Setting up your laptop for Scientific Computing with Python

General Advice on Operating System

Generally, you should use either a modern Linux or *nix distribution. For most users, we recommend Ubuntu. If you have a Mac, macOS is a custom flavor of *nix OS. Please do not try to install other systems such as Ubuntu or Arch Linux on a Mac if you don’t know what you are doing.

You should avoid using Windows as much as possible. For this module, we can only offer limited help if you are using Windows.

Which Python Distribution Should I Use?

If you are a newbie to Python, you would go to the official Python website, download the installation file and install it on your computer. In fact, this may be the worst thing you can do.

So, in most modern Linux and *nix operating systems, the Python distribution is installed for managing some system services by default. The system-installed Python can be used by the user for sure. However, because this distribution manages system services and needs constant writing privileges to some directories, we strongly recommend that you do not touch the system-installed Python as a new user. Moreover, you could encounter big problems when you are trying to install packages that build from scratch.

Instead, what we need is a Python distribution that has its own environment and can be easily removed when we want to. So the answer is Anaconda, a Python distribution that is built for Scientific Computing.

Anaconda delivers a custom Python distribution that includes over 1000 data science related packages, check here. After installation, all the Anaconda files are in a folder. If you messed something up, simply remove that folder and install it again!

Anaconda setup instructions

  1. Open a terminal and download Anaconda

     $ wget https://repo.continuum.io/archive/Anaconda2-5.1.0-Linux-x86_64.sh -O anaconda.sh  # for Linux

     $ curl https://repo.continuum.io/archive/Anaconda2-5.1.0-Linux-x86_64.sh -o anaconda.sh  # for macOS

  2. Install Anaconda

     $ bash ./anaconda.sh

    Follow the instructions and make sure Anaconda is added in your bash configuration file such as .bashrc or .zshrc.

  3. Close the current terminal and open another one (so that the bash configuration is loaded again). Type python, you should see something similar to this:

     Python 2.7.14 |Anaconda custom (64-bit)| (default, Nov  8 2017, 22:44:41)
 [GCC 7.2.0] on linux2
 Type "help", "copyright", "credits" or "license" for more information.
 >>>

Remark: For Windows OS, please read the installation instruction here. Or, remove your Windows and install Linux.

Which Python Version Should I Use?

Python 2.7

How to Install Other Python Packages?

Generally, with Anaconda, we have three ways of installing other Python packages.

  1. Use pip. pip is the official Python packaging system that manages package installation, uninstallation, version control, custom package building, etc. You can install additional Python packages by

     $ pip install some-package-name

    If the package is available in PyPi, pip will automatically pull the software from the website and install it.

  2. Use conda. Anaconda uses the packaging system conda to manage packages and libraries installation. At heart, conda does package, dependency and environment management for any language. conda can pull and install a pre-built package from a specific server and resolve the dependency accordingly.

     $ conda install some-package-name

  3. Use setup.py. A decent Python library usually has a setup.py script. With this script, you can install the package via

     $ python setup.py install

Do I Need Anything Else?

For a beginner who wants to learn Python for data science, you generally don’t need to install additional software packages. Anaconda has taken care of the software installation.

However, you should familiarize yourself with the software packaging system on your computer. For Debian family (including Ubuntu), that is apt-get or apt. For macOS, that is either MacPorts or homebrew.

IDE (Integrated Development Environment)

There are many different IDEs for Python on the market. Here, we list some of them that generally deliver fantastic coding experience.

Say NO to Jupyter Notebook

If you have taken a Python class or Python 101, you have probably seen that instructors like Jupyter Notebook as a default presentation tool. They even walk you through the steps so that you are comfortable with this kind of coding style.

However, we are strongly against this paradigm of coding. It is true that the Jupyter Notebook offered a nice presentation style. But it is not designed for managing and engineering serious projects.

Therefore, we advice that you do not use Jupyter Notebook.

What do you need from an IDE?

Atom

Atom is a very popular text editor that is built and maintained mainly by GitHub. This editor is modern and naturally integrates some of the best software engineering practices. You can find tons of additional packages that help you configure the editor to a Python IDE.

PyCharm

PyCharm is a new Python IDE that has a beautiful interface and integrates all the features you will need for developing a Python project.

Eclipse+PyDev

If you are a Eclipse user, perhaps you would be very happy to know that there is a dedicate Eclipse plugin for Python. PyDev takes advantages of the powerful Eclipse compiling and debugging framework.

Vim

For the experienced user, we recommend Vim or its community-driven fork neovim. By configuring Vim, you will be able to get a fast editor that has all the IDE features you want and being lightweight in some sense. Additionally, because Vim is a very flexible editor, you could do many things very efficient while other editors won’t or can’t do.

Further Readings