So you are thinking about adopting a Python
Congratulations! Googling (or DuckDuckgoing it if you are like me) about Python is the first step to adopt Python in your research work. If you haven’t, in this page I’ll try to convince you that adopting a Python in your research lab is the right choice for you!
A Python in a social sciences’ lab?
Well, of course I am not referring to a real reptile, but to one of my favorites programming language. Created by Guido Van Rossum, Python is:
an interpreted, high-level, general-purpose programming language with a focus on readability.
Python it’s easy to learn and to use, it’s free, lightweight, fast, and there are many packages that can be installed to make it your perfect S̶w̶i̶s̶s̶ ̶a̶r̶m̶y̶ ̶k̶n̶i̶f̶e Sonic Screwdriver for your experimental design, data collection, data visualization and statistical analysis. And if the word Sonic Screwdriver hasn’t convinced you yet, keep reading: the best as yet to come.
How easy?
If you have previous experience with programming languages you know that the first thing you learn is how to write a simple program that display on screen the phrase “Hello, World!”. Take a look on how this is done in Python:
print(“Hello, World!”)Yes, that’s it, no joke! Easy, isn't it? For a comparison with other programming languages, you can check this page.
What if I want to learn it?
If you want to dig into Python, there are a lot of free and premium tutorials and books. My favorite is definitely “Automate the Boring Stuff with Python”, which comes in a free online version and a ebook or paperbook edition. This is obviously not the only available book, but to me it’s still a good one (well, not as good as the one i want to write of course).
You said packages?
Like with Amazon, Python packages are delivered directly to your working environment, but in this case for free. Python is a widely adopted language, and it’s users have been creating packages for almost everything. For example, I myself created a Python package for the analysis of electrophysiological signals, that can analyze ECG, EMG, and EDA signals in just a few lines of code. And especially when it comes to data science, Python has packages for handling complex data structures, performing statistical analysis, create beautiful visualizations of your data, analyze complex signals, and even to train and test models.
If you want to take a look at some of my favorite packages for Data Science, and especially useful for Social Scientist, you can check this other post: “The toolbox of the Herpetologist Social Scientist”.
What about the Free part
Well, if you come from the Social Sciences, you may have hear of Matlab, Stata, and especially SPSS. If you are familiar with any of these, you know that software require a license in order to be used, which is usually very expensive, the software are not lightweight at all and they mostly use a proprietary format, which means you can work on data processed using one of those software only using another copy of those applications. From this point of view, Python is more similar to R, as it is released for free, and you can write code using the editor you prefer, on any operating system and hardware you want (and yes, even on a 5 dollars single board computer like a Raspberry Pi).
To sum up
To sum it up, Python it’s free, lightweight, fast, easy to use, it runs everywhere, and there are packages for everything related to data analysis. If you are still reading this, run and download it, and join us as a new Herpetologist Social Scientist!
