In This Article, You Will Learn About Numpy Pareto Distribution.
Numpy Pareto Distribution – Before moving ahead, let’s know a bit of Python Exponential Distribution
Pareto Distribution is distributed in the ratio of 80-20 distribution i.e., 20% factors cause 80% outcome.
It includes two parameters –
a – parameter shape. size - Shape of the returned array.
Example – Creating an array of random numbers of size 3×3 for Pareto distribution.
from numpy import random
z = random.pareto(a=2, size=(3, 3))
print(z)
Output - [[0.80365148 0.2705212 0.56911853] [0.00620553 0.98018188 1.63845207] [4.58486444 0.02704608 2.06710027]]
As shown above, it returned an array of shapes 3×3 containing random numbers.
Visualization of Pareto Distribution
Example – Visualizing the Pareto Distribution graph without histogram.
from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl
kl.distplot(random.pareto(a=2, size=5000), kde=False)
plt.show()
Example – Visualizing the Pareto Distribution graph with histogram.
from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl
kl.distplot(random.pareto(a=2, size=5000))
plt.show()
If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.
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