Skip to content

DataCompetency/PythonAdvanced

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

20 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Advanced Python Course

Is sometimes held on-site at the university of Greifswald. The course materials can also be used for independent self-study.

Requirements

The participants of this course should be comfortable using Python for simple scripts and should know the procedural or functional programming style. In particular, they should be familiar with handling of built-in datatypes, variables, control flow structures, defining and structuring code in functions and creating and loading modules.

Preparations

The course materials consist of Jupyter Notebooks. A Jupyter notebook is an interactive Python environment allowing to combine interactive execution of code along with formatted description texts, like an enriched, interactive lecture manuscript.

Option 1: Install Anaconda on your machine

In case you prefer to use AppHub (in case you have university login credentials), you can skip this setup section. If you'd like to write and test your code independently from the university infrastructure and start from scratch, install the Jupyter environment locally on your machine, following the instructions below:

Linux

Anaconda for Linux

Mac OS X

Anaconda for Mac OS X

Windows

Anaconda for Windows

or checkout this link to download the newest version.

Optional: Jupyter Notebook Extensions and Widgets

In order to use useful notebook extensions like e.g code completion, autoformatting to comply with PEP8 etc. install the extensions using conda with

conda install -c conda-forge jupyter_contrib_nbextensions
conda install -c conda-forge jupyter_nbextensions_configurator
conda install -c conda-forge ipywidgets
jupyter contrib nbextension install --user

A new tab named "NbExtensions" should appear now where you can manage all Jupyter notebook extensions.

A very useful extensions is "Hinterland" offering autocompletion like in the popular Python IDEs e.g. PyCharm.

IPython widgets allow interactive GUI elements to be used in Jupyter notebooks which can be very useful for interactive data visualizations.

Option 2: Use AppHub from within the university intranet

If you're already connected via Wifi to eduroam or via LAN in the university, you can access the AppHub, a Jupyter Notebook server runnning in the university cloud. As a student or employee of the university, you can use your default login credentials to login here

https://apphub.wolke.uni-greifswald.de/

then login with your credentials and choose "Datascience" as server type.

Option 3: Use AppHub remotely from home or anywhere else

If you are not connected to eduroam - or - if you're working from home, you have to install and setup a VPN client. Follow these instructions to install and setup the VPN client for the university of Greifswald before you can use the AppHub in the university cloud. In any case you need the university login credentials to have VPN access to the university intranet.

Download the course materials

If you're using the AppHub, either open a new Python 3 notebook and copy the lines

%%bash
git clone https://github.com/DataCompetency/PythonAdvanced

into a cell of a new notebook and then execute the cell with CTRL + Enter.

If you're using a locally installed version of Anaconda, simply go to an arbitrary directory and clone the github repository with the course materials using the following command in a terminal:

git clone https://github.com/DataCompetency/PythonAdvanced

Then simply open one of the Jupyter notebooks. Have fun !

Create a Github account

In order to follow the github crash course being part of this seminar, you should have your own github account. If you don't have one, signup beforehand.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published