Julia motivation: why weren't Numpy, Scipy, Numba, good enough?

[Tamas_Papp]

Initially I had the impression that Julia learned a lot from Python:

1.0 adoption path?

The big issue was upgrade deadlock. Instead of making it possible for packages to support both Python 2 and 3 at the same time, the Python devs provided the 2to3 tool, hoping that the entire ecosystem would upgrade from Python 2 to 3 all at once. But upgrade effort turned out not to be what stymied the transition. Packages still needed to support Python 2 because most of their users were still using it. And since most packages stayed on Python 2, users did as well – if anything they depended on…

but then the common recommendation was to drop support for 0.6:

[ANN] Introducing Upgradathon Fridays

And here is how you do it! First off, the process will be much simpler if you choose to drop support for Julia 0.6 on the master branch. Unless you have strong reason to create another release compatible with Julia 0.6 I would strongly recommend doing this, several packages, like StaticArrays and DataStructures, have already done so. If you need to release a 0.6 compatible bugfix release of your package you can do this from a branch later. If your package are dependent on other packages, I sug…

which I think was unfortunate in retrospect (even if, naturally, it was easier on the maintainers).