Upsetplot from Actual Sets
I Want to Use the Upsetplot Given the Actual Sets I Have, but I Cannot Find Any Example to Use It This Way. the Standard Example Is This: from Upsetplot Import...
I want to use the UpSetPlot given the actual sets I have, but I cannot find any example to use it this way. The standard example is this:
from upsetplot import generate_counts, plot
example = generate_counts()
plot(example, orientation='vertical')
where generated example is a Series looking like below.
cat0 cat1 cat2
False False False 56
True 283
True False 1279
True 5882
True False False 24
True 90
True False 429
True 1957
Name: value, dtype: int64
Is there a way to automatically generate this kind of count structure from the actual elements in the categories cat0, cat1, and cat2?
3 Answers
Using the tip by @StupidWolf in another answer, here is an answer to my own question. Given 3 sets
set1 = {0,1,2,3,4,5}
set2 = {3,4,5,6,10}
set3 = {0,5,6,7,8,9}
here is the code to draw an upsetplot for these three sets:
import pandas as pd
from upsetplot import plot
set_names = ['set1', 'set2', 'set3']
all_elems = set1.union(set2).union(set3)
df = pd.DataFrame([[e in set1, e in set2, e in set3] for e in all_elems], columns = set_names)
df_up = df.groupby(set_names).size()
plot(df_up, orientation='horizontal')
And here is the 4th and 5th line changed to generalize above code to a list of sets, say sets = [set1, set2, set3]:
all_elems = list(set().union(*sets))
df = pd.DataFrame([[e in st for st in sets] for e in all_elems], columns = set_names)
It looks like a product from pandas to me:
import numpy as np
import pandas as pd
from upsetplot import generate_counts, plot
example = generate_counts()
type(example)
pandas.core.series.Series
example.index
MultiIndex([(False, False, False),
(False, False, True),
(False, True, False),
(False, True, True),
( True, False, False),
( True, False, True),
( True, True, False),
( True, True, True)],
names=['cat0', 'cat1', 'cat2'])
So if your dataframe is like this:
df = pd.DataFrame(np.random.choice([True,False],(100,3)),
columns=['cat0','cat1','cat2'])
You can do:
example = df.groupby(['cat0','cat1','cat2']).size()
plot(example, orientation='vertical')
I think the limitation is that the elements in cat0, cat1, cat2 have to be boolean.
There are several ways that sets can be used to represent category membership. To help translate sets into the format required by upsetplot, you will find helpers from_memberships, from_contents and from_indicators.
See also the Data Format Guide.
Robert Thorne covers electric vehicle innovations, autonomous driving systems, global mobility trends, and automotive engineering developments.
