3 ms·
For me it is one of the signs why the Python 2/3 division was a failure. In data science, everything is Python 2 first, then (usually, but almost always with so
by stared 10y ago
For me it is one of the signs why the Python 2/3 division was a failure. In data science, everything is Python 2 first, then (usually, but almost always with some delay) ported to Python 3 (think: Spark, TensorFlow, etc).
If there is a new package (say, on HN), very often it does not work (well) on Python 3.
I use Python 3 (usually), but it makes it harder to do with data science, as often I need to overcome some small (but nasty) issues.
I teach Python 3. But not, it is not a no-brainer.
- rsfern 10y agoWhat kind of issues have you run into with Python 3 that make data science more difficult? I use Python 3 as well, and I don't think I've run into anything that fits your description...
- stared 10y agoDescribed above (and gave examples of two mainstream libraries; with smaller one its much more serious). If there is a new package, its likely to be either totally not working on Python 3, or be undertested. So there is a delay, before I can use something with Python 3. (Nothing about the Python 3 itself, but about its ecosystem.) At the same time, I hardy see any strong points, in which Python 3 is much better for data science. (Examples?)