Table of Content

• hu_wiwi_grades
• Background
• Installation
• Requirements
• Usage
• License

hu_wiwi_grades

hu_wiwi_grades is a Python library for searching, viewing and scraping published students' grading of the Faculty of Economics and Business Administration of the Humboldt University of Berlin.

Please note: The functionality maybe interrupted in case any changes in the publication occur or in case the website is not available.

Background

This library was primarily created for testing/training purposes, such as extracting information from PDF files, writing and publishing of code. It nevertheless aims to offer a use-case. Current and historical grading information may be of interest for (prospective) students, examiners or potentially even employers.

Installation

Use the package manager pip to install hu_wiwi_grades

pip install hu_wiwi_grades

Or install it through the author's Github repository

pip install git+https://github.com/NDelventhal/hu_wiwi_grades

Requirements

The following libraries are required:

• tabula
• pandas
• numpy
• requests
• beautifulsoup4

These libraries can be installed via the package manager pip.

pip install tabula numpy pandas requests beautifulsoup4

Usage

import hu_wiwi_grades as hu

hu.list_sources() 
# scrapes URL sources that list grading overviews and returns a dictionary containing the semesters as keys and the URLs as values. 

df = hu.scrape_overview(exam = "Economics") 
# Scrapes the latest grading overview and returns the overview or a subset based on the entered exam specification.
# In this example solely Economics exams are returned. The exam arguments defaults to "" (no filtering).  

df = hu.scrape_all_overviews(exam = "Valuation") 
# Same as above, but instead of solely the latest overview all historical overviews are pulled. Typically, a few semesters are available.

df = hu.get_grading(exam="", only_current_semester = True) 
# Scrapes the grades from the URLs listed in the overview pages of either only the latest semester (only_current_semester = True) or all (only_current_semester = False). 
# An exam filter may be specified as in the examples above or not.
# Returns a dataframe listing the number of participants, the examiner and all grades as variables. 

df2 = hu.prepare_for_analysis(df) 
# Prepares the dataframe output of get_grading() for further analysis, such as visualisations, descriptive statistics or regression analysis.

Further usuage examples are listed here

License

This project is licensed under the MIT License.

Contact

• The author: Niall Delventhal - [email protected]