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Among academics and scientists, Python is expanding at an increasingly faster pace thanks to both its simplicity and the fact that it’s free. This also results in significant savings and a change in philosophy.
Getting to know the community around Python and tools such as Ipython and Jupyter Notebook, libraries such as NumPy, matplotlib, SciPy… You rethink a lot of things. For example, IPython was created as a tool for personal use during a thesis; it was then shared with the community and the project resulted in Notebook, a key tool for analyzing data.
Python irradiates the conviction that sharing and collaborating brings about a better world. For this reason, I think it’s becoming more common that research groups and institutions such as CERN are now publishing their data or code. In the scientific ecosystem where the reproducibility of results is essential, this is a giant leap.
If you ask anyone why they like Python, they will soon tell you that it’s a simple language. This is not an accident. When Guido van Rossum created Python, he followed a series of guidelines including: “Simple is better than complex” and “Readability counts”. And this is not the case with other languages which focus in aspects such as performance.
Simplicity has a lot of advantages. The most obvious being that Python is easy to learn. And this makes it a more accessible tool for people who are less knowledgeable about computers. But there is more…
Python is a more legible and transparent code; it takes less time to develop and, as a consequence, productivity increases; it doesn’t have so many bugs and, as such, quality is better; and it’s a lot easier to maintain. Naturally, some compromises are needed but it’s a matter of prioritizing.
I’ve been working for one of Altran’s customers since I joined the company: Airbus Defence & Space. My department develops some of the tools that analysts use to analyze data from flight test aircraft. Our work goes from implementing and testing calculation algorithms to delivering a program with a GUI which can generate, represent and store the results correctly.
It’s very interesting work for an aeronautical engineer who likes planes and programming. The fact that I know Python was decisive in getting the job.
I’ve been in the Python ecosystem for over four years. I go to Python Madrid meetings regularly. And I have been to all PyCon conferences held in Spain. This time I felt like going ahead and giving something back to the community that has taught me so much. The idea during the event was to show the power of the Pandas library in data analysis and, to do this, we used a database of air accidents. If the guys from AeroPython are around, aeronautics can’t be far behind…
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