Pandas is usable? I had no idea..<p>Pandas is really badly designed, in the same way that most Python libraries are: each function has so many parameters. And a parameter can often be a bunch of different types. Pandas is useful, especially for time-series data, but no one particularly loves it. And, it’s embarrassingly slow. Maybe PandaPy is better, but I doubt it. When you start trying to use Python implemented functions (vs C ones) things are going to get bad no matter what you do.<p>Speaking of which, I decided to port over a statistical model for betting from Python to Julia week ago. I’m not done yet, and this is my first major experience with Julia, but it’s been <i>so</i> much nicer than using Python. The performance can easily be 10x-50x faster without really doing any extra work.<p>Also the language feels explicitly designed for scientific computing and really meshes well with the domain. Python the language never really was good for this, but the libraries were pretty compelling. Julia libraries have almost caught up (or in some domains, like linear algebra) have actually exceeded what’s available dor Python. Moreover, if you need to, PyCall is really easy to use.<p>I’m going to go out on a limb and say that people shouldn’t be using Python for new scientific computing projects. Julia has arrived, and is better in everyway (I’m still unsure about the 1-based indexing, but I’m sure I’ll get over it. 0-based waa never that great in the first place).