As more and more Python packages I need to install for my daily work and learn, I get tired of searching and installing individual packages, especially on my Window system, like what I did in how to install scientific Python packages on Windows. I decide to give Anaconda (by Continuum Analytics) a try, what it provides far exceed my expectation and I feel guilty did not try it earlier.


Anaconda is a free, cross-platform, and easy-to-install all-in-one analytic/scientific Python platform. Anaconda not only comes with various packages that I would like to use, such as NumPy, SciPy, matplotlib, pandas, scikit-learn,  and Jupyter/IPython, but also provides more functionalities, like creating and managing environments, installing and updating packages, and so on.

In this post, I describe some basic knowledge on Anaconda I have learned through my experience. If you did not start to use it yet, I highly recommend you a try.

Downloading and Installing

Installing Anaconda is as easy as 1-2-3:

  1. Go to Anaconda’s download page (

  2. download the right installer for your OS type (Window, OSX, Linux), OS version(32- or 64 bit), and Python version(2.7 or 3.5)

  3. Follow the instructions to install Anaconda

You might want to put Anaconda to your system path, so it will use Anaconda’s Python as the default python distribution. On Windows, you are recommended not automatically add Anaconda to your system path, instead you can manually add ANACONDA_HOME with value of ‘C:\User\your_user_name\ AppData\Local\Continuum\anacondX’ to the user varaibles and %ANACONDA_HOME%; %ANACONDA_HOME%\Scripts; %ANACONDA_HOME%\ Library\bin; to the user path variable.

Anaconda comes with a package manager named conda, which lets you manage your Python distributions and install new packages.

Managing Environments

Anaconda can help create different isolated Python environments. For example, you might use Python 3.5 as your default distribution, but sometimes you would switch to Python 2 for some rare cases.

Anaconda comes with a graphical launcher that you can use to  manage environments, install packages, start IPython, etc. You can find more details from

All these stuff can be realized through conda command. For example, a new environment for Python 2 (named py2, with Python 2.7) can be created through following command:

conda create -n py2 anaconda python=2.7

Then the new environment can be activated through

activate py2 on ** Windows**

source activate py2 on Linux or Mac OSX

, and be deactivated by:

deactivate py2 on ** Windows**

source deactivate py2 on Linux or Mac OSX

NOTE: For Windows system, the activate and deactivate commands do not work in PowerShell, but work in cmd. Here is one potential solution, if you want to keep using your PowerShell: Powershell activate and deactivate #626. You can also try another work around way: 1) type cmd in Powershell to switch to cmd line; 2) run activate or deactivate command; 3) type powershell to change back to Powershell.

Common Commands

Here is a list of frequently used conda commands, and you can see a longer list at the Conda cheet sheet.

  • conda info: Displays information about current conda install.

  • conda help: Displays the list of conda commands and their help strings.

  • conda list: Lists all packages installed in the current environment.

  • conda env list: Displays the list of environments installed and the currently active one is marked by a star *.

  • conda serarch _somepackage_: search for a package to see if it is available.

  • conda install _somepackage_: Installs a Python package_._

  • conda install _somepackage_=0.7: Installs a specific version of a package.

  • conda update _somepackage_: Updates a Python package to the latest available version.

  • conda update anaconda: Updates all packages.

  • conda update conda: Updates conda itself.

  • conda update --all: Updates all packages.

  • conda remove somepackage: Uninstalls a Python package.

  • conda remove -n _myenv_ --all: Removes the environment named myenv.

  • conda clean -t: Removes the old tarballs that are left over after installation and updates.

If the conda install somepackage fails, you can try pip install somepackage instead, which uses the PyPI instead of Anaconda. Many scientific Anaconda packages are easier to install than the corresponding PyPI packages because they are pre-compiled for your platform. However, many packages are available on PyPI but not on Anaconda.

A list of references about Anaconda: