Tutorial: Mike Müller - Migration from Python 2 to 3

Presented by: Mike Müller Python 2 reached end of life (EOL). However, there are still many projects that use Python 2. Staying with Python 2 is certainly no long-term option for most projects. Porting a larger program to Python 3 brakes backward compatibility with Python 2. One solution for this problem is the single-source approach that results in source code that runs with Python 2 and 3 without any changes. This tutorial provides a short overview over the most important differences between Python 2 and 3. After looking at different approaches for Python 3 support, the focus will be on writing single-source programs with python-future. You will learn how to port to Python 3 without loosing Python 2 support. Finally dropping Python 2 support will get as simple as removing a few imports. The content of this tutorial is inspired by questions I received from participants in trainings about how to smoothly transition from Python 2 to Python 3. Making Python 2 working as much as possible as Python 3 seems the best option to me. You are encouraged to bring your questions about this topic. I teach about this topic on a regular basis in my trainings. Being a full-time profession trainer, I have plenty opportunity to get involved with many programmers who have to deal with this problem. Software Requirements You will need Python 3.8 installed on your laptop. Python 3.6/3.7 should also work. You also need Python 2.7 installed to test if the code runs with Python 2 and Python 3. You may use Python 3.9 if is released at the time of the tutorial and all dependencies can be installed. JupyterLab I will use a JupyterLab for the tutorial because it makes a very good teaching tool. You are welcome to use the setup you prefer, i.e editor, IDE, REPL. If you also like to use a JupyterLab, I recommend conda for easy installation. Similarly to virtualenv, conda allows creating isolated environments but allows binary installs for all platforms. There are two ways to install Jupyter via conda: Use Minconda. This is a small install and (after you installed it) you can use the command conda to create an environment: conda create -n pycon2020py38 python=3.8 Now you can change into this environment: conda activate pycon2020py38. The prompt should change to (pycon2020py38). Now you can install JupyterLab: conda install jupyterlab. Do the same with Python 2.7, i.e. conda create -n pycon2020py27 python=2.7 and activate accordingly conda activate pycon2020py27. Install the dependencies: Jupyter Lab 2 ‘conda install jupyterlab’ six ‘conda install six’ python-future ‘conda install future’ Hint: You do all this in one command: conda create -n pycon2020py38 python=3.8 six jupyterlab future and conda create -n pycon2020py27 python=2.7 six jupyterlab future You can create a comparable setup with virtual environments and pip, if you prefer. Working with conda environments After creating a new environment, the system might still work with some stale settings. Even when the command which tells you that you are using an executable from your environment, this might actually not be the case. If you see strange behavior using a command line tool in your environment, use hash -r and try again. https://www.python-academy.com/download/pycon_2020/tutorial_migration_2to3.zip https://www.python-academy.com/download/pycon_2020/solutions.zip