Have you spent hours analyzing data in a Jupyter notebook, only to struggle when it comes to sharing your insights with non-technical stakeholders? Or perhaps you've created an amazing visualization that you want to make interactive for your team?

Converting Jupyter notebooks into interactive dashboards (data apps) used to require extensive web development skills and significant time investment. Not anymore. Today, there are multiple solutions available, each with different approaches and advantages. In this comprehensive guide, we'll explore the various alternatives for transforming your notebooks into interactive dashboards and provide a detailed comparison to help you choose the right tool for your specific needs. We'll cover everything from simple, code-free conversions to advanced enterprise-grade solutions.

Table of Contents

• Why Convert Notebooks to Dashboards?
• Real-World Use Cases
• The Current Landscape of Dashboard Tools
• Solutions
  • Streamlit
  • Plotly Dash
  • Panel
  • Voilà
  • Mercury
  • Gradio
  • Solara
  • Shiny for Python
  • MINEO
• Comprehensive Comparison of Dashboard Tools
• Conclusion

Why Convert Notebooks to Dashboards?

Before we dive into the how, let's quickly address the why:

• Accessibility: Make your data insights available to non-technical users
• Interactivity: Allow users to explore data with filters and dynamic visualizations
• Professionalism: Present your work in a polished, user-friendly interface
• Efficiency: Save time by automating repetitive analyses with interactive parameters
• Collaboration: Enable teams to work with the same data views and insights

The ultimate goal of converting notebooks to dashboards is to bridge the gap between data analysis and business decision-making. While notebooks excel at exploration and analysis, dashboards transform these insights into accessible, interactive tools that empower stakeholders to understand data, test scenarios, and make informed decisions without requiring technical expertise.

Major cloud providers have recognized this need, with AWS introducing Glue Interactive Sessions and Microsoft integrating Azure Notebooks, providing serverless Spark environments that seamlessly work with Jupyter notebooks for scalable data science workflows.

Real-World Use Cases

Typical use cases for interactive dashboards created from Python notebooks include:

• Sales Teams: Dynamic sales performance dashboards with KPIs and territory filters
• Marketing Analytics: Campaign performance trackers with interactive conversion funnels
• Research: Interactive data exploration tools for scientific discoveries
• Finance: Portfolio analysis dashboards with real-time market indicators
• Operations: Supply chain monitoring tools with inventory alerts and forecasting

Using Python notebooks as the foundation for dashboards enables simple and direct integration with many artificial intelligence algorithms. Data scientists can easily incorporate machine learning models, natural language processing, computer vision, and other AI capabilities into their dashboards, allowing business users to leverage these powerful technologies through an intuitive interface without needing to understand the underlying code.

The Current Landscape of Dashboard Tools

Python has become the preferred language for data scientists and analysts, but transforming analytical code into interactive applications has historically been challenging. Currently, multiple solutions exist, each with distinct advantages.

According to recent benchmarks and community metrics, the leading Python dashboard tools show significant differences in adoption: Streamlit leads with 972,000 monthly downloads, followed by Dash with 902,000 and Panel with 468,000. These tools, along with emerging solutions, offer various approaches to converting notebooks into dashboards, from AWS Glue's interactive sessions for Jupyter to Microsoft's Azure Notebooks integration.

In this guide, we'll walk through each of these solutions, showing how to convert a Jupyter notebook into an interactive dashboard with specific examples. We'll start with MINEO as our primary example, then explore each alternative, and finally provide a comprehensive comparison to help you select the right tool for your specific needs.

Solutions

MINEO

MINEO offers a perfect blend of code and no-code functionality with an intuitive drag & drop environment that sets it apart from other solutions. Its powerful no-code widgets system combined with integrated AI assistance powered by OpenAI O3 creates a comprehensive solution that truly bridges the gap between technical and non-technical users.

How to Convert a Notebook with MINEO

MINEO is a no-code solution that transforms your existing Jupyter notebooks into interactive dashboards without writing additional code:

1. Import Your Notebook: Upload your existing Jupyter notebook to MINEO's cloud platform
2. Add Interactive Widgets: Use the drag-and-drop interface to add filters, sliders, and controls
3. Connect Widgets to Data: Visually link widgets to your notebook variables using the no-code interface
4. Customize Layout: Arrange dashboard elements with responsive design tools
5. Publish & Share: Generate secure sharing links or embed in websites

MINEO's unique advantage is its zero-code approach - simply upload your notebook and use visual tools to create professional dashboards. The platform automatically handles the technical complexity of making notebooks interactive, while providing enterprise features like AI assistance, security controls, and data source integrations.

