diversity_spider_plot.py

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


groups = ['citizenship', 'race', 'sex', 'institute', 'religion', 'occupation', 'first names', 'eye color']
# List of grouped people, e.g. [1 - iranian, 1 - italian, 4 - russians]
number_of_people = [[1, 1, 4], [1, 5], [1, 5], [1, 1, 4], [6], [1, 1, 4], [2, 1, 1, 1, 1], [2,2,2]]
# Total number of people
n_people = 6
# You need to normalise to the best case scenario, e.g if there're only 2 possible variants for a group of 5 people
# [2.5, 2.5] will give you the maximum possible diversity index.

best_case = [[1]*n_people,
             [n_people / 5] * n_people,
             [n_people / 2] * n_people,
             [1] * n_people,
             [n_people / 5] * n_people,
             [1] * n_people,
             [1] * n_people,
             [n_people/4] * n_people]

# The metric is based on the Shannon diversity index, normalised by the best case scenario
shannon_index_norm = []
for s, sm in zip(number_of_people, best_case):
    shannon = -np.sum([x / n_people * np.log(x / n_people) for x in s])
    shannon_best = -np.sum([x / n_people * np.log(x / n_people) for x in sm])
    shannon_index_norm.append(shannon / shannon_best)


print(shannon_best)

# Set data
df = pd.DataFrame({k: [v] for k, v in zip(groups, shannon_index_norm)})

# number of variable
categories = list(df)
N = len(categories)

# We are going to plot the first line of the data frame.
# But we need to repeat the first value to close the circular graph:
values = df.loc[0].values.flatten().tolist()
values += values[:1]

# What will be the angle of each axis in the plot? (we divide the plot / number of variable)
angles = [n / float(N) * 2 * np.pi for n in range(N)]
angles += angles[:1]

plt.figure(figsize=(8, 8))
ax = plt.subplot(111, polar=True)

# Draw one axe per variable + add labels
plt.xticks(angles[:-1], categories, color='grey', size=15)

# Draw ylabels
ax.set_rlabel_position(0)
plt.yticks(np.linspace(0, 1, 5), np.linspace(0, 1, 5), color="grey", size=11)
plt.ylim(0, 1.)

# Plot data
ax.plot(angles, values, linewidth=1, linestyle='solid')

# Fill area
ax.fill(angles, values, 'b', alpha=0.1)

# Show the graph
plt.show()