diff --git a/blog/build-dashboard-python-without-javascript.md b/blog/build-dashboard-python-without-javascript.md new file mode 100644 index 000000000..3ef37b0c2 --- /dev/null +++ b/blog/build-dashboard-python-without-javascript.md @@ -0,0 +1,235 @@ +--- +author: Tom Gotsman +date: 2026-04-02 +title: "How to Build a Dashboard in Python Without Writing Any JavaScript (April 2026)" +title_tag: "Build Python Dashboards Without JavaScript (2026)" +description: "Learn how to build production dashboards in pure Python without JavaScript using Reflex. Real-time updates, 60+ components, one-command deploy. April 2026." +image: /blog/build-dashboard-python_thumbnail.webp +tag: Builder +meta: [ + {"name": "keywords", "content": "make a dashboard in python, how to make a dashboard in python"} +] +faq: [ + {"question": "How long does it take to build a dashboard in Python with Reflex?", "answer": "You can have a basic working dashboard running in minutes—install with `pip install reflex`, initialize with `reflex init`, and run `reflex run`. Most developers build production-ready dashboards with data connections and real-time updates within a few hours, depending on complexity."}, + {"question": "Can I use my existing Python data libraries with Reflex dashboards?", "answer": "Yes, your entire PyPI ecosystem works inside Reflex dashboards. Import pandas for data manipulation, SQLAlchemy for databases, requests for APIs, or any other Python library you already use for backend development and data analysis."}, + {"question": "What's the main difference between Reflex and Streamlit or Plotly Dash?", "answer": "Reflex gives you full control over UI customization, state management, and layout using pure Python throughout, while Streamlit and Plotly Dash require JavaScript when you need custom interactions, advanced styling, or complex state management beyond their built-in capabilities."}, + {"question": "Do I need to learn React or JavaScript to build production dashboards with Reflex?", "answer": "No JavaScript knowledge required. You write everything in Python—state management, UI components, event handlers, and styling all happen through Python classes, functions, and keyword arguments that update the interface automatically."}, + {"question": "How do I deploy a Reflex dashboard to production?", "answer": "Run `reflex deploy` from your project directory. This single command packages your Python application and provisions infrastructure on Reflex Cloud, or you can deploy to your own environment using the same Python codebase."} +] +--- + +```python exec +import reflex as rx +from pcweb.components.image_zoom import image_zoom +``` + +You've built the data pipeline in Python, connected to your database with SQLAlchemy, and processed everything with pandas. Now you need an interactive dashboard and suddenly you're debugging JavaScript event listeners and React components. Here's what changed: you can [create dashboards in Python](https://reflex.dev/) using Reflex where your entire stack stays in Python files you can read and debug normally. We're going to walk through building these dashboards with the same Python patterns you use for backend development. + +**TLDR:** + +- Build production dashboards in pure Python without learning JavaScript or frontend frameworks. +- Reflex provides 60+ built-in components and real-time updates using Python's yield statement. +- Deploy with one command (`reflex deploy`) and maintain readable Python code instead of compiled JavaScript. +- Connect to any database or API using existing Python libraries like pandas, SQLAlchemy, and requests. +- Reflex is an open-source framework that lets you build full-stack web apps entirely in Python. + +## What Python Developers Need to Build Dashboards + +You need Python 3.10 or higher and a virtual environment. If you're writing Python applications, you already have what's required to make a dashboard in Python without touching JavaScript. The workflow mirrors what you already know. Define state using Python classes, create UI components with Python functions, and handle user interactions through event handlers. Your dashboard lives in `.py` files that you can read, debug, and modify using the same tools you use for data analysis or backend development. + +Installation takes one command: `pip install reflex`. Initialize with `reflex init` to get a basic application structure. Run `reflex run` to start the development server with fast refresh. Change your code, save the file, and watch your dashboard update instantly in the browser. + +State management works like Python classes you write every day. Define variables as class attributes, create methods that modify those variables, and the UI updates automatically when state changes. No Redux, no hooks, no frontend state libraries. + +You compose dashboards from 60+ built-in components: charts, tables, forms, buttons, layouts. Each component accepts arguments as Python keyword parameters. Want a styled button? Pass `color="blue"` and `size="lg"`. Need a data table? Pass your pandas DataFrame directly to the component. + +## The JavaScript Problem With Traditional Dashboard Development + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/build-dashboard-python_python-vs-javascript.webp", border_radius="10px", alt="Python vs JavaScript developer workspace comparison", width="100%")), class_name="mb-4") +``` + +Dashboard frameworks like [Plotly Dash](https://reflex.dev/blog/reflex-dash/), [Streamlit](https://reflex.dev/blog/reflex-streamlit/), and Flask require JavaScript the moment you need custom interactions. A dropdown filter that updates multiple charts? You're writing JavaScript callbacks. Custom styling beyond basic themes? CSS and potentially React components. Real-time data updates with WebSockets? Back to JavaScript event listeners. + +This creates a difficult context switch. Python developers spend their time writing data pipelines, training models, and analyzing datasets. They think in pandas DataFrames, numpy arrays, and Python classes. Then dashboard requirements arrive and suddenly they're debugging React component lifecycles, managing npm dependencies, and tracing event propagation through the DOM. [46% of Python developers build web apps](https://blog.jetbrains.com/pycharm/2025/08/the-state-of-python-2025/), yet most web dashboard frameworks still force them to learn JavaScript for anything beyond basic functionality. + +[Python adoption surged 7 percent](https://keyholesoftware.com/software-development-statistics-2026-market-size-developer-trends-technology-adoption/) from 2024 to 2025, the largest single-year jump for any major language. More developers choose Python as their primary language, yet web dashboards still demand JavaScript fluency. The table below provides a quick overview of dashboard frameworks, and how they work across five key criteria: custom interactions, advanced styling, real-time updates, state management, and deployment. + +| Framework | Custom Interactions | Advanced Styling | Real-Time Updates | State Management | Deployment | +|---|---|---|---|---|---| +| Reflex | Pure Python event handlers with automatic UI updates through yield statements | Python keyword arguments for all styling, inline or component-level customization | Built-in WebSocket support through Python async/yield patterns | Python classes with automatic reactivity, computed properties with decorators | Single command deployment with reflex deploy, no build pipeline required | +| Plotly Dash | Callback decorators in Python, but complex interactions require JavaScript for custom components | Limited to CSS stylesheets and Dash-specific props, custom styling needs JavaScript | Interval components and callbacks, but custom WebSocket logic requires JavaScript | Callback-based with Input/Output decorators, state passed between callbacks | WSGI deployment to standard Python hosting, requires separate frontend build for custom components | +| Streamlit | Top-to-bottom script execution model, custom interactions require JavaScript through components | Limited theming and markdown styling, advanced customization needs custom components in JavaScript | Automatic reruns on input change, but true real-time streaming requires JavaScript workarounds | Session state dictionary, but reactive updates trigger full script reruns | Streamlit Cloud or standard Python deployment, limited control over frontend architecture | +| Flask | Requires full JavaScript frontend with AJAX calls to Flask API endpoints | Complete control but requires writing CSS and JavaScript for all interactive styling | Manual WebSocket implementation with Flask-SocketIO and JavaScript client code | Backend state only, frontend state requires JavaScript framework like React or Vue | Standard WSGI deployment for backend, separate build and deployment pipeline for frontend assets | + +## Building Your First Dashboard With Pure Python + +Build a dashboard with Python couldn't be easier with Reflex. + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/build-dashboard-python_dashboard-architecture.webp", border_radius="10px", alt="Python dashboard architecture diagram", width="100%")), class_name="mb-4") +``` + +Create a new file called `dashboard.py` and start with state. Your dashboard needs data and variables that track user interactions, so define them as a Python class. + +```python +import reflex as rx + +class DashboardState(rx.State): + revenue_data: list[dict] = [ + {"month": "Jan", "amount": 12500}, + {"month": "Feb", "amount": 15200}, + ] + selected_metric: str = "revenue" +``` + +State variables become the source of truth. When `selected_metric` changes, every component using that value updates automatically without manual DOM manipulation. + +Build the UI by composing components in a Python function. Each component accepts keyword arguments for styling and behavior. + +```python +def index(): + return rx.vstack( + rx.heading("Revenue Dashboard"), + rx.select( + ["revenue", "profit", "costs"], + value=DashboardState.selected_metric, + ), + rx.recharts.bar_chart( + rx.recharts.bar(data_key="amount"), + data=DashboardState.revenue_data, + ), + ) +``` + +Run `reflex run` and your dashboard appears at `localhost:3000`. Modify the data in your state class, save the file, and watch the charts update instantly. + +## Adding Real-Time Updates Without addEventListener + +Real-time dashboards update when data changes. In JavaScript frameworks, you'd attach event listeners, manage WebSocket connections, and manually update DOM elements. Reflex handles this through background tasks and Python's async patterns. + +Background tasks run independently without blocking the UI. Decorate an async method with `@rx.event(background=True)` and wrap state mutations in `async with self:` blocks. Each mutation triggers a UI update with the current state, so your dashboard refreshes in real time while the task continues running. + +```python +@rx.event(background=True) +async def poll_api_continuously(self): + async with self: + self.monitoring_active = True + for i in range(10): + await asyncio.sleep(1) + async with self: + self.current_value = fetch_latest_data() + yield +``` + +The dashboard shows each update as the loop executes. Users see values change every second without you having to write WebSocket handlers or manage connection state. + +Start a background task from any regular event handler by returning it as an event reference: + +```python +def start_monitoring(self): + return DashboardState.poll_api_continuously +``` + +Multiple users viewing the same dashboard see synchronized updates when shared state changes. + +## Styling and Layout in Python + +Every component accepts styling through keyword arguments. Pass `color`, `padding`, `margin`, `font_size`, or any CSS property as a Python parameter. Want a card with rounded corners and shadow? Write it as function arguments. + +```python +rx.box( + rx.text("Revenue: $45,200", font_size="24px", font_weight="bold"), + padding="20px", + border_radius="8px", + box_shadow="lg", + background="white", +) +``` + +Layout comes from component nesting. Stack components vertically with `rx.vstack`, arrange horizontally with `rx.hstack`, or create grid layouts with `rx.grid`. Reflex includes a theming system for consistent styling across your dashboard. Responsive design works through the same keyword arguments. Pass a list of values for different screen sizes: `width=["100%", "50%", "33%"]` displays full-width on mobile, half-width on tablet, and third-width on desktop. + +## Connecting to Data Sources and APIs + +Dashboards are only as good as the data they visualize. Your dashboard can pull data from databases, APIs, and external services. Import the Python libraries you already use: `requests` for REST APIs, `pandas` for data manipulation, `SQLAlchemy` for database connections, `psycopg2` for PostgreSQL. Then, connect to a database directly in your state class. Finally, query data in event handlers using the same SQL patterns you write for data analysis. + +```python +import sqlalchemy +from sqlalchemy import create_engine + +class DashboardState(rx.State): + metrics: list[dict] = [] + + def load_data(self): + engine = create_engine("postgresql://localhost/analytics") + query = "SELECT date, revenue, users FROM metrics" + self.metrics = pd.read_sql(query, engine).to_dict('records') +``` + +API calls work the same way. Use `requests.get()` or any HTTP library, parse the response, and assign results to state variables. + +```python +def fetch_api_data(self): + response = requests.get("https://api.example.com/metrics") + self.api_metrics = response.json() +``` + +Your entire PyPI ecosystem works inside dashboards. Need authentication? Use the same auth library you use in backend services. Parse CSV files? Import pandas. Connect to Snowflake or MongoDB using their Python SDKs. + +## Deployment Without Frontend Build Pipelines + +Reflex deploys with `reflex deploy`. One command packages your Python application and provisions infrastructure. No webpack configuration, no build scripts, no compiled frontend assets to manage. The deployment process mirrors deploying Flask or Django applications: your Python code goes directly to production without compilation steps. CI/CD pipelines run the same command in GitHub Actions, GitLab CI, or custom automation. + +```yaml +- name: Deploy to Reflex Cloud + run: | + pip install reflex + reflex deploy +``` + +[Reflex Cloud handles scaling and infrastructure](https://reflex.dev/hosting/) automatically. Organizations requiring on-premises deployment can run Reflex in their own environments using the same Python codebase. + +## When Pure Python Dashboards Make the Most Sense + +Pure Python dashboards work best when domain expertise matters more than frontend polish. Here are a few real-world use cases where pure Python dashboards make the most sense: + +- Data scientists building visualization tools for ML models don't need JavaScript expertise to show prediction outputs. Quantitative analysts creating strategy dashboards can iterate on business logic without coordinating with frontend developers. +- Finance teams building portfolio trackers, operations teams creating monitoring dashboards, and healthcare administrators managing patient data work faster when they write Python exclusively. The people who understand the business logic can build and modify the interface directly. +- Teams without dedicated frontend engineers gain immediate velocity. A three-person data team can ship production dashboards without hiring React developers or learning JavaScript tooling. Python developers already on staff handle everything from database queries to UI updates. +- [Rapid prototyping scenarios favor pure Python](https://reflex.dev/blog/reflex-jupyter/). When stakeholders want to test ideas quickly, building in one language removes coordination overhead. Prototype in Python, gather feedback, iterate in Python, and deploy the same code to production. + +## Building Production-Grade Dashboards in Pure Python With Reflex + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/build-dashboard-python_reflex.webp", border_radius="10px", alt="reflex.png", width="100%")), class_name="mb-4") +``` + +Reflex turns Python dashboards into production applications without requiring JavaScript expertise. The framework provides 60+ built-in components for charts, tables, forms, and layouts that you compose using Python functions. Authentication integrates with providers like Okta, Google, and Clerk through simple configuration. Database connections use the Python libraries you already know: SQLAlchemy, psycopg2, or MongoDB drivers. Deploy with `reflex deploy` to get multi-region infrastructure, monitoring, and team collaboration features. Organizations requiring on-premises deployment run the same Python codebase in their own environments. + +The entire application remains readable Python code. When dashboards behave unexpectedly, engineers inspect the source code directly instead of debugging compiled JavaScript bundles. State management, business logic, and UI components live in files that Python developers can understand using the same debugging skills they apply to data analysis and backend services. + +## Final Thoughts on Building Dashboards With Python + +You can [make a dashboard in Python](https://reflex.dev/) without touching JavaScript, and that changes who can build web interfaces. Data teams ship visualization tools directly, operations engineers create monitoring dashboards, and analysts iterate on business logic without waiting for frontend developers. See [pricing details](https://reflex.dev/pricing) if you want to deploy to production. Your Python code goes straight from development to users. + +## FAQ + +### How long does it take to build a dashboard in Python with Reflex? + +You can have a basic working dashboard running in minutes. Install with `pip install reflex`, initialize with `reflex init`, and run `reflex run`. Most developers build production-ready dashboards with data connections and real-time updates within a few hours, depending on complexity. + +### Can I use my existing Python data libraries with Reflex dashboards? + +Yes, your entire PyPI ecosystem works inside Reflex dashboards. Import pandas for data manipulation, SQLAlchemy for databases, requests for APIs, or any other Python library you already use for backend development and data analysis. + +### What's the main difference between Reflex and Streamlit or Plotly Dash? + +Reflex gives you full control over UI customization, state management, and layout using pure Python throughout, while Streamlit and Plotly Dash require JavaScript when you need custom interactions, advanced styling, or complex state management beyond their built-in capabilities. + +### Do I need to learn React or JavaScript to build production dashboards with Reflex? + +No JavaScript knowledge required. You write everything in Python: state management, UI components, event handlers, and styling all happen through Python classes, functions, and keyword arguments that update the interface automatically. + +### How do I deploy a Reflex dashboard to production? + +Run `reflex deploy` from your project directory. This single command packages your Python application and provisions infrastructure on Reflex Cloud, or you can deploy to your own environment using the same Python codebase. diff --git a/blog/django-vs-flask-vs-reflex-comparison.md b/blog/django-vs-flask-vs-reflex-comparison.md new file mode 100644 index 000000000..34c9f3781 --- /dev/null +++ b/blog/django-vs-flask-vs-reflex-comparison.md @@ -0,0 +1,174 @@ +--- +author: Tom Gotsman +date: 2026-04-02 +title: "Django vs. Flask vs. Reflex: Full-Stack Python Framework Comparison (April 2026)" +title_tag: "Django vs Flask vs Reflex (April 2026)" +description: "Compare Django, Flask, and Reflex for full-stack Python development. See performance, features, and use cases for each framework in April 2026." +image: /blog/django-vs-flask-vs-reflex_thumbnail.webp +tag: Builder +meta: [ + {"name": "keywords", "content": "top full stack python frameworks comparison"} +] +faq: [ + {"question": "How does Reflex differ from Django and Flask for full stack development?", "answer": "Django and Flask both require JavaScript frameworks like React or Vue for the frontend, splitting your team between Python backend developers and JavaScript frontend specialists. Reflex lets you write both frontend and backend in pure Python, compiling Python code into a React application automatically without touching JavaScript."}, + {"question": "Can I deploy Reflex applications in my own infrastructure?", "answer": "Yes, Reflex supports on-premises deployment and VPC options for enterprise security requirements. You can run Reflex's AI App Builder in your own environment while maintaining full control over data and code, meeting compliance requirements that cloud-only tools cannot satisfy."}, + {"question": "What's the learning curve like for Python developers new to web development?", "answer": "Python developers can start building full stack applications immediately without learning React, state management libraries, or frontend build tools. You write Python classes for state and Python functions for UI, skipping the JavaScript barrier entirely while Django requires learning ORM, middleware, and template systems first."}, + {"question": "How does Reflex handle production performance compared to Django or Flask?", "answer": "Reflex compiles Python into standard React for the frontend, delivering production React performance with Python backend response times comparable to Flask APIs. The compilation happens during development, so production applications run as optimized React frontends with Python backends."}, + {"question": "When should I choose Reflex over Django for my project?", "answer": "Choose Reflex when you need interactive dashboards, real-time data visualization, or dynamic user interfaces without hiring separate frontend specialists. If your team already uses Python for data science, machine learning, or backend services and wants to maintain a single language across your entire application stack, Reflex removes the coordination overhead of splitting your codebase across languages."} +] +--- + +```python exec +import reflex as rx +from pcweb.components.image_zoom import image_zoom +``` + +The [full stack Python framework comparison](https://reflex.dev/) usually ends with Django for big projects and Flask for small ones. That advice oversimplifies how these frameworks actually behave in production and completely ignores the frontend problem both of them create. We're comparing what you get from each framework, how they handle the split between backend and frontend development, and why the newest option lets you stay in Python for your entire application. + +**TLDR:** + +- Django provides built-in admin interfaces and ORM for content-heavy apps, while Flask offers minimal structure for API-focused microservices +- Reflex lets you write both frontend and backend in pure Python, eliminating the need for JavaScript frameworks like React or Vue +- Django has an 18-year ecosystem powering Instagram and Pinterest, Flask holds 33% of Python web framework usage +- Reflex deploys with a single `reflex deploy` command and powers 1M+ apps at 40% of Fortune 500 companies since 2023 + +## What Is a Full Stack Python Framework + +A full stack Python framework handles both frontend and backend development within a single cohesive system. Unlike microframeworks that provide minimal structure and require you to assemble your own solutions for routing, templating, and database management, full stack frameworks come with integrated tools for building complete web applications. + +Python has become a leading language for full stack development because teams already use it for data science, machine learning, and backend services. This question has driven adoption across finance, healthcare, and enterprise software teams who want to maintain a single language across their entire application stack. + +## Django Overview and Core Features + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/django-vs-flask-vs-reflex_django.webp", border_radius="10px", alt="django.png", width="100%")), class_name="mb-4") +``` + +Django launched in 2005 and has spent two decades refining the batteries-included approach to web development. The framework ships with an ORM that abstracts database operations, an automatic admin interface for content management, and built-in user authentication. Django remains one of the most widely adopted options for teams building data-intensive applications. Security comes configured by default. Django protects against SQL injection, cross-site scripting, cross-site request forgery, and clickjacking without requiring additional configuration. + +### When to Choose Django for Your Project + +Django works best when your project needs an admin interface from day one. Teams building content management systems, internal tools, or data entry applications can skip weeks of custom admin panel development. Choose Django when working with relational data and complex queries. The ORM handles migrations, relationships, and query optimization without writing SQL, which matters for applications managing inventory systems, customer databases, or financial records where data integrity is required. + +Enterprise teams building user-facing applications benefit from Django's authentication system. User registration, password reset flows, permission groups, and session management come configured out of the box. + +## Flask Overview and Core Features + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/django-vs-flask-vs-reflex_flask.webp", border_radius="10px", alt="flask.png", width="100%")), class_name="mb-4") +``` + +Flask takes the opposite approach. Released in 2010, it provides routing and templating but leaves database management, authentication, and form validation to the developer. This microframework philosophy means you choose your own ORM, select your preferred authentication system, and assemble exactly the components you need. The core library weighs in at roughly 30KB. Flask gives you Werkzeug for WSGI utilities and Jinja2 for templating, then steps back. Want SQLAlchemy for your database? Install it. Need user sessions? Add Flask-Login. This design makes Flask popular for building APIs and microservices where teams want control over every dependency. + +### When to Choose Flask for Your Project + +Flask fits microservices architectures where each service needs different dependencies. Your authentication service might use one library while your payment processor uses another. Flask's minimal core lets you install exactly what each service requires without carrying unused framework features. API-first projects benefit from Flask's simplicity. Building JSON endpoints for mobile apps or single-page applications doesn't need Django's template system or admin interface. You write routes, return data, and skip everything else. + +Choose Flask when your team values component selection over convention. Teams with strong