Streamlit

Streamlit has become the most popular tool for turning Python scripts into web applications, with nearly 1 million monthly downloads. According to 2025 framework comparisons, Streamlit leads with 26.9k GitHub stars and excels in rapid prototyping. Its simple API allows data scientists to create interactive web applications without any front-end experience, making it straightforward to convert notebook code despite not being specifically designed for notebooks.

How to Convert a Notebook to Streamlit

# app.py
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

# Use the sample data from above
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 150, 130, 180, 160, 200],
    'region': ['North', 'North', 'South', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

# Dashboard
st.title("Sales Dashboard")

# Sidebar filter
region = st.sidebar.selectbox("Select Region", ["All"] + list(df['region'].unique()))

# Filter data
filtered_df = df if region == "All" else df[df['region'] == region]

# Metrics
col1, col2 = st.columns(2)
col1.metric("Total Sales", f"${filtered_df['sales'].sum()}")
col2.metric("Avg Sales", f"${filtered_df['sales'].mean():.0f}")

# Chart
fig, ax = plt.subplots()
filtered_df.groupby('month')['sales'].sum().plot(kind='bar', ax=ax)
plt.title(f"Sales by Month - {region}")
st.pyplot(fig)

Run with: pip install streamlit && streamlit run app.py

Plotly Dash

Plotly Dash offers the highest level of customization for dashboards through its component-based architecture built on Plotly.js, React, and Flask. With 19.3k GitHub stars and 4,542 Stack Overflow queries, Dash demonstrates strong community support. According to performance benchmarks, Dash excels in scalability by storing all per-user state in the client browser, making it ideal for professional settings where custom functionality and pixel-perfect design are priorities.

How to Convert a Notebook to Dash

# app.py
import dash
from dash import dcc, html, Input, Output
import pandas as pd
import plotly.express as px

# Sample data from above
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 150, 130, 180, 160, 200],
    'region': ['North', 'North', 'South', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

app = dash.Dash(__name__)

app.layout = html.Div([
    html.H1("Sales Dashboard"),
    html.Div([
        html.Label("Select Region:"),
        dcc.Dropdown(
            id='region-dropdown',
            options=[{'label': 'All', 'value': 'All'}] + 
                    [{'label': r, 'value': r} for r in df['region'].unique()],
            value='All'
        )
    ], style={'width': '50%'}),
    html.Div(id='metrics'),
    dcc.Graph(id='sales-graph')
])

@app.callback(
    [Output('metrics', 'children'),
     Output('sales-graph', 'figure')],
    [Input('region-dropdown', 'value')]
)
def update_dashboard(region):
    filtered_df = df if region == 'All' else df[df['region'] == region]

    metrics = html.Div([
        html.H3(f"Total Sales: ${filtered_df['sales'].sum()}"),
        html.H3(f"Avg Sales: ${filtered_df['sales'].mean():.0f}")
    ])

    fig = px.bar(filtered_df.groupby('month')['sales'].sum().reset_index(), 
                 x='month', y='sales', title=f'Sales by Month - {region}')

    return metrics, fig

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

Run with: pip install dash plotly && python app.py

Panel

Panel excels at working with multiple visualization libraries including Matplotlib, Bokeh, Plotly, and hvPlot, all while operating directly within notebooks. As part of the HoloViz ecosystem, Panel is particularly strong for big data applications with native support for Datashader and Dask, allowing for highly scalable dashboards backed by compute clusters and GPUs. It supports the entire lifecycle of data science artifacts rather than just app development.

How to Convert a Notebook with Panel

# Works directly in Jupyter notebooks
import panel as pn
pn.extension()
import pandas as pd
import matplotlib.pyplot as plt

# Sample data from above
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 150, 130, 180, 160, 200],
    'region': ['North', 'North', 'South', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

# Widget
region_select = pn.widgets.Select(name='Region', 
                                 options=['All'] + list(df['region'].unique()), 
                                 value='All')

# Reactive function
@pn.depends(region_select.param.value)
def dashboard(region):
    filtered_df = df if region == 'All' else df[df['region'] == region]

    # Metrics
    metrics = pn.pane.Markdown(f"""
    ## Total Sales: ${filtered_df['sales'].sum()}
    ## Avg Sales: ${filtered_df['sales'].mean():.0f}
    """)

    # Plot
    fig, ax = plt.subplots(figsize=(8, 4))
    filtered_df.groupby('month')['sales'].sum().plot(kind='bar', ax=ax)
    plt.title(f'Sales by Month - {region}')
    plt.tight_layout()

    return pn.Column(metrics, pn.pane.Matplotlib(fig, tight=True))

# Layout
pn.template.FastListTemplate(
    site="Sales Dashboard",
    sidebar=[region_select],
    main=[dashboard]
).servable()

Run with: pip install panel && panel serve notebook.ipynb or display directly in notebook

Voilà

Voilà is the simplest way to convert a notebook, requiring almost no code changes by directly transforming notebooks into web applications. As Jupyter's official dashboard solution, Voilà preserves your notebook's structure while hiding code cells and displaying only markdown, outputs, and interactive widgets (ipywidgets), making it perfect for quickly sharing with non-technical audiences. It transforms notebooks into secure, stand-alone web applications that can be customized and shared.

