The main problem (from my point of view) of python data science ecosystem is a complete lack of standardization on anything.
You have ten different libraries that try to behave like 4 other languages and the only point of standardization is that there is usually something like .to_numpy() function. This means that most of the time I was not solving any specific problem related to data processing, but just converting data from a format one library would understand to something another library would understand.
In Julia (a language with it's own problems, of course) things mostly just work. The library for calculating uncertainties interacts well with a library handling units and all this works fine with the piloting library, or libraries solving differential equations etc. In python, this required quite a lot of boilerplate.
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