mdickinson / bigfloat

Python wrapper for MPFR, providing high-precision floating-point arithmetic
GNU Lesser General Public License v3.0
41 stars 11 forks source link

Make available on Python 3.5 and Python 3.6 #74

Closed mdickinson closed 6 years ago

mdickinson commented 7 years ago

This should just be a matter of updating the Trove classifiers in the setup script, and adding Python 3.5 and Python 3.6 to the CI setup.

At the same time, we might consider dropping support for Python 2.6 and earlier Python 3 versions.

FrankenApps commented 7 years ago

It would also be good if it could be pointed out somewhere in the docs, that 3.5 and 3.6 are not supported at the moment, it might people trying to install on such a version save some time...

mdickinson commented 7 years ago

Well, really they are supported. :-) I'm a little bit surprised that the general "Python :: 3" classifier isn't enough here. Do you know what logic is being used to interpret the classifiers? If I simply drop the 3.x subversion classifiers, does that logic assume that all Python 3.x versions are supported?

mdickinson commented 7 years ago

I've updated the trove classifiers directly on PyPI, so bigfloat should now be available for Python 3.5 and Python 3.6 without any need for a new release. I'll leave this issue open as a reminder to myself to update the classifiers here, too.

mdickinson commented 7 years ago

@FrankenApps: What mechanism are you using to install bigfloat, and does it work for you now? pip install bigfloat is working for me on OS X / Python 3.6, but then again, it was already working for me before I changed the classifiers on PyPI.

hugovk commented 6 years ago

Here's the pip installs for bigfloat from PyPI for the last year:

$ pypinfo -d 365 --percent --pip bigfloat pyversion
python_version percent download_count
-------------- ------- --------------
2.7              63.2%          3,047
3.5              17.9%            865
3.6              13.9%            668
3.4               4.8%            230
3.3               0.1%              5
2.6               0.1%              4