If you've decided to install an Anaconda distribution, you can take advantage of the conda binary installer we mentioned previously. conda is an open source package management system, and consequently, it can be installed separately from an Anaconda distribution. The core difference from pip is that conda can be used to install any package (not just Python's ones) in a conda environment (that is, an environment where you have installed conda and you are using it for providing packages). There are many advantages in using conda over pip,  as described by Jack VanderPlas in this famous blog post of his: jakevdp.github.io/blog/2016/08/25/conda-myths-and-misconceptions.

You can test immediately whether conda is available on your system. Open a shell and digit the following:

$> conda -V

If conda is available, your version will appear; otherwise, an error will be reported. If conda is not available, you can quickly install it on your system by going to http://conda.pydata.org/miniconda.html and installing the Miniconda software that's suitable for your computer. Miniconda is a minimal installation that only includes conda and its dependencies.

Conda can help you manage two tasks: installing packages and creating virtual environments. In this paragraph, we will explore how conda can help you easily install most of the packages you may need in your data science projects.

Before starting, please check that you have the latest version of conda at hand:

$> conda update conda

Now you can install any package you need. To install the <package-name> generic package, you just need to run the following command:

$> conda install <package-name>

You can also install a particular version of the package just by pointing it out:

$> conda install <package-name>=1.11.0

Similarly, you can install multiple packages at once by listing all their names:

$> conda install <package-name-1> <package-name-2> 

If you just need to update a package that you previously installed, you can keep on using conda:

$> conda update <package-name>

You can update all the available packages simply by using the --all argument:

$> conda update --all

Finally, conda can also uninstall packages for you:

$> conda remove <package-name>

If you would like to know more about conda, you can read its documentation at http://conda.pydata.org/docs/index.html. In summary, as its main advantage, it handles binaries even better than easy_install (by always providing a successful installation on Windows without any need to compile the packages from source) but without its problems and limitations. With the use of conda, packages are easy to install (and installation is always successful), update, and even uninstall. On the other hand, conda cannot install directly from a git server (so it cannot access the latest version of many packages under development), and it doesn't cover all the packages available on PyPI like pip itself.