This notebook is dedicated to a work of Biglan. He classified disciplines based on their position in three dimensions: pure-applied, life-non-life and hard-soft. His theory is interesting to us because he provided examples for for all combinations. The development of this theory is interesting on its own, but it is even better when we think of combining it with other theories. maybe, the easiest way to follow would be to work with works that references Biglan or are referenced by him. This might provide better insights into his ideas.
This notebook is structured as:
In [1]:
from disciplines.theory import biglan
import pandas as pd
columns = ['discipline','pure', 'hard', 'life']
the = biglan.the_classification
mylist = []
for line in the:
for discipline in line[0]:
mylist.append([discipline, line[1]['pure'], line[1]['hard'], line[1]['life']])
df = pd.DataFrame(mylist, columns=columns)
So, what we get.
In [3]:
df[(df.pure == False) & (df.hard == True) & (df.life == True)]
Out[3]:
Yes, current implementation is a bit messy. We should be able to retrieve disciplines easily. get_disciplines(['life','hard','pure']) get_disciplines(['applied]) this should be imported together with whole biglans theory.
In [2]:
# getting all disciplines
from disciplines.theory import biglan
the = biglan.the_classification
only = [x[0] for x in the]
combined = [item for sublist in only for item in sublist]
In [12]:
combined
Out[12]: