Haha, I see we are thinking along similar lines. I agree with the sentiment, but I think for many “data wrangly” workflows in Python you can get by with numpy, pandas and matplotlib, while in Julia I often have using CSV, XLSX, DataFrames, LinearAlgebra, Statistics, StatsFuns, [Stats]Plots (e.g. because norm and mean and countmap happen to be in different maths/stats related packages and the data reading capabilities are separated from the DataFrames library).
Again, I don’t think this is a problem at all - I don’t think there’s a reason why DataFrames and CSV should be more closely integrated, and the development speed for both packages is probably (unprovably) higher than in a world where they would be shoehorned into a big package.