opinions about their tooling gain control at the cost of more integration work. + +## Reflex Overview and Core Features + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/django-vs-flask-vs-reflex_reflex.webp", border_radius="10px", alt="reflex.png", width="100%")), class_name="mb-4") +``` + +Reflex takes a different approach. Django and Flask handle backend logic but require JavaScript frameworks like React or Vue for the frontend. [Reflex](https://reflex.dev) lets you write both sides in Python, compiling Python code into a React frontend automatically. Released in 2023, Reflex has powered over 1 million applications and earned [28,000+ GitHub stars](https://github.com/reflex-dev/reflex). The framework ships with 60+ built-in components, state management through Python classes, and event handlers that modify application state through Python functions. Beyond the built-in library, Reflex lets you wrap and use any React component directly in Python, giving your team access to the entire React ecosystem without writing a single line of JavaScript. + +### When to Choose Reflex for Your Project + +Reflex fits teams that want to build interactive web applications without splitting development across Python and JavaScript. Your data science team already writes Python for analytics, machine learning models, and backend services. Reflex lets them build user-facing dashboards and internal tools using the same language without hiring frontend specialists. + +Choose Reflex when you need real-time data visualization or interactive dashboards. The framework handles state management through Python classes and updates the UI automatically when data changes. Financial analysts building trading dashboards, healthcare teams creating patient management systems, and operations teams monitoring live metrics can write both the data processing logic and the interface in Python. + +Teams building applications for non-technical users benefit from Reflex's component library and deployment simplicity. Business analysts can describe requirements, the AI builder generates Python code, and developers review and deploy with `reflex deploy`. This workflow works for consulting firms standardizing client deliverables, enterprises building admin panels, and organizations where domain experts need to understand and modify production systems. + +Reflex makes sense when your organization needs on-premises deployment or VPC options for compliance. Finance, healthcare, and government teams can run the framework in their own infrastructure while maintaining data sovereignty. The Python codebase lets security teams audit applications using standard tools without reverse-engineering JavaScript bundles. + +## Comparing Django, Flask, and Reflex + +To compare these three Python frameworks, we looked at a number of key comparisons: + +- Performance and speed +- Architecture and design philosophy +- Learning curve and developer experience +- Ecosystem, community, and enterprise adoption +- Frontend considerations +- Deployment, hosting, and production operations + +### Performance and Speed Comparison + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/django-vs-flask-vs-reflex_server-stacks.webp", border_radius="10px", alt="Server stacks comparison across Django, Flask, and Reflex frameworks", width="100%")), class_name="mb-4") +``` + +Django with ASGI can handle 3,000 requests per second in production. Flask's lightweight architecture delivers faster response times for simple JSON API responses, where minimal overhead benefits basic request-response cycles. But, raw benchmarks don't tell the full story. Django's ORM adds query overhead versus raw SQL, while Flask's performance varies based on your library choices. Reflex, on the other hand, compiles Python into React, introducing a compilation step during development but delivering standard React performance in production. + +## Architecture and Design Philosophy Comparison + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/django-vs-flask-vs-reflex_architecture-patterns.webp", border_radius="10px", alt="Architecture patterns comparison across Django, Flask, and Reflex", width="100%")), class_name="mb-4") +``` + +Django enforces an MVT (Model-View-Template) pattern where models define data structures, views contain business logic, and templates render HTML. This opinionated structure means projects follow predictable patterns with consistent locations for authentication logic, database queries, and template inheritance. Flask, though, imposes no architectural pattern, letting you build REST APIs with functional views or structure monoliths around blueprints. This flexibility creates maintenance challenges when developers make inconsistent choices. Only Reflex uses [state-driven architecture](https://reflex.dev/blog/reflex-architecture/) where Python classes define application state, functions modify state through event handlers, and the UI updates automatically when state changes. + +## Learning Curve and Developer Experience + +Django's batteries-included approach creates a steeper initial learning curve for new developers. You learn the ORM, middleware, template inheritance, and admin configuration before shipping features. Once mastered, conventions answer architectural questions and productivity increases. Flask is much simpler. It offers a minimal API surface. Beginners build working applications within hours, but production readiness requires choosing libraries for database migrations, form validation, and authentication. Reflex, though, removes the JavaScript barrier entirely. Python developers skip React, state management libraries, and frontend build tools. Write Python classes for state and Python functions for UI without context-switching between languages. + +## Ecosystem, Community, and Enterprise Adoption + +Django powers Instagram's content delivery and Pinterest's visual discovery engine, proving its ability to scale to billions of requests. The framework's 18-year history means extensive third-party packages for payments, search, caching, and API serialization. Flask, though, holds [33% of web framework usage](https://webandcrafts.com/blog/django-vs-flask-vs-fastapi) across Python projects. Its minimal core attracted developers who built an extension ecosystem covering nearly every use case, though quality varies across packages. Finally, Reflex has grown to 28,000+ GitHub stars and [powers applications](https://reflex.dev/customers) at 40% of Fortune 500 companies since launching in 2023. The framework removes the need to hire separate frontend and backend specialists. + +## Frontend Considerations + +Django's template system generates server-side HTML with template inheritance and filters, while Django REST Framework serializes data for separate JavaScript frontends. Flask offers no opinion, requiring teams to choose between server-generated Jinja2 templates or API-only backends that feed React or Vue applications. + +Both approaches split your team between Python backend developers and JavaScript frontend specialists. Reflex ships [60+ components](https://github.com/reflex-dev/reflex) that compile from Python to React automatically. Write your UI in Python functions, manage state through Python classes, and deploy a production React application without touching JavaScript. + +## Deployment, Hosting, and Production Operations + +Django needs WSGI server setup with Gunicorn or uWSGI, reverse proxy configuration via Nginx, static file management, and database migrations. You configure environment variables, SSL certificates, and process monitoring manually. Flask follows similar deployment patterns with lighter resource needs. The same WSGI servers apply, though smaller projects can use simpler hosting. Web server configuration, static assets, and database connections require manual setup. + +Reflex, though, deploys with `reflex deploy`. This command pushes your application to Reflex Cloud, which handles infrastructure provisioning, multi-region scaling, and monitoring automatically. CI/CD works with GitHub Actions and GitLab CI without extra configuration. You can also deploy to your own cloud provider or run everything on your own local infrastructure, giving your team full control over where your apps live. + +## Side-by-Side Comparison by Feature + +| Feature | Django | Flask | Reflex | | +|---|---|---|---|---| +| Framework Philosophy | Batteries-included with opinionated MVT architecture, built-in ORM, admin interface, and authentication system | Minimal microframework with routing and templating only, requiring developers to choose and integrate their own libraries | Full stack Python framework that compiles Python into React, eliminating the need for JavaScript while providing state management and 60+ UI components | | +| Frontend Development | Server-side templates with Jinja2 or separate JavaScript frontend using Django REST Framework for API serialization | No opinion on frontend, requiring choice between server-side Jinja2 templates or building separate JavaScript frontend | Pure Python frontend that compiles to React automatically, allowing developers to write UI components and state management entirely in Python | | +| Deployment Complexity | Requires WSGI server setup with Gunicorn, Nginx reverse proxy configuration, manual static file management, and database migration handling | Similar WSGI deployment requirements with lighter resource needs but same manual configuration for web server, static assets, and database connections | Single command deployment with 'reflex deploy' that handles infrastructure provisioning, multi-region scaling, and monitoring automatically | | +| Built-in Features | ORM with automatic migrations, admin interface, user authentication, form validation, security protections against SQL injection and XSS | Werkzeug for WSGI utilities and Jinja2 for templating only, requiring external packages for database, authentication, and form handling | 60+ built-in components, state management through Python classes, event handlers, automatic UI updates, and integrated frontend-backend architecture | | +| Ideal Use Cases | Content management systems, data-intensive applications with complex relational queries, best suited for teams already comfortable managing separate Python backends and JavaScript frontends for any interactive or user-facing features | Microservices architectures, API-first projects for mobile apps or SPAs, projects where teams want granular control over dependency selection | Fortune 500 teams and compliance-focused industries (finance, healthcare, government) building enterprise-grade internal tools, interactive dashboards, real-time data visualization, LLM chat apps, and AI-powered workflows entirely in Python, eliminating frontend specialist headcount, maintaining audit-ready codebases, and deploying to on-premises or VPC infrastructure for full data sovereignty | | + +## Why Reflex Offers a Unique Approach for Python Teams + +Django and Flask both stop at the backend boundary. When you need interactive dashboards, real-time data visualization, or responsive user interfaces, you reach for React, Vue, or another JavaScript framework. Your Python backend team hands off to frontend specialists, creating coordination overhead and splitting your codebase across languages. + +Reflex was built to remove that handoff entirely. Python teams write state management, UI components, and business logic in the same language they already use for data processing and API development. The framework has powered over 1 million applications and earned adoption at 40% of Fortune 500 companies building internal tools where Python expertise already exists but JavaScript skills don't. + +## Final Thoughts on Selecting Your Full Stack Python Framework + +You can build production applications with any of these frameworks, but your team structure matters more than feature lists. If you're already maintaining separate Python and JavaScript teams, Django or Flask with a React frontend makes sense. [Reflex offers a different full stack approach](https://reflex.dev/) by letting your existing Python developers handle both sides of the application. See what [Reflex's pricing](https://reflex.dev/pricing) looks like for teams of your size. The best framework is the one that keeps your team productive without forcing them to become experts in languages they don't use daily. + +## FAQ + +### How does Reflex differ from Django and Flask for full stack development? + +Django and Flask both require JavaScript frameworks like React or Vue for the frontend, splitting your team between Python backend developers and JavaScript frontend specialists. Reflex lets you write both frontend and backend in pure Python, compiling Python code into a React application automatically without touching JavaScript. + +### Can I deploy Reflex applications in my own infrastructure? + +Yes, Reflex supports on-premises deployment and VPC options for enterprise security requirements. You can run Reflex's AI App Builder in your own environment while maintaining full control over data and code, meeting compliance requirements that cloud-only tools cannot satisfy. + +### What's the learning curve like for Python developers new to web development? + +Python developers can start building full stack applications immediately without learning React, state management libraries, or frontend build tools. You write Python classes for state and Python functions for UI, skipping the JavaScript barrier entirely while Django requires learning ORM, middleware, and template systems first. + +### How does Reflex handle production performance compared to Django or Flask? + +Reflex compiles Python into standard React for the frontend, delivering production React performance with Python backend response times comparable to Flask APIs. The compilation happens during development, so production applications run as optimized React frontends with Python backends. + +### When should I choose Reflex over Django for my project? + +Choose Reflex when you need interactive dashboards, real-time data visualization, or responsive user interfaces without hiring separate frontend specialists. If your team already uses Python for data science, machine learning, or backend services and wants to maintain a single language across your entire application stack, Reflex removes the coordination overhead of splitting your codebase across languages. diff --git a/blog/streamlit-vs-dash-python-dashboards.md b/blog/streamlit-vs-dash-python-dashboards.md new file mode 100644 index 000000000..b8221969b --- /dev/null +++ b/blog/streamlit-vs-dash-python-dashboards.md @@ -0,0 +1,152 @@ +--- +author: Tom Gotsman +date: 2026-04-02 +title: "Streamlit vs. Dash for Python Dashboards: Which One Should You Actually Use? (April 2026)" +title_tag: "Streamlit vs Dash Python Dashboards April 2026" +description: "Streamlit vs. Dash for Python dashboards: Compare script reruns vs. callbacks, performance, and production features." +image: /blog/streamlit-vs-dash_thumbnail.webp +tag: Builder +meta: [ + {"name": "keywords", "content": "streamlit vs dash for python dashboards, streamlit vs dash comparison, streamlit or dash"} +] +faq: [ + {"question": "Which tool is better for rapid prototyping: Streamlit or Dash?", "answer": "Streamlit wins for rapid prototyping because you write linear Python scripts without worrying about callbacks or component IDs. Add a few commands like `st.dataframe()` or `st.pyplot()`, and you get a working interface in minutes. Dash requires explicit callback setup and layout construction with Python objects, which slows down initial development. Reflex sits in a middle ground: you get a clean Python-only development experience like Streamlit, but the declarative state model scales into production without architectural rewrites, so your prototype doesn't become throwaway code."}, + {"question": "What are the main performance differences between Streamlit and Dash?", "answer": "Streamlit reruns your entire script on every user interaction unless you add caching, which creates performance bottlenecks for expensive operations. Dash executes only the specific callbacks tied to changed inputs, making it more performant by default. However, Dash runs single-threaded out of the box, so expensive calculations in one callback block other users unless you manually configure concurrent processing with Gunicorn. Reflex handles this differently: its async FastAPI backend processes concurrent requests without blocking, so multiple users can interact simultaneously without performance interference and without manual server configuration."}, + {"question": "How does Streamlit's caching work, and when does it cause problems?", "answer": "Streamlit's `@st.cache_data` and `@st.cache_resource` decorators memoize function outputs based on input parameters to avoid redundant computation during script reruns. The cache invalidates whenever parameters change, triggering full recomputation. You need to identify which operations to cache and manage cache invalidation yourself, which becomes complex as applications grow. Reflex avoids this problem entirely: its dependency tracking updates only the components affected by a state change, so you're not manually identifying what to cache or worrying about stale data."}, + {"question": "When should I choose Dash over Streamlit for a dashboard project?", "answer": "Choose Dash when you need precise control over application flow, have multiple connected components that update independently, or already use Plotly visualizations heavily. Dash's stateless callback architecture works better for complex dashboards where only specific sections need to update based on user interactions, avoiding Streamlit's full script reruns. That said, if your dashboard needs to grow into a production application with authentication and concurrent users, Reflex gives you that same component-level control through its state model without the callback wiring complexity."