Bootstrap your base conda environment
In a pinch, you might want to muck around on your system with some quick-and-dirty experiment. Having a suite of packages inside your base environment can be handy. It's like having a scratch environment available.
I would recommend bootstrapping your base anaconda environment with some basic data science packages.
conda activate base conda install -c conda-forge \ scipy numpy pandas matplotlib \ numpy jupyter jupyterlab \ scikit-learn ipython ipykernel \ ipywidgets mamba
Doing so gives you an environment where you can quickly prototype new things without necessarily going through the overhead of creating an entirely new project (and with it, a full conda environment). Of course, the alternative is to set up a
scratch environment, in which you install packages on-the-fly.
mamba can be helpful if you want a faster drop-in replacement for
conda. (see: Use Mamba as a faster drop-in replacement for conda for more information.)
Install Anaconda on your machine
Anaconda is a way to get a Python installed on your system.
One of the neat but oftentimes confusing things about Python is that you can have multiple Python executables living around on your system. Anaconda makes it easy for you to:
Why is this a good thing? Primarily because you might have individual projects that need different version of Python and different versions of packages that are built for Python. Also, default Python installations, such as the ones shipped with older versions of macOS, tend to be versions behind the latest, which is to the detriment of your projects. Some built-in apps in an operating system may depend on that old version of Python (such as iPhoto), which means if you mess up the installation, you might break those built-in apps. Hence, you will want a tool that lets you easily create isolated Python environments.
The Anaconda Python distribution fulfills the following key needs:
Installing Anaconda on your local machine thus helps you get easy access to Python, Jupyter (see: Use Jupyter as an experimentation playground), and other tools for modelling and analysis.
If you're on macOS: I'm assuming you have installed
homebrew (see: Install homebrew on your machine) and
wget. Then, install Miniconda, which will be a lighter-weight installer, using the following command:
cd ~ wget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O anaconda.sh
This will send you to your home directory, and then download the Miniconda bash script installer from Anaconda's download page.
If you're on Linux: Make sure you have
wget available on your system. Then:
cd ~ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O anaconda.sh
This will download the Miniconda installer for Linux operating sytems onto your home directory.
If you don't have
wget: You can head over to the Miniconda docs and download the bash installer to whatever location you want (the home directory is a convenient place). Rename it to
anaconda.sh to stay compatible with the instructions below.
Now, install Anaconda:
bash anaconda.sh -b -p $HOME/anaconda/
This will install the Anaconda distribution of Python onto your system inside your home directory. You can now install packages at will, without needing sudo privileges!
Configure your conda installation
Configuring some things with conda can help lubricate your interactions with the conda package manager. It will save you keystrokes at the terminal, primarily, thus saving you time. The place to do this configuration is in the
.condarc file, which the
conda package manager searches for by default in your user's home directory.
The condarc docs are your best bet for the full configuration, but I have some favourites that I'm more than happy to share below.
Firstly, you create a file in your home directory called
.condarc. Then edit it to have the following contents:
channels: - conda-forge - defaults auto_update_conda: True always_yes: True
auto_update_condasaves me from having to update conda all the time,
always_yeslets me always answer
yto the conda installation and update prompts.
conda-forgeas the default channel above the
defaultschannel allows me to type
conda install some_packagerather than
conda install -c conda-forge some_packageeach time I want to install a package, as conda will prioritize channels according to their order under the
If you prefer, you can set the channel priorities in a different order and/or expand the list. For example, bioinformatics users may want to add in the
bioconda channel, while R users may want to add in the
r channel. Users who prefer stability may want to prioritize
defaults ahead of
What this affects is how
conda will look for packages when you execute the
conda install command. However, it doesn't affect the channel priority in your per-project
environment.yml file (see: Create one conda environment per project).
Use Mamba as a faster drop-in replacement for conda
Mamba is a project originally developed by the Quantstack team. They went in and solved some of the annoyances with the conda package manager - specifically the problem of how long it takes to solve an environment specification.
Mamba is available on conda-forge and PyPI. Follow the instructions on the mamba repo to install it.
If you have muscle memory and want to make the switch from
mamba as easy as possible, you can use a shell alias inside your sourced
See the page Create shell command aliases for your commonly used commands for more information on shell aliases.
Configure your machine
After getting access to your development machine, you'll want to configure it and take full control over how it works. Backing the following steps are a core set of ideas:
Head over to the following pages to see how you can get things going.