How to use pandas the wrong way
The pandas library represents a very efficient and convenient tool for data manipulation, but sometimes hides unexpected pitfalls which can arise in various and sometimes unintelligible ways.
By briefly referring to some aspects of the implementation, I will review specific situations in which a change of approach can make code based on pandas more robust, or more performant.
I graduated in Mathematics, then switched to studies in Economics just few days after Lehman Brothers went bankrupt and the global financial crisis exploded. My main field of research is represented by networks in social sciences, but I also work on firms performance and fiscal evasion. In general, my passion is to uncover non-obvious ways to help data express themselves.
I started studying Python for fun, as an apparently harmless curiosity, but along the years it has become more and more difficult for me to keep it from intruding into my working life, and today I'm definitely addicted (and contagious).