What kinds of data analysis is Julia a top choice for?
As for what I’m looking for, I think it’s a couple of things, with different time scales:
- In the very near term, can it solve the immediate problem I’m trying to solve?
- In the more moderate to long term, how does Julia compare to Python for the types of things I typically do? How viable might it be as a Python replacement?
And yes, “data analysis” is a broad term. I typically do what people refer to as “exploratory analytics” - Python + Numpy + Pandas + Matplotlib (or Plotly) cover probably 80-85% of what I usually do**. But every once-in-a-while, I need to do something off-the-wall, like a regex-based pre-filter of large files before doing additional analysis. Or this one, which happened a few years ago: “iterate through all 100M+ possible permutations of a thing and perform a CPU-intensive calculation on each one.” (Python’s itertools + multiprocessing did the job there, albeit quite slowly.)
**Edit: in addition to data analytics, I’ve also worn the hat of “performance engineering,” “test engineering,” and “performance characterization” in the past, which has involved gathering data and then analyzing said data.
Thanks, I’ll take a look.