10 Best Tools to Build Your Data Science Web Applications in 2026

Introduction

Building interactive web applications for your data science projects has never been easier. Whether you're showcasing a machine learning model, creating a data dashboard, or deploying a full-stack analytics application, the right tool can make all the difference.

In this guide, I'll cover the 10 best tools for building data science web applications in 2026, complete with installation commands, key features, and use cases to help you choose the perfect solution for your needs.

Why Build Data Apps? Interactive web applications make your data science work accessible to non-technical stakeholders, enable real-time decision-making, and can turn your ML models into production-ready products.

Quick Comparison

1. Streamlit — The King of Rapid Prototyping

Streamlit has become the go-to choice for data scientists who want to quickly turn Python scripts into beautiful web applications. Its simplicity and intuitive API make it perfect for prototyping.

Key Features

• Pure Python: No frontend knowledge required
• Real-time updates: Auto-refresh on code changes
• Rich widgets: Sliders, buttons, file uploaders, and more
• Built-in caching: Optimize performance with @st.cache_data
• Community Cloud: Free hosting for public apps

Installation

pip install streamlit

Quick Start Example

import streamlit as st
import pandas as pd
import plotly.express as px
 
st.title("📊 Sales Dashboard")
 
# File upload
uploaded_file = st.file_uploader("Upload your data", type=["csv"])
 
if uploaded_file:
    df = pd.read_csv(uploaded_file)
 
    # Interactive filters
    category = st.selectbox("Select Category", df['category'].unique())
    filtered_df = df[df['category'] == category]
 
    # Visualization
    fig = px.bar(filtered_df, x='product', y='sales', title=f'Sales by Product')
    st.plotly_chart(fig)

Run Your App

streamlit run app.py

Best For

• Quick prototypes and MVPs
• Data exploration tools
• Internal team dashboards
• ML model demonstrations

Limitation: Streamlit re-runs the entire script on each interaction, which can cause performance issues with larger applications.

2. Dash by Plotly — Production-Grade Analytics

Dash is Plotly's open-source framework for building analytical web applications. It's more powerful than Streamlit but requires understanding callbacks and component-based architecture.

Key Features

• Advanced interactivity: Callbacks for complex interactions
• Enterprise-ready: Authentication, scaling, and deployment options
• Multi-page apps: Built-in support for complex app structures
• Integration: Seamless with Plotly's visualization library
• Design Kit: Professional UI components out of the box

Installation

pip install dash

Quick Start Example

from dash import Dash, html, dcc, callback, Output, Input
import plotly.express as px
import pandas as pd
 
app = Dash(__name__)
 
df = pd.read_csv('data.csv')
 
app.layout = html.Div([
    html.H1('📈 Analytics Dashboard'),
    dcc.Dropdown(
        id='category-dropdown',
        options=[{'label': c, 'value': c} for c in df['category'].unique()],
        value=df['category'].iloc[0]
    ),
    dcc.Graph(id='sales-chart')
])
 
@callback(
    Output('sales-chart', 'figure'),
    Input('category-dropdown', 'value')
)
def update_chart(selected_category):
    filtered_df = df[df['category'] == selected_category]
    return px.bar(filtered_df, x='product', y='sales')
 
if __name__ == '__main__':
    app.run(debug=True)

Run Your App

python app.py

Best For

• Production data applications
• Enterprise dashboards
• Complex multi-page applications
• When you need fine-grained control over interactivity

3. Gradio — ML Model Demos Made Easy

Gradio is specifically designed for creating quick demos of machine learning models. Now part of Hugging Face, it integrates seamlessly with the ML ecosystem.

Key Features

• ML-focused: Built-in components for images, audio, text, and more
• Hugging Face integration: Deploy to Hugging Face Spaces with one click
• Share links: Generate shareable links for remote collaboration
• Playground: Live code editing and preview
• Custom components: Build and use custom UI elements

Installation

pip install gradio

Quick Start Example

import gradio as gr
from transformers import pipeline
 
# Load a pre-trained sentiment analysis model
classifier = pipeline("sentiment-analysis")
 
def analyze_sentiment(text):
    result = classifier(text)[0]
    return f"{result['label']} (Confidence: {result['score']:.2%})"
 
demo = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(label="Enter text to analyze", placeholder="Type your text here..."),
    outputs=gr.Textbox(label="Sentiment Result"),
    title="🎭 Sentiment Analyzer",
    description="Analyze the sentiment of any text using AI",
    examples=[
        ["I absolutely love this product!"],
        ["This is the worst experience ever."],
        ["It's an okay day, nothing special."]
    ]
)
 
demo.launch()

Run Your App

python app.py

Best For

• ML model demonstrations
• AI/ML research presentations
• Quick prototypes for ML applications
• Hugging Face Spaces deployment

4. Panel — Flexible Interactive Dashboards

Panel is part of the HoloViz ecosystem and offers incredible flexibility in creating dashboards. It works with almost any Python plotting library.

Key Features

• Multi-library support: Works with Matplotlib, Bokeh, Altair, Plotly, and more
• Jupyter integration: Build apps directly in notebooks
• Reactive programming: Both high-level and low-level APIs
• Templates: Pre-built layouts for common dashboard patterns
• Multi-page apps: Create complex, navigable applications

Installation

pip install panel

Quick Start Example

import panel as pn
import pandas as pd
import hvplot.pandas
 
pn.extension()
 
# Load data
df = pd.read_csv('data.csv')
 
# Create widgets
category_select = pn.widgets.Select(
    name='Category',
    options=list(df['category'].unique())
)
 
@pn.depends(category_select)
def create_plot(category):
    filtered_df = df[df['category'] == category]
    return filtered_df.hvplot.bar(x='product', y='sales', title=f'Sales in {category}')
 
# Layout
dashboard = pn.Column(
    pn.pane.Markdown("# 📊 Sales Dashboard"),
    category_select,
    create_plot
)
 
dashboard.servable()

Run Your App

panel serve app.py --autoreload

Best For

• Complex, multi-library dashboards
• Scientific visualization
• Data exploration tools
• Jupyter notebook-based workflows

5. Shiny for Python — Reactive Excellence

Shiny for Python brings the power of R Shiny to Python. Known for its reactive programming model, it efficiently updates only the components that need updating.

Key Features

• Reactive execution: Only re-renders affected components
• Template library: Pre-built app templates
• Flexible layouts: Navbars, sidebars, cards, and more
• Input validation: Built-in validation mechanisms
• Efficient updates: Minimal re-rendering for better performance

Installation

pip install shiny

Quick Start Example

from shiny import App, render, ui
import pandas as pd
import matplotlib.pyplot as plt
 
df = pd.read_csv('data.csv')
 
app_ui = ui.page_fluid(
    ui.h2("📈 Interactive Sales Analysis"),
    ui.layout_sidebar(
        ui.sidebar(
            ui.input_select("category", "Select Category", choices=list(df['category'].unique()))
        ),
        ui.output_plot("sales_plot")
    )
)
 
def server(input, output, session):
    @output
    @render.plot
    def sales_plot():
        filtered_df = df[df['category'] == input.category()]
        fig, ax = plt.subplots()
        filtered_df.plot(kind='bar', x='product', y='sales', ax=ax)
        ax.set_title(f'Sales in {input.category()}')
        return fig
 
app = App(app_ui, server)

Run Your App

shiny run app.py --reload

Best For

• R Shiny users transitioning to Python
• Applications requiring efficient updates
• Interactive data exploration
• Reactive data applications

6. Anvil — Full-Stack Python Development

Anvil is a platform for building full-stack web applications entirely in Python. It offers a drag-and-drop interface combined with Python code.

Key Features

• Drag-and-drop builder: Visual UI design
• Built-in database: No SQL required
• Authentication: Built-in user management
• One-click deployment: Easy publishing
• Self-hosted option: Run on your own servers

Installation

For local development with Anvil's open-source runtime:

pip install anvil-app-server

Key Advantages

# Anvil uses a web-based IDE, but you can also work locally
# Example server function (runs on server)
 
@anvil.server.callable
def get_sales_data(category):
    # Query built-in database
    return list(app_tables.sales.search(category=category))
 
# Client-side code calls server functions directly
# Example client code
results = anvil.server.call('get_sales_data', 'Electronics')

Best For

• Full-stack Python applications
• Applications requiring user authentication
• Database-driven applications
• Teams wanting rapid development without frontend complexity

7. Taipy — Production-Ready Data Apps

Taipy is designed for building production-ready data science applications with both frontend and backend capabilities.

Key Features

• Scenario management: Built-in data pipeline management
• Visual editor: Taipy Studio for graphical development
• Background tasks: Run heavy computations without blocking
• Theming: Customizable themes and styles
• Scalability: Built for production workloads

Installation

pip install taipy

Quick Start Example

from taipy.gui import Gui
import pandas as pd
 
# Load data
df = pd.read_csv('data.csv')
selected_category = df['category'].iloc[0]
 
def filter_data(state):
    return df[df['category'] == state.selected_category]
 
page = """
# 📊 Sales Dashboard
 
<|{selected_category}|selector|lov={list(df['category'].unique())}|>
 
<|{filter_data(state)}|chart|type=bar|x=product|y=sales|>
"""
 
gui = Gui(page)
gui.run()

Run Your App

python app.py

Best For

• Production data applications
• Complex data pipelines
• Applications requiring scenario management
• Enterprise deployments

8. Mercury — Jupyter Notebooks as Apps

Mercury transforms Jupyter notebooks into interactive web applications without rewriting your code.

Key Features

• Notebook-first: Keep your existing notebook workflow
• Widget-based: Add interactive controls with simple YAML
• PDF/HTML export: Generate reports from notebooks
• Authentication: Secure your applications
• Cell-level control: Optimize which cells re-execute

Installation

pip install mercury

Quick Start Example

In your Jupyter notebook:

# Cell 1: Mercury configuration
---
title: Sales Analysis
description: Interactive sales dashboard
params:
    category:
        input: select
        label: Select Category
        choices: [Electronics, Clothing, Food]
        value: Electronics
---
 
# Cell 2: Analysis
import pandas as pd
import matplotlib.pyplot as plt
 
df = pd.read_csv('data.csv')
filtered_df = df[df['category'] == category]
 
plt.figure(figsize=(10, 6))
plt.bar(filtered_df['product'], filtered_df['sales'])
plt.title(f'Sales in {category}')
plt.xticks(rotation=45)
plt.show()

Run Your App

mercury run notebook.ipynb

Best For

• Existing Jupyter notebook workflows
• Data analysts who prefer notebooks
• Report generation
• Quick notebook-to-app conversion

9. Deepnote — Collaborative Data Science

Deepnote is a collaborative data science platform that allows teams to work together on notebooks and deploy interactive applications.

Key Features

• Real-time collaboration: Google Docs-style editing
• Built-in version control: Track changes automatically
• Environment management: Custom dependencies and libraries
• App deployment: Deploy notebooks as interactive apps
• AI assistance: Smart code completion

Getting Started

Deepnote is a cloud-based platform, so there's no local installation:

# Visit https://deepnote.com to sign up
# Or integrate with your existing tools
pip install deepnote  # For local integration

Key Advantages

# In Deepnote, you can create interactive apps directly from notebooks
# Add interactive widgets with simple code
 
import deepnote
 
@deepnote.as_app
def my_dashboard():
    category = deepnote.select("Category", options=["A", "B", "C"])
 
    # Your analysis code here
    display_chart(category)

Best For

• Team collaboration
• Educational environments
• Sharing insights with non-technical stakeholders
• Organizations needing real-time collaborative data science

10. Datalore — AI-Powered Development

Datalore by JetBrains combines Jupyter notebooks with AI code assistance and interactive dashboard creation.

Key Features

• Smart code completion: AI-powered suggestions
• Multi-language support: Python, Scala, Kotlin, and R
• Interactive reports: Convert notebooks to shareable reports
• Real-time collaboration: Work with team members simultaneously
• Cloud and on-premises: Flexible deployment options

Getting Started

Datalore is primarily cloud-based:

# Visit https://datalore.jetbrains.com to sign up
# For enterprise on-premises installation, contact JetBrains

Key Advantages

# Datalore provides smart coding assistance
# Example: Creating an interactive report
 
import datalore.report as dr
 
dr.title("Sales Analysis Report")
dr.markdown("## Key Insights")
dr.chart(df, x='product', y='sales', type='bar')
dr.table(summary_stats)

Best For

• Teams already using JetBrains products
• AI-assisted development workflows
• Enterprise environments
• Converting notebooks to professional reports

Summary: Choosing the Right Tool

Quick Installation Reference

Here's a handy reference for installing all the tools locally:

# Core frameworks
pip install streamlit          # Streamlit
pip install dash               # Dash by Plotly
pip install gradio             # Gradio
pip install panel              # Panel
pip install shiny              # Shiny for Python
pip install taipy              # Taipy
pip install mercury            # Mercury
 
# Full installation with common dependencies
pip install streamlit dash gradio panel shiny taipy mercury
 
# For Anvil (open-source runtime)
pip install anvil-app-server
 
# Optional: JupyterLab for notebook-based workflows
pip install jupyterlab

My Recommendations

Based on my experience building data science applications:

1. For beginners: Start with Streamlit — it has the gentlest learning curve
2. For ML practitioners: Use Gradio — perfect for showcasing models
3. For production: Choose Dash — enterprise-ready with full customization
4. For notebooks fans: Try Mercury — keep your existing workflow

Useful Resources

Documentation

• Streamlit Documentation
• Dash User Guide
• Gradio Guides
• Panel Documentation
• Shiny for Python

My Related Blog Posts

• Top 15 Data Scientist Skills You Need in 2026
• Python Data Analysis Essentials
• Machine Learning Workflow: From Data to Deployment

Conclusion

The data science web application landscape in 2026 offers something for everyone. Whether you need rapid prototyping with Streamlit, production-grade dashboards with Dash, ML demos with Gradio, or collaborative notebooks with Deepnote, there's a tool that fits your workflow.

The key is to choose based on your specific needs:

• Speed of development → Streamlit, Gradio
• Production readiness → Dash, Taipy
• Flexibility → Panel, Shiny
• Collaboration → Deepnote, Datalore

What's your favorite tool for building data apps? Let me know! 🚀

This article was inspired by insights from ClickUp, Plotly, BuddyX Theme, and Anvil.