Overall, through a brief and anonymously-answered survey to better collect their opinions and impressions, the general feeling of 15 participants about content/duration balance was “okay” (not too short, not too long). Most of them were Undergraduate/Graduate and PhD students, and there were some senior researchers and technicians too. I was glad to see high heterogeneity regarding their expertise with programming (in general, Python, R…), from users which only use Excel to “fair enough/advanced” programming users, including some of them whose programming skills are getting rusty and wanted to refresh it. “Excel” was the most common way to load and work with dataframes in their daily activities, followed by minor cases with “plain-text”, “comma-separated files”, “dataframes in general using R, and dealing with “TSV” and “VCF” files. Of course, in Human Cancer Genetics labs, we work with dataframes for mutations, gene expression or clinical data analysis. 60% of them used Windows OS and 70% had heard before of Miniconda, the terminal and Python (actually, all of them knew of Python). Only 40% had heard of Matplotlib and/or Scikit-learn libraries too. As expected, more than 90% of people replying had worked previously with dataframes, although 2 of them did not.

Also, we had an interesting debate about the use of Python instead of R. I am much more confident programming with Python since it’s the language I mostly practice during my masters and I do confess the use of Pandas has tightly fixed my preference for Python (while talking about dataframes). I am certainly not experienced enough to talk about it, and evenmore, bioinformatics/IT forums are flooded with posts about this topic. If you want to know more, you can start with this one in Biostars. As a bioinformatics PhD student, my aim is to expand my programming skills and solve my research questions using the smartest solution (by “smartest”, I mean “easiest”, “most computationally powerful”, “as close as possible to the state-of-the-art”, “more robust”,…). Who said programming is addictive? The more programming I learn, the more time I’m willing to spend on it. Hey, someone made an interesting question about parsing larger (let’s say ~20GB) VCF files using Pandas. This brought up other issues such as manually modifying a VCF, which can break the right formatting. I’d rather use other bioinformatics tools to handle big VCFs files. Also, I would even split and parallelize the problem. But, if at some point, some lines of code with Python were to make my day, apart from trying out with Pandas, I am starting to read about other libraries for “larger data” such as Dask, Vaes and PySpark. However, this topic is far from being an “introductory minicourse to Pandas”. Finally, some days after, I knew that some of them could not finish the whole exercise for various reasons and errors while executing the code. Of course, I desired all of them to achieve the final figure, but I’m happy to hear that all of them have been up to a two-hour gap to learn how to open the terminal, install Miniconda, discover iPython and run some code using Pandas.