- altair
- numpy
- pandas
- scikit-learn
- panel==0.13.1a2
import asyncio
import altair as alt
import panel as pn
import pandas as pd
from panel.io.pyodide import show
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')
y = pn.widgets.Select(name='y', options=cols, value='bill_length_mm')
n_clusters = pn.widgets.IntSlider(name='n_clusters', start=1, end=5, value=3)
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')
)
chart = pn.pane.Vega()
table = pn.widgets.Tabulator(pagination='remote', page_size=10)
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()
intro = """
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.
"""
await show(x, 'x-widget')
await show(y, 'y-widget')
await show(n_clusters, 'n-widget')
await show(intro, 'intro')
await show(chart, 'cluster-plot')
await show(table, 'table')