# Jupyter introduction ## Useful links + Project home: https://jupyter.org/ + A Youtube video summarizing the whole thing (very well done, about half an hour) : https://www.youtube.com/watch?v=HW29067qVWk + A quick description of the Jupyter notebook: http://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/What%20is%20the%20Jupyter%20Notebook.ipynb# + Try Jupyter online and learn about the main features in more details: https://jupyter.org/try. It will launch a Jupyter server online, so no installation is needed. + A tutorial with a steeper learning curve, for people who don't know about Jupyter, but already have a good background in Computer Science: https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook + A gallery of interesting Jupyter notebooks: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks ## Instructions for Jupyter installation The instructions are also in the links above, but more details are given here. Pre-requisite: knowing how to access the command line on your computer. A simple Google search should indicate how to proceed, depending on your platform (Windows, Mac OS, Linux). Using the command line is not strictly necessary to run Jupyter, but it's highly recommended. ### Installing Anaconda Python usually comes in what's called distributions, such as Anaconda, Canopy, IPython, or PyPy. [Quite](https://pythonforengineers.com/stop-struggling-with-python-on-windows/) a [number](https://pyscience.wordpress.com/2014/09/01/anaconda-the-creme-de-la-creme-of-python-distros-3/) of [people](https://www.reddit.com/r/Python/comments/3t23vv/what_advantages_are_there_of_using_anaconda/) think Anaconda is the best Python distribution currently available. Basically it's (1) easy to install on all platforms, (2) a well-thought package manager, and (3) allows to create environments to separate the Python installations you need for your different projects. A package manager is a software that automatically handles package dependencies (here for Python) without you having to worry about it. An environment is a container that allows you to have a specific Python installation fully separated from your main installation. Anaconda allows you to switch very easily between environments. Visit the [Anaconda download page](https://www.anaconda.com/download/) to find download and install instructions. It should be fairly easy, whichever platform you use. The installation process should (automatically) add conda to your PATH, which means that your system recognizes your new installation as a valid Python distribution. ### Launching Jupyter Type on your command line ``` jupyter notebook ``` The startup folder in the Jupyter dashboard is the one you launched the command from. Or launch Jupyter from the Anaconda interface, if the distribution you installed has one. ### Optional: creating a conda environment with Jupyter Once you have Anaconda installed, you should be able to create an environment with Jupyter. The following instruction (on the command line) should do the job; it creates an environment called `myenv` and installs Python 3.6, Jupyter notebook, and all theirs dependencies in this environment. ```bash conda create -n myenv python=3.6 notebook ``` To enter your environment and launch Jupyter notebook: ```bash source activate myenv # on Linux and Mac OS activate myenv # on Windows jupyter notebook ``` If you want more info about conda envs, please visit the [official documentation](https://conda.io/docs/user-guide/tasks/manage-environments.html).