The nature of these changes is such that Python 3 was incompatible with Python 2. It is backward incompatible. Some features of Python 3 have been backported to Python 2. As a result, for any organization who was using Python 2. These changes not only relate to projects and applications but also all the libraries that form part of the Python ecosystem.
Although, Python 2 is an old open source version here are where you still need to learn Python When it comes to Python 2 vs Python 3 differences today, Python 3 version is the outright winner.
Mass Python 3 adoption is the clear direction of the future. After considering declining support for Python 2 programming language and added benefits from upgrades to Python 3, it is always advisable for a new developer to select Python version 3. However, if a job demands Python 2 capabilities, that would be an only compelling reason to use this version. If you are writing documentation, and want the additional safety that the correct version of Python is being used by your reader you can specify the major and minor version number in the command, like so:.
If the reader is using a version other than 3. However, any patch version for example 3. When the environment is active, any packages can be installed to it via pip as normal. By default, the newly created environment will not include any packages already installed on the machine. As pip itself will not necessarily be installed on the machine. It is recommended to first upgrade pip to the latest version, using pip install --upgrade pip.
Projects will commonly have a requirements. This allows the shortcut command pip install -r requirements. They will only exist in the virtual environment. It will not be available when it is deactivated but will persist when it is reactivated. If you do not need to use additional versions of Python itself, then this is all you need to create isolated, project specific, virtual environments.
If you wish to use multiple versions of Python on a single machine, then pyenv is a commonly used tool to install and switch between versions. This is not to be confused with the previously mentioned depreciated pyvenv script. It does not come bundled with Python and must be installed separately. The pyenv documentation includes a great description of how it works , so here we will look simply at how to use it.
Firstly we will need to install it. Next, add the following towards the bottom of your shell scripts to allow pyenv to automatically change versions for you:. To install an additional version, say 3.
When setting up a new project that is to use Python 3. This would both set the version, and create a. The full description of pyenv commands is one to bookmark.
When working with Python 3. If we then ran python3 -m venv example-project a new virtual environment would be set up under example-project , using our locally enabled Python 3. Which means a follow-up release with plenty of bug fixes. New versions of Python often have new syntax, and that is the case with Python 3. However, other tools need to support the new syntax too—autoformatters, linters, and so on.
Given that it takes work to upgrade—some additional testing, some tweaks to your code—it can be tempting to put off upgrading Python versions indefinitely. Why worry about incompatibilities, new versions, and what not, when you can just stick with your current version indefinitely? Which makes upgrading scary. And sooner or later you will have to upgrade.
Whenever a new major Python version comes out, or a new major library version, wait a bit, and then switch. Which is to say that at the moment, you really should be using 3. In addition to dependency availability and toolchain support, at that point you may also have access to the first point release, 3. If dependencies are still missing, keep trying again every month. With as much as a dozen different intersecting technologies, and an unknown number of details to get right, Docker packaging isn't simple, especially for production.
But you still need fast builds that save you time, and security best practices that keep you safe. Take the fast path to learning best practices, by using the Python on Docker Production Handbook.
0コメント