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  3. Cluster Analysis in Python

Exercise

Pokémon sightings: hierarchical clustering

We are going to continue the investigation into the sightings of legendary Pokémon from the previous exercise. Remember that in the scatter plot of the previous exercise, you identified two areas where Pokémon sightings were dense. This means that the points seem to separate into two clusters. In this exercise, you will form two clusters of the sightings using hierarchical clustering.

'x' and 'y' are columns of X and Y coordinates of the locations of sightings, stored in a pandas DataFrame, df. The following are available for use: matplotlib.pyplot as plt, seaborn as sns, and pandas as pd.

Instructions

100 XP
  • Import the linkage and fcluster libraries.
  • Use the linkage() function to compute distances using the ward method.
  • Generate cluster labels for each data point with two clusters using the fcluster() function.
  • Plot the points with seaborn and assign a different color to each cluster.