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So there’s a tree of dependencies blocking a new release. Having fixed an incompatibility in the code, the next problem is that the test suite relies on the numexpr package, and numexpr can’t have 3.10 wheels until NumPy has complete 3.10 wheels.
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In other cases, however, the code is incompatible and requires some work to support new Python releases.įor example, when I first started writing this article I’d started porting the Fil memory profiler to 3.10, and initially the tests were failing. That means you can can compile it yourself, with enough work, and the maintainer will usually release a new wheel pretty quickly. Sometimes it’s just a matter of recompiling the code.
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Important: Make sure you upgrade pip, otherwise you might not get the latest binary wheels for 3.10 on Linux, and instead pip will try to build from source. There are plenty of other projects missing 3.10 wheels to pick another random example, matplotlib (with 27M downloads a month from PyPI) is also missing a 3.10 binary wheel. That being said, the NumPy package is limited to Linux, and mostly exists for testing purposes.Īs of Oct 12, 2021, Windows and macOS are still not supported.Īnd since Pandas depends on NumPy, it won’t work on Windows or macOS either until the NumPy package is released. To be fair, things seem to be improving on this front: for the 3.9 release, NumPy and Pandas did not have wheels available this soon after the release. In this case, psycopg2-binary still doesn’t have wheels for 3.10.
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When you install a package, you can just download the binary wheel and don’t need to compile it (unless you’re using Alpine Linux).īut it’s so soon after 3.10’s release, many packages don’t have wheels for Python 3.10 yet. Typically, Python package maintainers upload compiled versions of their packages–known as “wheels”–to PyPI. Pg_config is required to build psycopg2 from source. $ docker run python:3.10-rc-slim pip install psycopg2-binary Let’s try running Python 3.10 under Docker:
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Update Oct 12, 2021: The final Docker 3.10 image is now available. With that in mind, let’s consider the problems with using 3.10 on its release day, and in the following months: 1.
In addition, because upgrades involve so many different groups, coordination and releases take even more time. None of this should be read as a complaint towards the people doing the maintenance, they’re doing hugely valuable work for free, and everything takes time. I originally wrote this article on October 5th, 2021, the day after 3.10 was released… and far too early to start using Python 3.10.Īs with many open source projects, Python, Python libraries, and most of the toolchain and packages mentioned in this article are based on volunteer labor. The problems with a new major Python release We can then make a guess about when Python 3.10 will actually be usable.
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To understand why, we need to consider Python packaging, the software development process, and take a look at the history of past releases. Now that some time has passed, we’re getting closer to yes, or at least a maybe. The short answer after its immediate release was, no, you probably don’t want to switch immediately quite possibly you can’t switch immediately. Python 3.10 is now available–but should you switch to it immediately? The np package also provides a convenient way of ensuring something is a numpy array, that is, a shortcut to numpy.asarray(): > import np > mylist = > mylist + > np ( mylist ) + array ()Īs an experimental feature, there are also shortcuts for giving the arrays a specific data type (numpy dtype): > np array () > np. The above also shows how you can use np.m and colons to easily create matrices (NxM) or row vectors (1xM). Also, the syntax np resembles the syntax for bytes literals, b"asd". Since most people would have numpy imported as np anyway, this requires no additional names to clutter the namespace. m Īs you can see from the above example, you can create numpy arrays by subscripting the np module. The above code becomes: > import np > my_array = np > my_2d_array = np, ] > my_matrix = np.
The most important feature of np is to make the creation of arrays less verbose, while everything else works as before. Even before the np tool, a popular style of using numpy has been to import it as np: > import numpy as np > my_array = np.
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