How to Convert a Notebook with Voilà

# In Jupyter notebook
import ipywidgets as widgets
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import display

# Sample data from above
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 150, 130, 180, 160, 200],
    'region': ['North', 'North', 'South', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

# Widget
region_dropdown = widgets.Dropdown(
    options=['All'] + list(df['region'].unique()),
    value='All',
    description='Region:'
)

output = widgets.Output()

def update_display(change):
    with output:
        output.clear_output()

        filtered_df = df if region_dropdown.value == 'All' else df[df['region'] == region_dropdown.value]

        # Metrics
        print(f"Total Sales: ${filtered_df['sales'].sum()}")
        print(f"Avg Sales: ${filtered_df['sales'].mean():.0f}")

        # Plot
        fig, ax = plt.subplots(figsize=(8, 4))
        filtered_df.groupby('month')['sales'].sum().plot(kind='bar', ax=ax)
        plt.title(f'Sales by Month - {region_dropdown.value}')
        plt.show()

region_dropdown.observe(update_display, 'value')
display(region_dropdown, output)
update_display(None)

Run with: pip install voila ipywidgets && voila notebook.ipynb

Mercury

Mercury is designed to convert notebooks with minimal changes through its unique YAML-based configuration system. This approach allows you to define interactive widgets directly in notebook markdown cells without modifying your code, simply by adding configuration at the top of your file.

How to Convert a Notebook with Mercury

# In first cell (Markdown)
# Sales Dashboard

---
mercury:
  title: Sales Dashboard
  widgets:
    - name: region
      type: select
      label: Select Region
      options: [All, North, South]
      value: All
---

# In code cell
import pandas as pd
import matplotlib.pyplot as plt

# Sample data
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 150, 130, 180, 160, 200],
    'region': ['North', 'North', 'South', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

# Mercury automatically creates 'region' variable from YAML
filtered_df = df if region == 'All' else df[df['region'] == region]

# Display metrics
print(f"Total Sales: ${filtered_df['sales'].sum()}")
print(f"Avg Sales: ${filtered_df['sales'].mean():.0f}")

# Plot
plt.figure(figsize=(8, 4))
filtered_df.groupby('month')['sales'].sum().plot(kind='bar')
plt.title(f'Sales by Month - {region}')
plt.show()

Run with: pip install mercury && mercury run

Gradio

Gradio specializes in creating interfaces for machine learning models but works well for any data analysis dashboard. Its component library excels at handling various inputs and outputs like images, audio, and video, making it particularly suited for multimedia applications with tight integration to Hugging Face's ecosystem.

How to Convert a Notebook to Gradio

# app.py
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt

# Sample data
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 150, 130, 180, 160, 200],
    'region': ['North', 'North', 'South', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

def create_dashboard(region):
    filtered_df = df if region == "All" else df[df['region'] == region]

    # Metrics HTML
    metrics = f"""
    <h3>Total Sales: ${filtered_df['sales'].sum()}</h3>
    <h3>Avg Sales: ${filtered_df['sales'].mean():.0f}</h3>
    """

    # Plot
    fig, ax = plt.subplots(figsize=(8, 4))
    filtered_df.groupby('month')['sales'].sum().plot(kind='bar', ax=ax)
    plt.title(f'Sales by Month - {region}')
    plt.tight_layout()

    return metrics, fig

# Create interface
demo = gr.Interface(
    fn=create_dashboard,
    inputs=gr.Dropdown(choices=["All", "North", "South"], value="All", label="Region"),
    outputs=[gr.HTML(label="Metrics"), gr.Plot(label="Sales Chart")],
    title="Sales Dashboard"
)

if __name__ == "__main__":
    demo.launch()

Run with: pip install gradio && python app.py

Solara

Solara brings React-like architecture to Python dashboards, offering excellent performance through efficient reactivity. It introduces a component-based architecture using hooks and functional components entirely in Python while using Vue.js and Vuetify under the hood to provide rich UI components without requiring JavaScript knowledge.

