Skip to content

jdcla/DS-python-data-analysis

 
 

Repository files navigation

Data manipulation, analysis and visualisation in Python

Specialist course Doctoral schools of Ghent University

For how to set up a working environment, fetch the course materials and start the notebooks, see the slides: https://jorisvandenbossche.github.io/DS-python-data-analysis/

Requirements to run this tutorial

Look at the environment.yml file for all packages that need to be installed.

Setting up a working environment

Getting the course materials

  • With using git ([1] and recommended for [2])

    First time (inside a terminal or cmd):

$ git clone https://github.com/jorisvandenbossche/DS-python-data-analysis.git $ cd DS-python-data-analysis


Updating (on second or third day):

$ git pull


* Without git for [2]: download ZIP from https://github.com/jorisvandenbossche/DS-python-data-analysis (green button "Clone or download")


### Create an environment with Anaconda (for [2])

(*make sure you have a working internet connection*)

Use our [environment.yml](https://github.com/jorisvandenbossche/DS-python-data-analysis/blob/master/environment.yml) file inside the course folder to set up your Python working environment

In a terminal or cmd, navigate to the `DS-python-data-analysis` folder and use the following command:

$ conda env create -f environment.yml


When the environment is installed, activate the environment inside the terminal/cmd:

* Windows-users

$ activate DS-python-data-analysis


* Linux/Mac-users

$ source activate DS-python-data-analysis


### Starting a Jupyter Notebook (for [2])

* In the terminal, navigate to the `DS-python-data-analysis` directory if not there already

* Ensure that the correct environment is activated.

* Start a jupyter notebook server with

$ jupyter notebook


This will open a browser window automatically.



Authors: Joris Van den Bossche, Stijn Van Hoey

About

Data manipulation, analysis and visualisation in Python - specialist course Doctoral schools of Ghent University

Resources

License

Contributing

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.3%
  • Other 0.7%