}, + {"question": "Do Streamlit or Dash include built-in authentication and deployment for production apps?", "answer": "Neither framework ships with production-ready authentication or secure deployment out of the box. Both require third-party integrations or reverse proxy configurations for OAuth and session management. Streamlit Community Cloud handles simple deployments but lacks business security controls, while open-source Dash needs external DevOps work. Paid enterprise versions like Dash Enterprise bundle these features but require additional investment. Reflex includes authentication with Clerk, Google Auth, Azure, and OpenID Connect providers, plus RBAC and one-command deployment to Reflex Cloud, a private VPC, or on-premises environments, all without a separate enterprise license."}, + {"question": "What is the main architectural difference between Streamlit and Dash?", "answer": "Streamlit uses a script rerun model where the entire Python script executes from top to bottom on every user interaction, while Dash uses a stateless callback system where only specific decorated functions execute when their corresponding inputs change. Reflex takes a different approach entirely: a declarative class-based state model that automatically tracks dependencies and updates only affected components through WebSocket synchronization, avoiding the trade-offs of both architectures."}, + {"question": "Why does Streamlit's rerun model cause memory scaling issues?", "answer": "Streamlit spawns a separate Python process for each user session, holding UI state and execution context in RAM. With ten concurrent users, that means ten Python interpreters running simultaneously, and with large datasets in memory, this quickly adds up and limits horizontal scaling options. Reflex avoids this entirely with an async FastAPI backend that handles concurrent users with isolated state and no per-session process overhead."}, + {"question": "How does Reflex handle concurrent users differently than Streamlit or Dash?", "answer": "Reflex runs on an async FastAPI backend that processes concurrent requests without blocking, allowing multiple users to interact with different dashboards simultaneously with isolated state. This contrasts with Streamlit's per-user Python processes and Dash's single-threaded default that blocks operations until manual Gunicorn configuration is added."}, + {"question": "What makes callback management in Dash complex as applications grow?", "answer": "Dash requires explicit callback wiring for every component interaction, so dashboards with multiple connected components can require dozens of callbacks to coordinate updates. This splits related business logic across separate decorated functions that become difficult to track at scale. Reflex replaces this pattern with class-based event handlers that keep related logic organized together as applications grow."}, + {"question": "Can I deploy Streamlit or Dash applications to my own infrastructure?", "answer": "Yes, but both require external DevOps configuration for production deployments. Open-source versions lack built-in deployment tooling for authentication, load balancing, and secure hosting, so you'll need to configure reverse proxies, session management, and infrastructure yourself unless you use paid enterprise versions. Reflex supports one-command deployment with reflex deploy to Reflex Cloud, a private VPC, or on-premises environments without a separate enterprise license."}, + {"question": "What is Reflex's state management approach and how is it different?", "answer": "Reflex uses Python class-based state management where you define application state in classes and write event handlers as methods. The framework automatically tracks dependencies and updates only affected components through WebSocket synchronization, avoiding both Streamlit's full script reruns and Dash's manual callback wiring."}, + {"question": "Why do data science teams struggle when moving Streamlit or Dash prototypes to production?", "answer": "Both frameworks handle the UI layer well for demos but lack authentication, RBAC, secure deployment, and production features that business applications need. Teams end up spending weeks building session management, user databases, and infrastructure that isn't included in the framework. Reflex includes built-in authentication with Clerk, Google Auth, Azure, and OpenID Connect providers, plus RBAC and one-command deployment, so production requirements are covered from day one."}, + {"question": "How much code reduction does Reflex provide compared to Dash?", "answer": "Teams building equivalent dashboards in Reflex report approximately 50% less code compared to Dash implementations. The class-based structure and automatic dependency tracking in Reflex eliminate the callback wiring ceremony that accumulates in Dash applications as connected components multiply."}, + {"question": "What types of organizations use Reflex for production applications?", "answer": "Finance teams run trading dashboards, healthcare organizations manage patient data, and government agencies deploy internal tools using Reflex. The framework is trusted by 40% of Fortune 500 companies for building production-grade web applications entirely in Python."}, + {"question": "Does Dash support multi-page applications out of the box?", "answer": "No, Dash requires extra configuration beyond the core framework to support multi-page applications. This adds another layer of setup complexity on top of the callback wiring and layout construction that Dash already requires. Reflex includes multi-page support with a built-in file-based routing system, dynamic routes, and shared state across pages without additional configuration."} +] +--- + +```python exec +import reflex as rx +from pcweb.components.image_zoom import image_zoom +``` + +You've narrowed your Streamlit vs Dash decision down to architecture differences: script reruns versus callbacks, rapid prototyping versus fine-grained control. But here's the part that matters more than syntax: neither framework gives you what production dashboards actually need without extensive custom work. Streamlit's full reruns mean expensive operations repeat constantly unless you identify and cache them perfectly. Dash's callback model stays performant but fragments your business logic across decorated functions that become hard to track at scale. Both look clean in demos with CSV files and sample data. Then you connect to PostgreSQL, add user authentication, implement role-based access, and realize the framework only covered the UI layer. Your team is now building session management, configuring reverse proxies, and wiring background job queues because the tool that promised Python-only development still requires a full stack. + +**TLDR:** + +- Streamlit reruns your entire script on every user interaction, creating performance bottlenecks for database queries and API calls unless you manually cache operations. +- Dash requires explicit callback wiring for each component interaction, causing complexity to explode as you add connected filters, graphs, and tables. +- Neither framework includes authentication, RBAC, or production deployment features that business applications require from day one. +- Reflex provides built-in auth, RBAC, async state management, and one-command deployment with 50% less code than equivalent Dash implementations. +- Reflex is a full-stack Python framework trusted by 40% of Fortune 500 companies for building production-grade web apps entirely in Python without JavaScript. + +## What Streamlit Does and How It Works + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/streamlit-vs-dash_streamlit.webp", border_radius="10px", alt="streamlit.png", width="100%")), class_name="mb-4") +``` + +Streamlit is an open-source Python framework built for data scientists and analysts who want to turn scripts into interactive web applications without learning frontend development. You write Python, add a few Streamlit commands, and get a working web interface. The framework operates on a script rerun model. Every time a user interacts with a widget (clicking a button, adjusting a slider, typing in a text box), [Streamlit reruns](https://docs.streamlit.io/develop/concepts/architecture/run-your-app) your entire Python script from top to bottom. This approach makes the mental model simple: your script executes linearly, and the UI reflects the current state of that execution. + +The rerun behavior creates challenges when scripts involve expensive operations. Loading a large dataset, training a model, or querying an external API happens again on every interaction unless you explicitly cache results. Streamlit provides `@st.cache_data` and `@st.cache_resource` decorators to prevent redundant computation, but you need to identify what should be cached and manage cache invalidation yourself. + +For data that needs to persist between reruns (like form inputs or user selections), Streamlit offers `st.session_state`, a dictionary-like object that survives script reruns. Without this, variables reset to their initial values each time the script executes. + +The framework integrates