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The panel library

The Panel library is an open-source Python library designed to streamline the development
of robust tools, dashboards, and complex applications entirely within Python. It is part of
the HoloViz ecosystem, which provides a suite of data exploration tools.

pip install panel

show vs servable

The show method

  • Purpose: Immediately displays the Panel object in a web browser.
  • Context: Ideal for local development, quick previews, and Jupyter notebooks.
  • Usage: Simply call show on your Panel object to open it in the default web browser.

The servable method

  • Purpose: Prepares the Panel object to be served by a Panel server.
  • Context: Used for deploying applications in a server environment for production use.
  • Usage: Call servable on your Panel object, then use the panel serve command
    to serve the application.

In essence, use show for instant, local previews and servable for deploying
applications to be accessed over the web.

Use cases:

  • Dashboards - Create interactive and dynamic dashboards to visualize and
    explore data.
  • Web Applications - Develop web applications with user interfaces that interact
    with Python code.
  • Exploratory Data Analysis - Build tools to quickly explore and analyze data
    sets interactively.
  • Interactive Reports - Generate interactive reports that allow users to
    manipulate data and visualize results.
  • Real-Time Monitoring - Set up real-time monitoring dashboards to track live
    data feeds and visualize changes.
  • Custom GUIs - Create custom graphical user interfaces for scientific
    computing, financial analysis, and other applications.

Simple

import panel as pn

pn.extension()

pn.panel("Panel 1").servable()
pn.panel("Panel 2").servable()
pn.panel("Panel 3").servable()

Slider

import panel as pn

# Initialize the Panel extension
pn.extension()

# Create a FloatSlider widget
slider = pn.widgets.FloatSlider(name='Value', start=0, end=10, step=0.1)

# Bind the slider value to a text display
text = pn.bind(lambda x: f"Slider value: {x:.1f}", slider)

# Create a layout with the slider and text display
app = pn.Column(slider, text)

# Display the app
app.show()

Column

import panel as pn

pn.extension(design="material")

component = pn.panel("Hello World")
layout = pn.Column(
    component, pn.widgets.IntSlider(value=2, start=0, end=10, name="Value"),
    pn.pane.HTML("<h1>Title</h1>")
)
print(layout)
layout.servable()

Rows

import panel as pn

#  Apply the custom CSS

custom_css = """
* {
    background-color: #2e2e2e;
    color: white;
}
"""

# Apply the custom CSS
pn.extension(raw_css=[custom_css])

# # Create a Panel object
# text = pn.pane.Markdown("## This is some text on a dark background")

# # Display the Panel
# pn.Row(text).show()


component1 = pn.panel("Panel 1")
component2 = pn.panel("Panel 2")

base = pn.FlexBox(flex_direction='column')

row1 = pn.Row(
    component1, component2,
    pn.pane.HTML("<p>paragraph</p>"),
    pn.pane.Str(
        'This is a raw string that will not be formatted in any way.',
    )
)

row2 = pn.Row(
    pn.pane.Markdown("""\
# Wind Turbine

A wind turbine is a device that converts the kinetic energy of wind into \
[electrical energy](https://en.wikipedia.org/wiki/Electrical_energy).

Read more [here](https://en.wikipedia.org/wiki/Wind_turbine).
""")
)

base.append(row1)
base.append(row2)

print(base)
base.servable()

Dataframe

import pandas as pd
import panel as pn

pn.extension()

data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12]}
df = pd.DataFrame(data)

table = pn.widgets.DataFrame(df, name='DataFrame Viewer')
table.width = 400
table.height = 350

app = pn.Column("# DataFrame Viewer", table)
app.show()

Matplotlib example

import panel as pn
import matplotlib.pyplot as plt
import numpy as np

pn.extension()

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plot = pn.pane.Matplotlib(plt.gcf(), tight=True)

app = pn.Column("# Simple Sine Wave Plot", plot)
app.show()

Matplotlib with slider

import panel as pn
import matplotlib.pyplot as plt
import numpy as np

pn.extension()

# Define the function to create the plot
def create_plot(frequency):
    x = np.linspace(0, 10, 100)
    y = np.sin(frequency * x)
    plt.figure()
    plt.plot(x, y)
    plt.title(f"Sine Wave with Frequency {frequency}")
    return pn.pane.Matplotlib(plt.gcf(), tight=True)

# Create a slider to control the frequency
frequency_slider = pn.widgets.FloatSlider(name='Frequency', start=0.1, end=10, step=0.1, value=1)

# Bind the slider value to the create_plot function
interactive_plot = pn.bind(create_plot, frequency_slider)

# Layout the slider and plot together
app = pn.Column("# Interactive Sine Wave Plot", frequency_slider, interactive_plot)

# Show the app
app.show()

Load data

import panel as pn
import requests
import pandas as pd

# Initialize Panel extension
pn.extension()

# Function to fetch data and update the table
def load_data(event):
    url = text_input.value
    response = requests.get(url)
    data = response.json()
    df = pd.DataFrame(data)
    print(df)  # Debugging: Print the DataFrame to check its content
    table.object = df  # Update the table object

# Create a TextInput widget for URL
text_input = pn.widgets.TextInput(name='JSON URL', value='https://jsonplaceholder.typicode.com/users')

# Create a button
button = pn.widgets.Button(name='Load Data', button_type='primary')
button.on_click(load_data)

# Create an empty DataFrame pane
table = pn.pane.DataFrame(pd.DataFrame(), sizing_mode='stretch_width')

# Create a layout with the TextInput, button, and the table
app = pn.Column(pn.Row(text_input, button), table)

# Serve the application
app.show()