Zipf distributions are used to sample data based on zipf's law.
Note: Zipf's Law: In a collection, the nth common term is 1/n times of the most common term. E.g. the 5th most common word in English occurs nearly 1/5 times as often as the most common word.
It has two parameters:
a - distribution parameter.
size - The shape of the returned array.
Example
from numpy import random
x = random.zipf(a=2, size=(2, 3))
print(x)
Visualization of Zipf Distribution
Sample 1000 points but plotting only ones with value < 10 for more meaningful chart.
Example
from numpy import random
import matplotlib.pyplot as plt
import seaborn as sns
x = random.zipf(a=2, size=1000)
sns.displot(x[x<10])
plt.show()
