Let's learn how we can code a hierarchical algorithm in Python:

1.  We will first import AgglomerativeClustering from the sklearn.cluster library, along with the pandas and numpy packages:

from sklearn.cluster import AgglomerativeClustering
import pandas as pd
import numpy as np

2. Then we will create 20 data points in a two-dimensional problem space:

dataset = pd.DataFrame({
    'x': [11, 21, 28, 17, 29, 33, 24, 45, 45, 52, 51, 52, 55, 53, 55, 61, 62, 70, 72, 10],
    'y': [39, 36, 30, 52, 53, 46, 55, 59, 63, 70, 66, 63, 58, 23, 14, 8, 18, 7, 24, 10]
})

3. Then we create the hierarchical cluster by specifying the hyperparameters. We use the fit_predict function to actually process the algorithm:

cluster = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='ward') 
cluster.fit_predict(dataset) 

4. Now let's look at the association of each data point to the two clusters that were created:

You can see that the cluster assignment for both hierarchical and k-means algorithms are very similar.