of the conference — the community.
The history of Python goes back some 26 years. It started as a teaching language, but was eventually picked up by other communities because of its ease of use and ability to interface with legacy (outdated) computer systems. Scientists were one of those communities, and they laid the groundwork for the broader PyData community.
The PyData community is an interesting collection of academics, business people, and toolmakers that all come together with the goal of doing or enabling more and better data analysis.
The results are impressive.
From top-notch machine learning libraries like Scikit-Learn and climate research to the discovery of planets orbiting other stars. The scientific python stack (a bunch of software pieces that build on each other) has proved its value. Equally interesting are the stories of what went into the building the cornerstones of the stack, and then releasing that code to the public for free knowing it would make the product better.
While speaking with one toolmaker, I asked why he chose to work in Python all day instead of the many alternative languages. He said that he didn’t particularly love the language. This could be shocking to some, as programmers will often heated debates in defense of their preferred language.
Rather, he built his tools in Python because of the community.
Currently, the PyData community is expanding beyond its Python origins. Organizations like NumFocus are sponsoring projects in the Julia programming language, and Project Jupyter — which began is a Python exclusive — are evolving to support multiple languages. The goal is to foster a broader and more diverse community, enabling richer data analysis.
We’ll keep watching and stay involved in these communities here at MITTERA, enabling our analysts to do their best work.
– Toms Augspurger, Lead Data Scientist at MITTERA Data and Analytics
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