Bokeh is a powerful Python library for creating interactive visualizations. It provides a wide range of tools and features that enable data clustering and visualization. In this blog post, we will explore how to use Bokeh for clustering and visualizing data.

1. Installing Bokeh

To get started, you need to have Bokeh installed on your system. You can install it using pip:

pip install bokeh

2. Loading and Preparing the Data

Before we can start clustering the data, we need to load and preprocess it. Let’s assume we have a dataset in a CSV file. We can use the Pandas library to load the data:

import pandas as pd

# Load the data from CSV file
data = pd.read_csv('data.csv')

# Preprocess the data
# ...

Once the data is loaded, we may need to preprocess it to prepare it for clustering. This might include scaling the features, handling missing values, or applying dimensionality reduction techniques.

3. Clustering the Data

Now that our data is prepared, we can proceed with clustering. Bokeh itself does not provide built-in clustering algorithms, but it integrates well with popular libraries like Scikit-learn for clustering.

from sklearn.cluster import KMeans

# Perform clustering
kmeans = KMeans(n_clusters=3)
clusters = kmeans.fit_predict(data)

In this example, we are using the K-means algorithm from Scikit-learn to perform clustering. Adjust the number of clusters based on your data and requirements.

4. Visualizing the Clusters with Bokeh

Now that our data is clustered, it’s time to visualize the results using Bokeh. Bokeh provides various plot types and customization options to create visually appealing interactive plots.

from bokeh.plotting import figure, show

# Initialize the figure
p = figure(title='Data Clustering', x_axis_label='X', y_axis_label='Y')

# Assign colors to the clusters
colors = ['red', 'green', 'blue', 'yellow']  # Add more colors based on the number of clusters
cluster_colors = [colors[label] for label in clusters]

# Create scatter plot of the data points
p.scatter(data['x'], data['y'], fill_color=cluster_colors, size=10)

# Show the plot
show(p)

In the above code, we create a scatter plot using Bokeh’s figure function. We assign colors to each data point based on its cluster label. Adjust the colors based on the number of clusters you have. Finally, we call the show function to display the plot.

Conclusion

In this blog post, we explored how to use Bokeh for data clustering and visualization in Python. We covered the basic steps of loading and preprocessing the data, performing clustering using external libraries like Scikit-learn, and creating interactive plots with Bokeh.

Bokeh’s extensive documentation and community support make it a powerful tool for visualizing and exploring complex datasets. Give it a try and see how it can enhance your data analysis and storytelling capabilities.