Jingwen Zheng
  • Nov 06, 2018

Matplotlib Series 6: Venn diagram

This blog specifies how to create venn diagram with matplotlib in Python, and its use case.

This blog is part of Matplotlib Series:

Venn diagram

A venn diagram (also called primary diagram, set diagram or logic diagram) is a diagram that shows all possible logical relations between a finite collection of different sets.

When to use it ?

  • Show logical relations between different groups (intersection, difference, union)

Example 1

Venn diagram 2 

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib_venn import venn2

# First way
df = pd.DataFrame({'Product': ['Only cheese', 'Only red wine', 'Both'],
                   'NbClient': [900, 1200, 400]},
                  columns = ['Product', 'NbClient'])

v2 = venn2(subsets = {'10': df.loc[0, 'NbClient'],
                      '01': df.loc[1, 'NbClient'],
                      '11': df.loc[2, 'NbClient']},
           set_labels=('', ''))
v2.get_patch_by_id('10').set_color('yellow')
v2.get_patch_by_id('01').set_color('red')
v2.get_patch_by_id('11').set_color('orange')

v2.get_patch_by_id('10').set_edgecolor('none')
v2.get_patch_by_id('01').set_edgecolor('none')
v2.get_patch_by_id('11').set_edgecolor('none')

v2.get_label_by_id('10').set_text('%s\n%d\n(%.0f%%)' % (df.loc[0, 'Product'],
                                                        df.loc[0, 'NbClient'],
                                                        np.divide(df.loc[0, 'NbClient'],
                                                                  df.NbClient.sum())*100))

v2.get_label_by_id('01').set_text('%s\n%d\n(%.0f%%)' % (df.loc[1, 'Product'],
                                                        df.loc[1, 'NbClient'],
                                                        np.divide(df.loc[1, 'NbClient'],
                                                                  df.NbClient.sum())*100))

v2.get_label_by_id('11').set_text('%s\n%d\n(%.0f%%)' % (df.loc[2, 'Product'],
                                                        df.loc[2, 'NbClient'],
                                                        np.divide(df.loc[2, 'NbClient'],
                                                                  df.NbClient.sum())*100))

for text in v2.subset_labels:
    text.set_fontsize(12)

plt.show()
# Second way
grp1 = set(['cheese-a', 'cheese-b', 'cheese-c', 'cheese-d',
            'cheese-e', 'cheese-f', 'cheese-g', 'cheese-h',
            'cheese-i', 'cheese', 'red wine'])
grp2 = set(['red wine-a', 'red wine-b', 'red wine-c', 'red wine-d',
            'red wine-e', 'red wine-f', 'red wine-g', 'red wine-h',
            'red wine-i', 'red wine-j', 'red wine-k', 'red wine-l',
            'red wine', 'cheese'])

v2 = venn2([grp1, grp2], set_labels = ('', ''))

v2.get_patch_by_id('10').set_color('yellow')
v2.get_patch_by_id('01').set_color('red')
v2.get_patch_by_id('11').set_color('orange')

v2.get_patch_by_id('10').set_edgecolor('none')
v2.get_patch_by_id('01').set_edgecolor('none')
v2.get_patch_by_id('11').set_edgecolor('none')

v2.get_label_by_id('10').set_text('Only cheese\n(36%)')
v2.get_label_by_id('01').set_text('Only red wine\n(48%)')
v2.get_label_by_id('11').set_text('Both\n(16%)')

plt.show()

This kind of venn diagram can usually be used in retail trading. Assuming that we need to study the popularity of cheese and red wine, and 2500 clients answered our questionnaire. According to the diagram above, we find that among 2500 clients, 900 clients(36%) prefer cheese, 1200 clients(48%) prefer red wine, and 400 clients(16%) favor both product.

Example 2

Venn diagram 3 

from matplotlib_venn import venn3

v3 = venn3(subsets = {'100':30, '010':30, '110':17,
                      '001':30, '101':17, '011':17, '111':5},
           set_labels = ('', '', ''))

v3.get_patch_by_id('100').set_color('red')
v3.get_patch_by_id('010').set_color('yellow')
v3.get_patch_by_id('001').set_color('blue')
v3.get_patch_by_id('110').set_color('orange')
v3.get_patch_by_id('101').set_color('purple')
v3.get_patch_by_id('011').set_color('green')
v3.get_patch_by_id('111').set_color('grey')

v3.get_label_by_id('100').set_text('Mathematique')
v3.get_label_by_id('010').set_text('Computer science')
v3.get_label_by_id('001').set_text('Domain expertise')
v3.get_label_by_id('110').set_text('Machine learning')
v3.get_label_by_id('101').set_text('Statistical research')
v3.get_label_by_id('011').set_text('Data processing')
v3.get_label_by_id('111').set_text('Data science')

for text in v3.subset_labels:
    text.set_fontsize(13)

plt.show()

I read an article and imitate its venn diagram as above, which presents different essential capacities for data science related workers.

You can click here to check this example in jupyter notebook.

Reference

Search