The difference is dataframes can also be seen like matrices. You can do row operations, row + column operations, multiply rows and columns, multiply different matrices, transpose them etc. These kind of things don't really make sense in DB tables (and they are generally not supported and you jump through hoops to do similar things in DBs).
Oh, and another important difference is memory layout. The dataframe implementations mostly (or all) use column-major format. Whereas most conventional SQL implementations use row-major format, I believe.
> The difference is dataframes can also be seen like matrices. You can do row operations, row + column operations, multiply rows and columns, multiply different matrices, transpose them etc.
I think this is overblowing the similarities to matrices. Matrices have elements all of the same type, while data.frames mix numbers, characters, factors, etc. You certainly cannot transpose a data.frame and still have a data.frame that makes sense. Multiplying rows would not make sense either, since within one row you will have different types of data. Unless you have a data.frame that is all numeric, but in that case one should probably be using a matrix in the first place.
> Unless you have a data.frame that is all numeric, but in that case one should probably be using a matrix in the first place.
They still have their advantages with row/column labels, NaN handling etc. These are not operations I am speculating about by the way. I am most familiar with pandas and the dataframe there has transpose, dot product operations and almost all column operations have their correspondence in rows (i.e. you either sum(axis=0) or sum(axis=1)).
Oh, based on the comment you replied to I thought this was about R. In R matrices can handle NaNs and NAs, have column and row labels, have dot products and much more.
