Introduction¶
After years of using Python for handling various kinds of data (e.g., with numpy, pandas, and scipy) and making plots (primarily for scientific publications with matplotlib and more recently plotly),
I have accumulated quite a bit of tips and tricks for data crunching and visualization.
I have always wanted to collect them into a knowledge base in the form of series of notebooks, to serve as a reference for myself and share with others at the same time.
I came across the Jupyter Book project by accident and decided that this is the best format for this “book” project of mine.
Scope¶
This is not meant as a collection of tutorials for the aforementioned Python libraries. There are more than enough of those on the Internet. Besides, I don’t want to misguide readers with my (potentially) bad (and still evolving) coding habits (since I don’t have a proper programmer/developer background and have wrongfully treated Python simply as a fancy scientific calculator and replacement for Matlab for the longest time). I plan to just share my ideas and the ways I approach and realize them in Python, with the hope that I will be able to polish the skills I already have and gain new ones along the way.
I am interested in many topics both related to and outside of my work. A short list of the major ones (for the moment) are listed below:
Laser spectroscopy
Fluid dynamics
Machine learning
Finance
I will slowly add content in no particular order and will revise them over and over again in the future. Feel free to open an issue on the repo if you have questions, suggestions or requests for certain topics.
About me¶
I’m a research scientist located in Germany. With a background in laser spectroscopy and plasma physics, I’m currently dealing with new combustion technologies that could handle low-carbon, sustainable fuels for both stationary gas turbines and aircraft engines. To find out more about me, check out my homepage, (still few) repos on Github and my Google Scholar page.