I started using Julia in December, DataFrames are in a sort of weird place because they're so much less necessary compared to e.g. Python. In Julia, you could just use a dict of arrays and get most of the benefits, thanks to libraries like Query.jl and Tables.jl. Thus the ecosystem is a lot more spread out. I actually use DataFrames much less than I used to in Python.
This is mostly good, because you can apply the same operations on DataFrames, Streams, Time Series data, Differential Equations Results, etc., but it does mean that some of the specialized optimizations haven't made it into DataFrames.jl
I've been using Python a lot longer than I've been using Julia, and this isn't really true. Python tends towards much larger packages where everything is bundled together, and there are fairly deep language-level reasons for that. Python doesn't have major alternatives to pandas the way Julia has half a dozen alternatives to DataFrames. There is nothing like Query.jl that applies to all table-like structures in Python.
In pandas, you'll see things like exponentially weighted moving averages, while DataFrames.jl is pretty much just the data structure.
The centralization of the Python ecosystem and extra attention that pandas has gotten has made it much better in several ways – for example, pandas's indexing makes filtering significantly faster. These optimizations might make it to DataFrames.jl eventually, but I doubt you'll ever see the same level of centralization.
This is mostly good, because you can apply the same operations on DataFrames, Streams, Time Series data, Differential Equations Results, etc., but it does mean that some of the specialized optimizations haven't made it into DataFrames.jl