Anaconda is a distribution of packages built for data science. It comes with conda, a package and environment manager. You'll be using conda to create environments for isolating your projects that use different versions of Python and/or different packages. You'll also use it to install, uninstall, and update packages in your environments. Using Anaconda has made my life working with data much more pleasant.
With Anaconda, it's simple to install the packages you'll often use in data science work. You'll also use it to create virtual environments that make working on multiple projects much less mind-twisting. Anaconda has simplified my workflow and solved a lot of issues I had dealing with packages and multiple Python versions.
Anaconda is actually a distribution of software that comes with conda, Python, and over 150 scientific packages and their dependencies. The application conda is a package and environment manager. Anaconda is a fairly large download (~500 MB) because it comes with the most common data science packages in Python. If you don't need all the packages or need to conserve bandwidth or storage space, there is also Miniconda, a smaller distribution that includes only conda and Python. You can still install any of the available packages with conda, it just doesn't come with them.
Conda is a program you'll be using exclusively from the command line, so if you aren't comfortable using it, check out this command prompt tutorial for Windows or our Linux Command Line Basics course for OSX/Linux.
You probably already have Python installed and wonder why you need this at all. Firstly, since Anaconda comes with a bunch of data science packages, you'll be all set to start working with data. Secondly, using conda to manage your packages and environments will reduce future issues dealing with the various libraries you'll be using.
Package managers are used to install libraries and other software on your computer. You’re probably already familiar with pip, it’s the default package manager for Python libraries. Conda is similar to pip except that the available packages are focused around data science while pip is for general use. However, conda is not Python specific like pip is, it can also install non-Python packages. It is a package manager for any software stack. That being said, not all Python libraries are available from the Anaconda distribution and conda. You can (and will) still use pip alongside conda to install packages. Conda installs precompiled packages. For example, the Anaconda distribution comes with Numpy, Scipy and Scikit-learn compiled with the MKL library, speeding up various math operations. The packages are maintained by contributors to the distribution which means they usually lag behind new releases. But because someone needed to build the packages for many systems, they tend to be more stable (and more convenient for you).
Along with managing packages, Conda is also a virtual environment manager. It's similar to virtualenv and pyenv, other popular environment managers.
Environments allow you to separate and isolate the packages you are using for different projects. Often you’ll be working with code that depends on different versions of some library. For example, you could have code that uses new features in Numpy, or code that uses old features that have been removed. It’s practically impossible to have two versions of Numpy installed at once. Instead, you should make an environment for each version of Numpy then work in the appropriate environment for the project.
This issue also happens a lot when dealing with Python 2 and Python 3. You might be working with old code that doesn’t run in Python 3 and new code that doesn’t run in Python 2. Having both installed can lead to a lot of confusion and bugs. It’s much better to have separate environments.
You can also export the list of packages in an environment to a file, then include that file with your code. This allows other people to easily load all the dependencies for your code. Pip has similar functionality with pip freeze > requirements.txt.
Anaconda is available for Windows, Mac OS X, and Linux. You can find the installers and installation instructions at https://www.continuum.io/downloads.
If you already have Python installed on your computer, this won't break anything. Instead, the default Python used by your scripts and programs will be the one that comes with Anaconda.
Choose the Python 3.6 version, you can install Python 2 versions later. (For Machine Learning Engineer Nanodegree you need Python 2 version) Also, choose the 64-bit installer if you have a 64- bit operating system, otherwise go with the 32-bit installer. Go ahead and choose the appropriate version, then install it. Continue on afterwards!
After installation, you’re automatically in the default conda environment with all packages installed which you can see below. You can check out your own install by entering conda list into your terminal.
Once you have Anaconda installed, managing packages is fairly straightforward. To install a package, type conda install package_name in your terminal. For example, to install numpy, type conda install numpy.
You can install multiple packages at the same time. Something like conda install numpy scipy pandas will install all those packages simultaneously. It's also possible to specify which version of a package you want by adding the version number such as conda install numpy=1.10.
Conda also automatically installs dependencies for you. For example scipy depends on numpy, it uses and requires numpy. If you install just scipy (conda install scipy), Conda will also install numpy if it isn't already installed.
Most of the commands are pretty intuitive. To uninstall, use conda remove package_name. To update a package conda update package_name. If you want to update all packages in an environment, which is often useful, use conda update --all. And finally, to list installed packages, it's conda list which you've seen before.
If you don't know the exact name of the package you're looking for, you can try searching with conda search search_term. For example, I know I want to install Beautiful Soup, but I'm not sure of the exact package name. So, I try conda search beautifulsoup.
It returns a list of the Beautiful Soup packages available with the appropriate package name, beautifulsoup4.
As I mentioned before, conda can be used to create environments to isolate your projects. To create an environment, use conda create -n env_name list of packages in your terminal. Here -n env_name sets the name of your environment (-n for name) and list of packages is the list of packages you want installed in the environment. For example, to create an environment named my_env and install numpy in it, type conda create -n my_env numpy.
When creating an environment, you can specify which version of Python to install in the environment. This is useful when you're working with code in both Python 2.x and Python 3.x. To my personal computer. I use them as general environments not tied to any specific project, but rather for general work with each Python version easily accessible. These commands will install the most recent version of Python 3 and 2, respectively. To install a specific version, use conda create -n py python=3.3 for Python 3.3.
Once you have an environment created, use source activate my_env to enter it on OSX/Linux. On Windows, use activate my_env.
When you're in the environment, you'll see the environment name in the terminal prompt. Something like (my_env) ~ $. The environment has only a few packages installed by default, plus the ones you installed when creating it. You can check this out with conda list. Installing packages in the environment is the same as before: conda install package_name. Only this time, the specific packages you install will only be available when you're in the environment. To leave the environment, type source deactivate (on OSX/Linux). On Windows, use deactivate.
A really useful feature is sharing environments so others can install all the packages used in your code, with the correct versions. You can save the packages to a YAML file with conda env export > environment.yaml. The first part conda env export writes out all the packages in the environment, including the Python version.
Above you can see the name of the environment and all the dependencies (along with versions) are listed. The second part of the export command, > environment.yaml writes the exported text to a YAML file environment.yaml. This file can now be shared and others will be able to create the same environment you used for the project.
To create an environment from an environment file use conda env create -f environment.yaml. This will create a new environment with the same name listed in environment.yaml.
If you forget what your environments are named (happens to me sometimes), use conda env list to list out all the environments you've created. You should see a list of environments, there will be an asterisk next to the environment you're currently in. The default environment, the environment used when you aren't in one, is called root.
You can specify the environment to install to in the install command
conda install -n env-name package-name
Or you can activate the environment, then install
[source] activate env-name
conda install package-name
If there are environments you don't use anymore, conda env remove -n env_name will remove the specified environment (here, named env_name).
NOTE: If you already have an environment.yml file in your current directory, it will be overwritten during this task.
Activate the environment to export:
Windows:
activate myenv
macOS and Linux:
source activate myenv
NOTE: Replace myenv with the name of the environment. Export your active environment to a new file:
conda env export > environment.yml
NOTE: This file handles both the environment’s pip packages and conda packages.
Email or copy the exported environment.yml file to the other person.
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