naturally with [Python data libraries](https://www.datacamp.com/blog/top-python-libraries-for-data-science). Pass a Pandas DataFrame to `st.dataframe()`, a Matplotlib figure to `st.pyplot()`, or a Plotly chart to `st.plotly_chart()`. This makes Streamlit particularly appealing for quick data exploration dashboards and internal analytics tools. + +## What Dash Does and How It Works + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/streamlit-vs-dash_dash.webp", border_radius="10px", alt="dash.png", width="100%")), class_name="mb-4") +``` + +Dash takes a different architectural approach. Built on Flask, React, and Plotly.js, Dash connects UI components to Python functions through an explicit callback system. You define which component properties trigger updates and which components receive the results. The callback model is stateless. When a user adjusts a dropdown or slider, only the callback functions tied to that specific input execute. The rest of your application stays idle. This contrasts with Streamlit's full script rerun and can be more performant when you have expensive operations that don't need to re-execute on every interaction. Each callback decorates a Python function with `@app.callback`, specifying Input components that trigger the function, Output components that receive return values, and optional State components that pass current values without triggering execution. A dropdown selection might trigger a callback that filters data and returns an updated graph. + +The trade-off for this control is complexity. Dash requires you to think in terms of component IDs, property names, and callback chains. Building layouts means working with Python objects that represent HTML and CSS structures like `html.Div` and `dbc.Row`. You get precise control over application flow and how components display, but the learning curve steepens compared to Streamlit's linear script model. + +Dash integrates tightly with Plotly's charting library, making it a natural choice for teams already invested in Plotly visualizations. + +## Streamlit's Script Rerun Model Creates Performance Issues + +```python eval +rx.el.div(image_zoom(rx.image(src=f"{REFLEX_ASSETS_CDN}blog/streamlit-vs-dash_performance-bottleneck.webp", border_radius="10px", alt="Performance bottleneck visualization for dashboard frameworks", width="100%")), class_name="mb-4") +``` + +The rerun model works fine for small scripts that load a CSV and display a few charts. But when you start building real applications with database queries, API calls, or model inference, the architecture becomes a bottleneck. + +Consider a dashboard that loads 500MB of time-series data from PostgreSQL and applies transformations before visualization. A user adjusts a date range filter. Streamlit reruns the entire script, re-executing the database query and transformations unless you've wrapped every expensive operation in caching decorators. Miss one uncached operation and your users wait several seconds for each interaction. + +The memory model creates scaling problems. Streamlit spawns a separate Python process for each user session, holding UI state and execution context in RAM. Ten concurrent users mean ten Python interpreters running at once. With large datasets in memory, this adds up quickly. Load balancers must route users to the same server instance throughout their session, limiting horizontal scaling options and creating sticky session dependencies. + +Caching helps but introduces new problems. The `@st.cache_data` decorator memoizes function outputs based on input parameters. Change a parameter and the cache invalidates, triggering a full recomputation. + +## Dash's Callback Architecture Gets Complex at Scale + +Dash's callback model requires explicit wiring for every interaction. Filter a table or toggle a chart? Each needs its own decorated callback function. This precision works for simple cases, but the ceremony accumulates quickly. When you build dashboards with multiple connected components, the callback count explodes. Five dropdowns, three graphs, and two data tables can require fifteen callbacks to coordinate updates. One dropdown filters data, updates a graph, then changes options in another dropdown. Each connection needs its own decorated function. [Independent framework comparisons](https://hackernoon.com/a-quick-comparison-of-streamlit-dash-reflex-and-rio) consistently identify this complexity as a key trade-off for Dash's precise control model. + +Graph recalculation blocks other operations by default since Dash runs single-threaded. Expensive calculations in one callback prevent other users from interacting with the app. You can extract the Flask server and use Gunicorn for concurrent processing, but this requires manual configuration. + +Dash operates as a stateless framework, so tracking user selections across callbacks means storing them in `dcc.Store` components or serializing JSON into hidden div properties. These patterns function but feel like workarounds. + +The callback decorator syntax splits related logic across multiple functions. Business logic that belongs together fragments into separate callbacks triggered by different component interactions. Multi-page applications need extra configuration beyond the core framework. + +## Authentication, Deployment, and Production Readiness Gaps + +Neither Streamlit nor Dash ships ready for production business applications. Both frameworks require additional work to meet basic enterprise requirements that teams expect from day one: + +- **Authentication needs external solutions in both tools**. Streamlit requires third-party integrations or reverse proxy configurations. Dash follows the same pattern. Teams spend days wiring OAuth providers, session management, and user databases that have no framework support. +- **Deployment differs between paid and free versions**. Dash Enterprise provides horizontally scalable hosting and built-in authentication, but open-source Dash requires external DevOps resources to stand up infrastructure. Streamlit Community Cloud handles simple deployments but lacks the security controls and access management that business applications need. +- **Role-based access control doesn't exist in either framework**. Database connections require external libraries like SQLAlchemy. Background job processing needs separate implementation with Celery or similar tools. The frameworks assume you'll build these capabilities yourself or purchase enterprise versions that bundle them. +- **Data science teams building internal dashboards hit these gaps immediately**. The tool that takes ten minutes to prototype takes weeks to deploy securely because authentication, user management, and security features require custom integration work. + +## Reflex Gives You Production-Ready Python Dashboards Without the Trade-Offs + +Reflex, though, replaces the script rerun and callback patterns with a declarative state model. You define application state in Python classes, write event handlers as class methods, and the framework tracks dependencies automatically. When state changes, only affected components update through WebSocket synchronization. No full script reruns. No manual callback wiring. + +The architecture runs on an async FastAPI backend that handles concurrent requests without blocking. Multiple users interact with different dashboards at the same time, each with isolated state. Database queries in one user's session don't slow down another's view. + +Authentication comes built into the framework. Connect Clerk, [Google Auth](https://reflex.dev/blog/implementing-sign-in-with-google/), Azure, or any OpenID Connect provider with a few lines of configuration. RBAC controls which team members access which data and features. The ORM handles database connections and migrations. Background jobs process async tasks without external task queues. + +Deployment takes one command: `reflex deploy`. Applications go to Reflex Cloud infrastructure, your VPC for compliance requirements, or [on-premises environments](https://reflex.dev/blog/on-premises-deployment/) where finance, healthcare, and government teams need full control. Finance teams run trading dashboards. Healthcare organizations manage patient data. Government agencies [deploy internal tools](https://reflex.dev/blog/self-hosting-reflex-with-docker/). + +Teams building equivalent dashboards report approximately 50% less code compared to Dash implementations. The class-based structure stays organized as applications grow. The framework includes 60+ components and wraps any React library in pure Python when you need custom functionality. + +## Streamlit vs Dash vs Reflex: Complete Comparison + +| Feature | Streamlit | Dash | Reflex | +|---|---|---|---| +| Architecture Model | Full script rerun on every interaction. Entire Python script executes top-to-bottom when users click buttons, adjust sliders, or modify inputs. | Stateless callback system. Explicit decorator-based callbacks connect UI components to Python functions that execute only when triggered. | Declarative