Python 101 - Anaconda, environments, and more¶
This is a small introduction of what I think you should have installed in order to property use python in your projects.
The very first thing for you to have is Anaconda installed. From their website:
With over 4.5 million users, Anaconda is the world’s most popular Python data science platform. Anaconda, Inc. continues to lead open source projects like Anaconda, NumPy and SciPy that form the foundation of modern data science. Anaconda’s flagship product, Anaconda Enterprise, allows organizations to secure, govern, scale and extend Anaconda to deliver actionable insights that drive businesses and industries forward.
Having Anaconda installed on your computer is important, since it allows you to not worry about installing missing dependencies, creates environments for you projects, etc.
The first thing is to download Anaconda. If you’re starting with Python from scratch, it is better to start with Python 3.
You can download Anaconda from here: https://www.anaconda.com/download, and make sure to download the Python 3 version.
If you’re downloading it from the terminal, you can download the executable from by typing:
>>> wget https://repo.continuum.io/archive/Anaconda3-5.0.1-MacOSX-x86_64.pkg /path/to/download/to/
>>> bash /path/to/download/to/Anaconda3*.sh
For more information on how to download it, go to https://docs.anaconda.com/anaconda/install/#detailed-installation-information
Once you have downloaded Anaconda, you should be able to start using python and iPython. You can try this by typing the following on the terminal:
>>> which python /home/username/anaconda/bin/python >>> which ipython /home/username/anaconda/bin/ipython >>> ipython Python 3.6.3 |Anaconda custom (64-bit)| (default, Oct 6 2017, 12:04:38) Type 'copyright', 'credits' or 'license' for more information IPython 6.1.0 -- An enhanced Interactive Python. Type '?' for help.
If you’re using a separate machine, to which you ssh, you can install Anaconda to a specified location other than your home directory. This is important if you are limited by the number of files in your **home directory*, e.g. a computer hosted by ACCRE.
When working on a project, it is really important to keep reproducibility in mind. For example, if you were to hand me you code, I should be able to read the documentation and understand it, as well as running the code.
This is why creating an environment for your project is extremely important. This is where Ananconda helps a lot. Anaconda let’s you have your own defined environment for your project, and you can specify which packages to include in your project.
All of the packages can be specified in an environment.yml file. An example for such file would look like (taken from Conda Manage Environments):
name: example-environment channels: - defaults dependencies: - python=3 - anaconda - astropy - h5py - numpy - pandas - scipy - seaborn - pip - pip: - GitPython - progressbar2 - halotools - sphinx_rtd_theme
You can install the desired environment example-environment by running the command on the terminal:
>>> conda env create -f environment.yml
For more information, see Creating an environment from an environment.yml file.
A helpful package to use is conda-env-auto which allows you to automatically create and activate the project environment once you are in the directory. For more information on how to install it and use it, see https://github.com/chdoig/conda-auto-env.