> there's nothing inherent to any particular domain that demands this kind of 'magical' API Plotting seems to tend towards magic because plots are basically ar

> there's nothing inherent to any particular domain that demands this kind of 'magical' API Plotting seems to tend towards magic because plots are basically art, with all the desire for aesthetic customization that applies, and it's a very common task so users also want brevity (magic). The result is a plot() function with a gazillion options hidden behind keyword arguments. I agree that matplotlib has a sprawling interface, and this can be annoying, but I'm still not sure what "guess the caller's intent by inspecting the types of the arguments" means. Sure, the functions have multiple call signatures, but that's not exactly unusual in libraries or languages. I don't understand the context that brings guesswork into the picture. Skimming the manual—are you using the data keyword argument and hitting the `plot('n', 'o', data=obj)` ambiguity [0]? Or calling plot through `pyplot.plot` &c. (which rely on state) instead of `Axes.plot` &c.? Asking because if there's an interface trap I'm unaware of I'd like to learn about it before walking into it blindly. Pandas I sort of agree with; I personally find it harder to remember how to use pandas than dplyr, despite using pandas more often and spending more time reading the pandas documentation. I also find it inconvenient to represent missing values in Pandas (`None` and `NaN` are overloaded, and `None` forces the `object` dtype). But maybe the problem is on my end. [0] https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.plot.html https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.p...