How to Convert a Notebook to Solara

# app.py
import solara
import pandas as pd
import matplotlib.pyplot as plt

# Sample data
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 150, 130, 180, 160, 200],
    'region': ['North', 'North', 'South', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

@solara.component
def SalesDashboard():
    region, set_region = solara.use_state("All")

    # Filter data
    filtered_df = df if region == "All" else df[df['region'] == region]

    # Layout
    with solara.Column():
        solara.Title("Sales Dashboard")

        # Filter
        solara.Select(
            label="Region",
            value=region,
            values=["All", "North", "South"],
            on_value=set_region
        )

        # Metrics
        with solara.Row():
            solara.Info(f"Total Sales: ${filtered_df['sales'].sum()}")
            solara.Info(f"Avg Sales: ${filtered_df['sales'].mean():.0f}")

        # Chart
        fig, ax = plt.subplots(figsize=(8, 4))
        filtered_df.groupby('month')['sales'].sum().plot(kind='bar', ax=ax)
        plt.title(f'Sales by Month - {region}')
        plt.tight_layout()
        solara.FigureMatplotlib(fig)

# Run the app
SalesDashboard()

Run with: pip install solara && solara run app.py

Shiny for Python

Shiny for Python brings R's popular reactive programming model to Python dashboards as the Python implementation of the widely-used Shiny framework from the R world. Its reactive execution model automatically updates dependent outputs when inputs change without manual callback wiring, creating a clean separation between UI components and the logic that powers them.

How to Convert a Notebook to Shiny

# app.py
from shiny import App, ui, render, reactive
import pandas as pd
import matplotlib.pyplot as plt

# Sample data
data = {
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'sales': [100, 150, 130, 180, 160, 200],
    'region': ['North', 'North', 'South', 'South', 'North', 'South'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B']
}
df = pd.DataFrame(data)

# UI
app_ui = ui.page_fluid(
    ui.h1("Sales Dashboard"),
    ui.input_select("region", "Select Region:", 
                   {"All": "All", "North": "North", "South": "South"}, 
                   selected="All"),
    ui.row(
        ui.column(6, ui.h3(ui.output_text("total_sales"))),
        ui.column(6, ui.h3(ui.output_text("avg_sales")))
    ),
    ui.output_plot("sales_plot")
)

# Server
def server(input, output, session):
    @reactive.Calc
    def filtered_data():
        return df if input.region() == "All" else df[df['region'] == input.region()]

    @output
    @render.text
    def total_sales():
        return f"Total Sales: ${filtered_data()['sales'].sum()}"

    @output
    @render.text
    def avg_sales():
        return f"Avg Sales: ${filtered_data()['sales'].mean():.0f}"

    @output
    @render.plot
    def sales_plot():
        fig, ax = plt.subplots(figsize=(8, 4))
        filtered_data().groupby('month')['sales'].sum().plot(kind='bar', ax=ax)
        plt.title(f'Sales by Month - {input.region()}')
        plt.tight_layout()
        return fig

app = App(app_ui, server)

if __name__ == "__main__":
    app.run()

Run with: pip install shiny && python app.py

📊 Comprehensive Comparison of Dashboard Tools

🔍 Feature Comparison

🎯 Quick Selection Guide

• 🚀 Choose MINEO if: You want a no-code solution with enterprise features and AI assistance
• ⚡ Choose Streamlit if: You prioritize speed and simplicity over customization
• 🔧 Choose Dash if: You need maximum customization and don't mind complexity
• 🎛️ Choose Panel if: You work with multiple visualization libraries and big data
• 📋 Choose Voilà if: You want to share notebooks with minimal changes
• 🔄 Choose Mercury if: You want simple parameterization without code changes
• 🤖 Choose Gradio if: You're building ML model interfaces
• ⚛️ Choose Solara if: You need React-like components and advanced reactivity
• ✨ Choose Shiny if: You're familiar with R Shiny or need reactive programming

Conclusion

Converting Jupyter notebooks to interactive dashboards has become increasingly accessible with the variety of tools now available. This guide has explored multiple options, each with distinct strengths suited to different needs and workflows.

When selecting a dashboard solution, consider your specific requirements:

• Technical expertise level: Solutions like Voilà and Mercury require minimal code changes, while Dash and Solara offer more customization but with steeper learning curves.
• Workflow preferences: Panel and MINEO work directly within notebooks, while Streamlit and Shiny use separate application structures.
• Specific needs: Gradio excels with multimedia and ML models, Dash offers precise control over appearance, and MINEO provides drag-and-drop functionality for non-technical users.
• Deployment context: Consider audience size, interactivity requirements, and enterprise needs like security and authentication.

The Python ecosystem now offers solutions for every scenario—from quick prototypes to enterprise-grade applications, from code-centric to no-code approaches. The Jupyter Enhancement Proposal for Dashboard Extension shows the community's commitment to standardizing dashboard creation from notebooks. Whether you're building internal data tools or production-ready applications, the right choice depends on your specific context, team skills, and deployment requirements.

Whichever tool you choose, creating interactive dashboards from notebooks enables broader access to your data insights, empowering more stakeholders to explore and understand your analyses.

Happy coding!