Overview#
The powerful data exploration & web app framework for Python
Panel is an open-source Python library that lets you easily build powerful tools, dashboards and complex applications entirely in Python. It has a batteries-included philosophy, putting the PyData ecosystem, powerful data tables and much more at your fingertips. High-level reactive APIs and lower-level callback based APIs ensure you can quickly build exploratory applications, but you aren’t limited if you build complex, multi-page apps with rich interactivity. Panel is a member of the HoloViz ecosystem, your gateway into a connected ecosystem of data exploration tools.
Show code cell source
import panel as pn
import pandas as pd
import holoviews as hv
from sklearn.cluster import KMeans
pn.extension(design='material')
import hvplot.pandas
Show code cell source
penguins = pd.read_csv('https://datasets.holoviz.org/penguins/v1/penguins.csv').dropna()
cols = list(penguins.columns)[2:6]
x = pn.widgets.Select(name='x', options=cols, sizing_mode="stretch_width", margin=10)
y = pn.widgets.Select(name='y', options=cols, value='bill_depth_mm', sizing_mode="stretch_width")
n_clusters = pn.widgets.IntSlider(name='n_clusters', start=2, end=5, value=3, sizing_mode="stretch_width", margin=10)
def cluster(data, n_clusters):
kmeans = KMeans(n_clusters=n_clusters, n_init='auto')
est = kmeans.fit(data)
return est.labels_.astype('str')
def plot(x, y, n_clusters):
penguins['labels'] = cluster(penguins.iloc[:, 2:6].values, n_clusters)
centers = penguins.groupby('labels').mean(numeric_only=True)
return (penguins.sort_values('labels').hvplot.scatter(
x, y, c='labels', hover_cols=['species'], line_width=1, size=60, frame_width=400, frame_height=400
).opts(marker=hv.dim('species').categorize({'Adelie': 'square', 'Chinstrap': 'circle', 'Gentoo': 'triangle'})) * centers.hvplot.scatter(
x, y, marker='x', color='black', size=400, padding=0.1, line_width=5
))
description = pn.pane.Markdown("""
This app applies *k-means clustering* on the Palmer Penguins dataset using scikit-learn, parameterizing the number of clusters and the variables to plot.
<br><br>
Each cluster is denoted by one color while the penguin species is indicated using markers:
<br><br>
● - Adelie, ■ - Chinstrap, ▲ - Gentoo
<br><br>
By comparing the two we can assess the performance of the clustering algorithm.
<br><br>
Additionally the center of each cluster is marked with an `X`.
<br><br>
""", sizing_mode="stretch_width")
explanation = pn.pane.Markdown("""
**Species**
Adelie: ●\n
Chinstrap: ■\n
Gentoo: ▲
""", margin=(0, 10))
code = pn.pane.Markdown("""
```python
import panel as pn
pn.extension()
x = pn.widgets.Select(name='x', options=cols)
y = pn.widgets.Select(name='y', options=cols, value='bill_depth_mm')
n_clusters = pn.widgets.IntSlider(name='n_clusters', start=2, end=5, value=3)
explanation = pn.pane.Markdown(...)
def plot(x, y, n_clusters):
...
interactive_plot = pn.bind(plot, x, y, n_clusters)
pn.Row(
pn.WidgetBox(x, y, n_clusters, explanation),
interactive_plot
)
```
""", width=800)
app = pn.Tabs(
('APP',
pn.Row(
pn.WidgetBox(x, y, n_clusters, explanation, width=175, margin=10),
pn.bind(plot, x, y, n_clusters),),
),
('CODE', code),
('DESCRIPTION', description),
width=800
)
pn.Row(
pn.layout.HSpacer(),
app,
pn.layout.HSpacer(),
sizing_mode='stretch_width'
).embed(max_opts=4, json=True, json_prefix='json')
Panel makes it simple to:
Develop in your favorite editor or notebook environment
Combine the PyData tools and plotting libraries that you know and love
Iterate quickly to develop data tools, dashboards and complex apps
Collaborate across skill levels and tool preferences
Add advanced bi-directional communication to your data apps
Create interactive big data applications with crossfiltering
Create high performing, streaming data applications
Create data apps that can run entirely in the browser
Create polished, performant, secure and production-ready web applications
Usage#
For usage questions or technical assistance, please head over to Discourse or our Discord server. If you have any issues, feature requests or wish to contribute, you can visit our GitHub site.
Sponsors#
The Panel project is grateful for the sponsorship by the organizations and companies below:
