Pros of Streamlit

• Simpler and more intuitive API, allowing for faster development
• Built-in widgets and components for common data science tasks
• Automatic UI updates based on script changes

Cons of Streamlit

• Less flexibility in layout and design compared to Dash
• Limited support for complex, multi-page applications
• Fewer options for customization and advanced interactivity

Code Comparison

Streamlit example:

import streamlit as st
import pandas as pd

df = pd.read_csv("data.csv")
st.line_chart(df)

Dash example:

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd

app = dash.Dash(__name__)
df = pd.read_csv("data.csv")

app.layout = html.Div([
    dcc.Graph(id='graph'),
])

@app.callback(Output('graph', 'figure'), Input('graph', 'relayoutData'))
def update_graph(relayout_data):
    return {'data': [{'x': df['x'], 'y': df['y'], 'type': 'line'}]}

if __name__ == '__main__':
    app.run_server(debug=True)

Pros of Bokeh

• More flexible and customizable for advanced visualizations
• Better performance for large datasets and real-time updates
• Supports standalone HTML output without a server

Cons of Bokeh

• Steeper learning curve, especially for beginners
• Less extensive documentation and community support
• Fewer built-in components for complex dashboards

Code Comparison

Bokeh:

from bokeh.plotting import figure, show
p = figure(title="Simple Line Plot")
p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5])
show(p)

Dash:

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

app = dash.Dash(__name__)
app.layout = html.Div([
    dcc.Graph(id='example-graph'),
])

if __name__ == '__main__':
    app.run_server(debug=True)

Both Bokeh and Dash are powerful libraries for creating interactive visualizations and dashboards in Python. Bokeh offers more flexibility and better performance for complex visualizations, while Dash provides a more streamlined approach for creating web applications with a focus on ease of use and integration with Plotly charts.

Pros of Panel

• More flexible layout system with reactive components
• Supports a wider range of plotting libraries (Bokeh, Matplotlib, Plotly, etc.)
• Easier integration with existing Python workflows and Jupyter notebooks

Cons of Panel

• Smaller community and fewer resources compared to Dash
• Less extensive documentation and examples
• Steeper learning curve for beginners

Code Comparison

Panel:

import panel as pn
import numpy as np

def plot(n):
    return pn.pane.Matplotlib(plt.plot(np.random.randn(n)))

pn.interact(plot, n=pn.widgets.IntSlider(value=100, start=10, end=1000))

Dash:

import dash
from dash import dcc, html
from dash.dependencies import Input, Output
import plotly.express as px

app = dash.Dash(__name__)
app.layout = html.Div([
    dcc.Slider(id='n-slider', min=10, max=1000, value=100, step=10),
    dcc.Graph(id='plot')
])

@app.callback(Output('plot', 'figure'), Input('n-slider', 'value'))
def update_plot(n):
    return px.scatter(x=range(n), y=np.random.randn(n))

app.run_server(debug=True)

Both Panel and Dash offer powerful tools for creating interactive dashboards, but they cater to different use cases and preferences. Panel excels in flexibility and integration with existing Python workflows, while Dash provides a more structured approach with extensive documentation and a larger community.

Pros of Voila

• Seamless integration with Jupyter notebooks, allowing easy conversion of existing notebooks to web applications
• Supports a wide range of Jupyter widgets and custom JavaScript libraries
• Simpler setup process for users already familiar with Jupyter ecosystem

Cons of Voila

• Less flexibility in layout and design compared to Dash
• Limited interactivity options without custom JavaScript
• Steeper learning curve for users not familiar with Jupyter notebooks

Code Comparison

Voila (in a Jupyter notebook):

import ipywidgets as widgets
from IPython.display import display

slider = widgets.IntSlider(description='Value')
output = widgets.Output()

display(slider, output)

with output:
    print(f"The slider value is: {slider.value}")

Dash:

import dash
from dash import dcc, html
from dash.dependencies import Input, Output

app = dash.Dash(__name__)
app.layout = html.Div([
    dcc.Slider(id='slider', min=0, max=100, step=1, value=50),
    html.Div(id='output')
])

@app.callback(Output('output', 'children'), Input('slider', 'value'))
def update_output(value):
    return f"The slider value is: {value}"

if __name__ == '__main__':
    app.run_server(debug=True)

Pros of SandDance

• Offers unique 3D visualizations and cube-based data representations
• Provides smooth transitions between different chart types
• Integrates well with Power BI and other Microsoft tools

Cons of SandDance

• Less extensive documentation compared to Dash
• Smaller community and fewer third-party extensions
• Limited customization options for complex dashboards

Code Comparison

SandDance (TypeScript):

import * as vega from 'vega';
import { SandDance } from '@msrvida/sanddance-explorer';

const spec = {
  // Vega specification
};

SandDance.renderVegaSpec(spec);

Dash (Python):

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash(__name__)
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

if __name__ == '__main__':
    app.run_server(debug=True)

Summary

While SandDance excels in creating visually striking 3D visualizations and seamless transitions between chart types, Dash offers more extensive documentation, a larger community, and greater flexibility for building complex dashboards. SandDance is particularly useful for Microsoft-centric environments, whereas Dash provides a more versatile solution for web-based data visualization applications across various platforms.