packages = [ "altair", "numpy", "pandas", "scikit-learn", "panel==0.13.1" ]
import altair as alt import panel as pn import pandas as pd from sklearn.cluster import KMeans from pyodide.http import open_url pn.config.sizing_mode = 'stretch_width' url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins.csv' penguins = pd.read_csv(open_url(url)).dropna() cols = list(penguins.columns)[2:6] x = pn.widgets.Select(name='x', options=cols, value='bill_depth_mm').servable(target='x-widget') y = pn.widgets.Select(name='y', options=cols, value='bill_length_mm').servable(target='y-widget') n_clusters = pn.widgets.IntSlider(name='n_clusters', start=1, end=5, value=3).servable(target='n-widget') brush = alt.selection_interval(name='brush') # selection of type "interval" def get_clusters(n_clusters): kmeans = KMeans(n_clusters=n_clusters) est = kmeans.fit(penguins[cols].values) df = penguins.copy() df['labels'] = est.labels_.astype('str') return df def get_chart(x, y, df): centers = df.groupby('labels').mean() return ( alt.Chart(df) .mark_point(size=100) .encode( x=alt.X(x, scale=alt.Scale(zero=False)), y=alt.Y(y, scale=alt.Scale(zero=False)), shape='labels', color='species' ).add_selection(brush).properties(width=800) + alt.Chart(centers) .mark_point(size=250, shape='cross', color='black') .encode(x=x+':Q', y=y+':Q') ) intro = pn.pane.Markdown(""" 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 Penguins dataset** using scikit-learn, parameterizing the number of clusters and the variables to plot.\n\nThe plot and the table are linked, i.e. selecting on the plot will filter the data in the table.\n\n The **`x` marks the center** of the cluster. """).servable(target='intro') chart = pn.pane.Vega().servable(target='cluster-plot') table = pn.widgets.Tabulator(pagination='remote', page_size=10).servable(target='table') def update_table(event=None): table.value = get_clusters(n_clusters.value) n_clusters.param.watch(update_table, 'value') @pn.depends(x, y, n_clusters, watch=True) def update_chart(*events): chart.object = get_chart(x.value, y.value, table.value) chart.selection.param.watch(update_filters, 'brush') def update_filters(event=None): filters = [] for k, v in (getattr(event, 'new') or {}).items(): filters.append(dict(field=k, type='>=', value=v[0])) filters.append(dict(field=k, type='<=', value=v[1])) table.filters = filters update_table() update_chart()