Note For triton specific instructions see triton python page. For Aalto Linux workstation specific stuff, see Aalto python page.
Python is widely used high level programming language that is widely used in many branches of science.
Python to use
How to install own packages
Simple programs with common packages, not switching between Pythons often
Most of the use cases, but sometimes different versions of modules needed
conda environment + conda
Special advanced cases.
Python from module system
virtualenv + pip install
There are two main versions of python: 2 and 3. There are also different distributions: The “regular” CPython that is usually provided with the operating system, Anaconda (a package containing cpython + a lot of other scientific software all bundled together), PyPy (a just-in-time compiler, which can be much faster for some use cases).
For general scientific/data science use, we suggest that you use Anaconda. It comes with the most common scientific software included, and is reasonably optimized.
PyPy is still mainly for advanced use (it can be faster under certain cases, but does not work everywhere). It is available in a module.
Installing your own packages with “pip install” won’t work unless you have administrator access, since it tries to install globally for all users. Instead, you have these options:
pip install --user: install a package in your home directory (
~/.local/lib/pythonN.N/). This is quick and effective, but if you start using multiple versions of Python, you will start having problems and the only recommendation will be to delete all modules and reinstall.
Virtual environments: these are self-contained python environment with all of its own modules, separate from any other. Thus, you can install any combination of modules you want, and this is most recommended.
Anaconda: use conda, see below
Normal Python: virtualenv + pip install, see below
Installing own packages: Virtualenv, conda, and pip¶
You often need to install your own packages. Python has its own package manager system that can do this for you. There are three important related concepts:
pip: the Python package installer. Installs Python packages globally, in a user’s directory (
--user), or anywhere. Installs from the Python Package Index.
virtualenv: Creates a directory that has all self-contained packages that is manageable by the user themself. When the virtualenv is activated, all the operating-system global packages are no longer used. Instead, you install only the packages you want. This is important if you need to install specific versions of software, and also provides isolation from the rest of the system (so that you work can be uninterrupted). It also allows different projects to have different versions of things installed. virtualenv isn’t magic, it could almost be seen as just manipulating PYTHONPATH, PATH, and the like. Docs: https://docs.python-guide.org/dev/virtualenvs/
conda: Sort of a combination of package manager and virtual environment. However, it only installed packages into environments, and is not limited to Python packages. It can also install other libraries (c, fortran, etc) into the environment. This is extremely useful for scientific computing, and the reason it was created. Docs for envs: https://conda.io/projects/conda/en/latest/user-guide/concepts/environments.html.
So, to install packages, there is pip and conda. To make virtual environments, there is venv and conda.
Advanced users can see this rosetta stone for reference.
Anaconda is a Python distribution by Continuum Analytics. It is nothing fancy, they just take a lot of useful scientific packages and put them all together, make sure they work, and do some sort of optimization. They also include all of the libraries needed. It is also all open source, and is packaged nicely so that it can easily be installed on any major OS. Thus, for basic use, it is a good base to start with. virtualenv does not work with Anaconda, use conda instead.
Watch a Research Software Hour episode on conda for an introduction + demo.
A conda environment lets you install all your own packages. For instructions how to create, activate and deactivate conda environments see http://conda.pydata.org/docs/using/envs.html .
A few notes about conda environments:
Once you use a conda environment, everything goes into it. Don’t mix versions with, for example, local packages in your home dir. Eventually you’ll get dependency problems.
Often the same goes for other python based modules. We have setup many modules that do use anaconda as a backend. So, if you know what you are doing this might work.
The commands below will fail:
conda create -n foo pip# tries to use the global dir, use the
conda create --prefix $WRKDIR/foo --clone root# will fail as our anaconda module has additional packages (e.g. via pip) installed.
Basic pip usage¶
pip install by itself won’t work, because it tries to install globally. Instead, use this:
pip install --user
Warning! If you do this, then the module will be shared among all
your projects. It is quite likely that eventually, you will get some
incompatibilities between the Python you are using and the modules
installed. In that case, you are on your own (simple recommendation is
to remove all modules from
~/.local/lib/pythonN.N and reinstall). If
you get incompatible module errors, our first recommendation will be to
remove everything installed this way and not do it anymore.
Virtualenv is default-Python way of making environments, but does not work with Anaconda.
# Create environment virtualenv DIR # activate it (in each shell that uses it) source DIR/bin/activate # install more things (e.g. ipython, etc.) pip install PACKAGE_NAME # deactivate the virtualenv deactivate