Overview

The Powerful Data Exploration & Web App Framework for Python

Portfolio Analyzer

https://panel-gallery-dev.holoviz-demo.anaconda.com/portfolio_analyzer

Portfolio Optimizer

https://panel-gallery-dev.holoviz-demo.anaconda.com/portfolio_optimizer

WebLLM Chat App

https://panel-gallery-dev.holoviz-demo.anaconda.com/webllm

Videostream

https://panel-gallery-dev.holoviz-demo.anaconda.com/streaming_videostream

Panel is an open-source Python library designed to streamline the development of robust tools, dashboards, and complex applications entirely within Python. With a comprehensive philosophy, Panel integrates seamlessly with the PyData ecosystem, offering powerful, interactive data tables, visualizations, and much more, to unlock, visualize, share, and collaborate on your data for efficient workflows.

Its feature set includes high-level reactive APIs and lower-level callback-based APIs, enabling rapid development of exploratory applications and facilitating the creation of intricate, multi-page applications with extensive interactivity.

Panel is a proud member of the HoloViz ecosystem, providing a gateway to a cohesive suite of data exploration tools.

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import panel as pn

import pandas as pd
import holoviews as hv

from sklearn.cluster import KMeans

pn.extension()

import hvplot.pandas  # noqa: F401

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penguins = pd.read_csv('https://datasets.holoviz.org/penguins/v1/penguins.csv').dropna()
cols = list(penguins.columns)[2:6]

x = pn.ui.Select(label='x', options=cols, sizing_mode="stretch_width", margin=10)
y = pn.ui.Select(label='y', options=cols, value='bill_depth_mm', sizing_mode="stretch_width")
n_clusters = pn.ui.IntSlider(label='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.ui.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.ui.Markdown("""
**Species**

Adelie: ●\n
Chinstrap: ■\n
Gentoo: ▲
""", margin=(0, 10))

code = pn.ui.Markdown("""
```python
import panel as pn

pn.extension()

x = pn.ui.Select(label='x', options=cols)
y = pn.ui.Select(label='y', options=cols, value='bill_depth_mm')
n_clusters = pn.ui.IntSlider(label='n_clusters', start=2, end=5, value=3)

explanation = pn.ui.Markdown(...)

def plot(x, y, n_clusters):
    ...

interactive_plot = pn.bind(plot, x, y, n_clusters)

pn.ui.Row(
    pn.ui.WidgetBox(x, y, n_clusters, explanation),
    interactive_plot
)
```
""", width=800)

app = pn.ui.Tabs(
    ('APP',
        pn.ui.Row(
            pn.ui.WidgetBox(x, y, n_clusters, explanation, width=175, margin=10),
            pn.bind(plot, x, y, n_clusters),),
    ),
    ('CODE', code),
    ('DESCRIPTION', description),
    width=800
)


pn.ui.Row(
    pn.ui.HSpacer(),
    app,
    pn.ui.HSpacer(),
    sizing_mode='stretch_width'
).embed(max_opts=4, json=True, json_prefix='json')