Gonna preface by saying I like what matplotlib is trying to do, and that it has done a lot of good for a lot of people.
Seaborn is a wrapper around matplotlib. It's popular because it removes a lot of the boilerplate from matplotlib and is pandas-aware
For example, you call the pairplot function with a dataframe, and you just get a matrix of correlation plots and histograms. Versus matplotlib where half the documentation/search results use imperative w/ global state and the other half is OOP, and all the extra subplots shenanigans you have to decipher to get something that looks good.
It's convenience, really. The people who use seaborn don't want to dive into matplotlib because the interface is kinda a mess with multiple incompatible ways to do things. It also documents what arguments mean instead of hiding most of them in **kwargs soup. You get plots in 1 minute of seaborn that would otherwise take 10 minutes in matplotlib to write.
Seaborn is a wrapper around matplotlib. It's popular because it removes a lot of the boilerplate from matplotlib and is pandas-aware
For example, you call the pairplot function with a dataframe, and you just get a matrix of correlation plots and histograms. Versus matplotlib where half the documentation/search results use imperative w/ global state and the other half is OOP, and all the extra subplots shenanigans you have to decipher to get something that looks good.
It's convenience, really. The people who use seaborn don't want to dive into matplotlib because the interface is kinda a mess with multiple incompatible ways to do things. It also documents what arguments mean instead of hiding most of them in **kwargs soup. You get plots in 1 minute of seaborn that would otherwise take 10 minutes in matplotlib to write.