Iris Kmeans

Download this notebook from GitHub (right-click to download).

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import panel as pn
import hvplot.pandas

from sklearn.cluster import KMeans
from bokeh.sampledata import iris
pn.extension(sizing_mode="stretch_width")

This app provides an example of building a simple dashboard using Panel. It demonstrates how to take the output of k-means clustering on the Iris dataset (performed using scikit-learn), parameterizing the number of clusters and the x and y variables to plot. The entire clustering and plotting pipeline is expressed as a single reactive function that returns a plot that responsively updates when one of the widgets changes.

flowers = iris.flowers.copy()
cols = list(flowers.columns)[:-1]

x = pn.widgets.Select(name='x', options=cols)
y = pn.widgets.Select(name='y', options=cols, value='sepal_width')
n_clusters = pn.widgets.IntSlider(name='n_clusters', start=1, end=5, value=3)

@pn.depends(x.param.value, y.param.value, n_clusters.param.value)
def get_clusters(x, y, n_clusters):
    kmeans = KMeans(n_clusters=n_clusters)
    est = kmeans.fit(iris.flowers.iloc[:, :-1].values)
    flowers['labels'] = est.labels_.astype('str')
    centers = flowers.groupby('labels').mean()
    return (flowers.sort_values('labels').hvplot.scatter(x, y, c='labels', size=100, height=500) *
            centers.hvplot.scatter(x, y, marker='x', color='black', size=400,
                                   padding=0.1, line_width=5))

pn.Column(
    '# Iris K-Means Clustering',
    pn.Row(pn.WidgetBox(x, y, n_clusters), get_clusters)
)

App

Lets wrap it into nice template that can be served via panel serve iris_kmeans.ipynb

pn.template.FastListTemplate(
    site="Panel", title="Iris Kmeans", 
    sidebar=[x, y, n_clusters],
    main=[
        "This app provides an example of **building a simple dashboard using Panel**.\n\nIt demonstrates how to take the output of **k-means clustering on the Iris dataset** using scikit-learn, parameterizing the number of clusters and the variables to plot.\n\nThe entire clustering and plotting pipeline is expressed as a **single reactive function** that responsively returns an updated plot when one of the widgets changes.\n\n The **`x` marks the center** of the cluster.""",
        get_clusters
        ]
).servable();

This web page was generated from a Jupyter notebook and not all interactivity will work on this website. Right click to download and run locally for full Python-backed interactivity.

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Download this notebook from GitHub (right-click to download).