If you need such a package, you can use a virtual machine or a computer with the appropriate operating system. Note that not all packages are pre-packaged for all platforms (or will build on all platforms). Intel MKL support, which can boost performance. It is preferable to use conda, since the packages are often built with things you may not have at home, e.g.
If you are using Anaconda, start by using conda and failing that, use pip. XCode on Mac OS X or the “Development Tools†option on many Linux distributions or appropriate version of Microsoft Visual Studio and/or MinGW on Microsoft Windows) to use pip.Īll methods (conda, pip) will resolve dependencies. Note that you may need a development toolchain (e.g. Once installed, you can open a terminal and run (*nix/Mac OS X users)įor the current user. We still recommend you install and use Anaconda, as the system install of Python will not come with many of the packages we will need for this course and may change depending on operating system updates. If you are a *nix or Mac OS X user, note that your system likely ships with a Python install. You may need you to manually install some packages using the built-in package manager (detailed below). These Python distributions contain most of what you will need for the course.
#Install anaconda for mac continuum how to#
We recommend you install Continuum Anaconda with Python 3 (do not install the Python 2 version or miniconda – they are not suitable for this course if you have the Python 2 version installed, see the Anaconda documentation on how to setup a Python 3 environment). Assignments will be distributed as Jupyter Notebooks (formerly known as IPython Notebooks). In this course, we will be making use of Python 3 and several freely available packages for big data.
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