This short primer is an introduction to the scientific Python stack for Data Science. It is designed as a tour around the major Python packages used for the main computational tasks encountered in the sexiest job of the 21st century. At the end of this tour, you'll have a broad overview of the available libraries as well as why and how they are used for each task. This notebook aims at answering the following question: which tool should I use for which task and how.

The primer is a Jupyter notebook.

• The easiest way to play with it from your browser without installing anything is to click on the binder badge.
• If you only want to look at it, open the HTML version rendered by nbviewer.
• The most interactive way is to run the code by yourself, after installing Python and the required packages on your computer.

  # brew / apt-get / yum / pacman
  package-manager install python3
  # virtual environment
  pyvenv /path/to/new/virtual/env
  . /path/to/new/virtual/env/bin/activate
  # clone repository
  git clone https://github.com/mdeff/python_tour_of_data_science.git
  cd python_tour_of_data_science
  make install  # install the dependencies (requirements.txt)
  make          # run the notebook to be sure everything is fine
  make clean    # clear the generated outputs
  # display notebook
  jupyter notebook

All codes and examples are released under the terms of the MIT License.