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.

I disagree. Python's data science community is strongly clustered around pandas, even though it's possible to use other approaches