Python

How to build a Text Adventure Game

Schattengabe - Tutorial Part 01

In den letzten Wochen und Monaten habe ich ein paar Projekte mit Django, dem Web Framework basierend auf Python, gearbeitet. Ich muss zugeben, am Anfang fand ich das alles ziemlich kompliziert. Django verlangt einen Berg an verschiedenen Dateien, die alle ineinander greifen, und wenn man an der einen Stelle etwas ändert, zieht das meistens dreimal soviele notwendige Änderungen in anderen Dateien mit sich. Je mehr ich allerdings mit dem System arbeitete und je mehr Seiten ich damit baute, desto…

Analyzing Learning and Working Behaviour of Students with E-Learning Support

Report - Individual Project - Spring Semester 2018

With proceeding digitization in educational realms, today’s learning more and more involves the use of Learning Management Systems (LMS). These systems (like ILIAS in the actual case) can be customized to individual needs and accessed and used with an ordinary browser. Within this online e-learning system, lecturers, tutors, and students are able to communicate and work together in various different ways. All this actions and connections between users, as they are happening…

Labelling Output

Identifying Influencer, Experts and Broker within the Network Structure of Twitter

import networkx as nx
import pylab as plt
import pandas as pd
from collections import defaultdict
g = nx.read_edgelist("C:/Users/laris/Desktop/PROJECT-WM/output.txt", delimiter=",", create_using=nx.DiGraph(), nodetype=int)
#udg = nx.Graph(g)
def dict_sort(cent_dict, pos):
    # Create ordered tuple of centrality data
    cent_items=[(b,a) for (a,b) in cent_dict.items()]
    # Sort in descending order
    cent_items.sort()
    cent_items.reverse()
    return…

Identifying Experts

Finding Experts in the Structure of the Twitter7 Following/Follower Network

import networkx as nx
import pylab as plt
import math

EXPERT below

g = nx.read_edgelist("C:\\Users\\laris\\Desktop\\www_twitter7_results\\reply.txt", create_using=nx.DiGraph())
def dict_sort(cent_dict, pos):
    # Create ordered tuple of centrality data
    cent_items=[(b,a) for (a,b) in cent_dict.items()]
    # Sort in descending order
    cent_items.sort()
    cent_items.reverse()
    return tuple(reversed(cent_items[0:pos]))
#nx.draw(g)
#nx.is_connected(g)…