reading csv in Julia is slow compared to Python

The best answer is probably that I'm not as a good a programmer as Wes.

In general, the code in DataFrames is much less well-optimized than the code in Pandas. I'm confident that we can catch up, but it will take some time as there's a lot of basic functionality that we need to implement first. Since there's so much that needs to be built in Julia, I tend to focus on doing things in three parts: (1) build any version, (2) build a correct version, (3) build a fast, correct version. For the work I do, Julia often doesn't offer any versions of essential functionality, so my work gets focused on (1) and (2). As more of the tools I need get built, it'll be easier to focus on performance.

As for memory usage, I think the answer is that we use a set of data structures when parsing tabular data that's much less efficient than those used by Pandas. If I knew the internals of Pandas better, I could list off places where we're less efficient, but for now I'll just speculate that one obvious failing is that we're reading the whole dataset into memory rather than grabbing chunks from disk. This certainly can be avoided and there are issues open for doing so. It's just a matter of time.

On that note, the readtable code is fairly easy to read. The most certain way to get readtable to be faster is to whip out the Julia profiler and start fixing the performance flaws it uncovers.

There is a relatively new julia package called CSV.jl by Jacob Quinn that provides a much faster CSV parser, in many cases on par with pandas: https://github.com/JuliaData/CSV.jl