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.