state model with automatic dependency tracking. WebSocket synchronization updates only affected components when state changes. | +| Performance Characteristics | Requires manual caching with @st.cache_data decorators to prevent expensive operations from re-executing. Memory intensive with separate Python process per user session. | Executes only specific callbacks tied to changed inputs. Single-threaded by default, requiring Gunicorn configuration for concurrent user support. | Async FastAPI backend handles concurrent requests without blocking. Multiple users interact simultaneously with isolated state and no performance interference. | +| State Management | st.session_state dictionary persists data between script reruns. Variables reset to initial values without explicit session state storage. | Stateless design requires dcc.Store components or JSON serialization into hidden div properties to track user selections across callbacks. | Python class-based state management with automatic persistence. Event handlers as class methods maintain organized logic as applications scale. | +| Production Features | No built-in authentication or RBAC. Requires third-party integrations or reverse proxy configurations for OAuth and user management. | No built-in authentication or RBAC in open-source version. Database connections and background jobs need external libraries like SQLAlchemy and Celery. | Built-in authentication with Clerk, Google Auth, Azure, and OpenID Connect providers. Includes RBAC, ORM for database operations, and background job processing. | +| Deployment Options | Streamlit Community Cloud for simple deployments lacking business security controls. Enterprise deployments require external DevOps configuration. | Open-source version needs manual infrastructure setup. Dash Enterprise provides managed hosting and authentication but requires paid license. | One-command deployment with reflex deploy to Reflex Cloud, private VPC for compliance, or on-premises for finance, healthcare, and government requirements. | +| Ideal Use Cases | Rapid prototyping and internal data exploration dashboards. Quick demonstrations with CSV files and sample datasets where performance isn't critical. | Complex dashboards with multiple connected components requiring precise control over update flow. Teams heavily invested in Plotly visualizations. | Production-grade business applications requiring authentication, RBAC, concurrent users, and enterprise deployment. Dashboards that need to scale without architectural rewrites. | + +## Final Thoughts on Building Python Dashboards + +The whole [Streamlit vs Dash comparison](https://reflex.dev/) becomes less relevant once you see what modern Python frameworks can do. Reflex removes the architectural problems both tools create while keeping Python as your only language. You get declarative state, built-in auth, and deployment that actually works for business applications. Check [pricing](https://reflex.dev/pricing) to start building production-ready dashboards today. + +## FAQ + +### Which tool is better for rapid prototyping: Streamlit or Dash? + +Streamlit wins for rapid prototyping because you write linear Python scripts without worrying about callbacks or component IDs. Add a few commands like `st.dataframe()` or `st.pyplot()`, and you get a working interface in minutes. Dash requires explicit callback setup and layout construction with Python objects, which slows down initial development. Reflex sits in a middle ground: you get a clean Python-only development experience like Streamlit, but the declarative state model scales into production without architectural rewrites, so your prototype doesn't become throwaway code. + +### What are the main performance differences between Streamlit and Dash? + +Streamlit reruns your entire script on every user interaction unless you add caching, which creates performance bottlenecks for expensive operations. Dash executes only the specific callbacks tied to changed inputs, making it more performant by default. However, Dash runs single-threaded out of the box, so expensive calculations in one callback block other users unless you manually configure concurrent processing with Gunicorn. Reflex handles this differently: its async FastAPI backend processes concurrent requests without blocking, so multiple users can interact simultaneously without performance interference and without manual server configuration. + +### How does Streamlit's caching work, and when does it cause problems? + +Streamlit's `@st.cache_data` and `@st.cache_resource` decorators memoize function outputs based on input parameters to avoid redundant computation during script reruns. The cache invalidates whenever parameters change, triggering full recomputation. You need to identify which operations to cache and manage cache invalidation yourself, which becomes complex as applications grow. Reflex avoids this problem entirely: its dependency tracking updates only the components affected by a state change, so you're not manually identifying what to cache or worrying about stale data. + +### When should I choose Dash over Streamlit for a dashboard project? + +Choose Dash when you need precise control over application flow, have multiple connected components that update independently, or already use Plotly visualizations heavily. Dash's stateless callback architecture works better for complex dashboards where only specific sections need to update based on user interactions, avoiding Streamlit's full script reruns. That said, if your dashboard needs to grow into a production application with authentication and concurrent users, Reflex gives you that same component-level control through its state model without the callback wiring complexity. + +### Do Streamlit or Dash include built-in authentication and deployment for production apps? + +Neither framework ships with production-ready authentication or secure deployment out of the box. Both require third-party integrations or reverse proxy configurations for OAuth and session management. Streamlit Community Cloud handles simple deployments but lacks business security controls, while open-source Dash needs external DevOps work. Paid enterprise versions like Dash Enterprise bundle these features but require additional investment. Reflex includes authentication with Clerk, Google Auth, Azure, and OpenID Connect providers, plus RBAC and one-command deployment to Reflex Cloud, a private VPC, or on-premises environments, all without a separate enterprise license. diff --git a/pcweb/flexdown.py b/pcweb/flexdown.py index c160fb6b0..67876f04c 100644 --- a/pcweb/flexdown.py +++ b/pcweb/flexdown.py @@ -576,6 +576,66 @@ def render(self, env) -> rx.Component: ) +def _markdown_table(*children, **props) -> rx.Component: + return rx.box( + rx.el.table( + *children, + class_name="w-full border-collapse text-sm border border-secondary-4 rounded-lg overflow-hidden bg-white-1 ", + **props, + ), + class_name="w-full rounded-xl border border-secondary-a4 my-6 max-w-full overflow-hidden", + ) + + +def _markdown_thead(*children, **props) -> rx.Component: + return rx.el.thead( + *children, + class_name="bg-secondary-1 border-b border-secondary-4", + **props, + ) + + +def _markdown_tbody(*children, **props) -> rx.Component: + return rx.el.tbody( + *children, + class_name="[&_tr:nth-child(even)]:bg-secondary-1", + **props, + ) + + +def _markdown_tr(*children, **props) -> rx.Component: + return rx.el.tr( + *children, + class_name="border-b border-secondary-4 last:border-b-0", + **props, + ) + + +def _markdown_th(*children, **props) -> rx.Component: + return rx.el.th( + *children, + class_name="px-3 py-2.5 text-left text-xs font-[575] text-secondary-12 align-top", + **props, + ) + + +def _markdown_td(*children, **props) -> rx.Component: + return rx.el.td( + *children, + class_name="px-3 py-2.5 text-xs font-medium first:font-[575] text-secondary-11 align-top", + **props, + ) + + +_markdown_table_component_map: dict[str, object] = { + "table": _markdown_table, + "thead": _markdown_thead, + "tbody": _markdown_tbody, + "tr": _markdown_tr, + "th": _markdown_th, + "td": _markdown_td, +} + component_map = { "h1": lambda text: h1_comp_xd(text=text), "h2": lambda text: h2_comp_xd(text=text), @@ -587,6 +647,7 @@ def render(self, env) -> rx.Component: "code": lambda text: code_comp(text=text), "pre": code_block_markdown, "img": lambda src: img_comp_xd(src=src), + **_markdown_table_component_map, } comp2 = component_map.copy() comp2["pre"] = code_block_markdown_dark diff --git a/pcweb/pages/blog/page.py b/pcweb/pages/blog/page.py index 0e7654492..8ff812316 100644 --- a/pcweb/pages/blog/page.py +++ b/pcweb/pages/blog/page.py @@ -286,7 +286,7 @@ def page(document, route) -> rx.Component: ), rx.el.div( xd.render(document, document.filename), - class_name="flex flex-col gap-4 w-full max-w-2xl", + class_name="flex flex-col gap-4 w-full xl:max-w-[45rem]", ), class_name="flex flex-col gap-12 flex-1", ), diff --git a/pcweb/templates/storypage.py b/pcweb/templates/storypage.py index 09d5f12a3..9a71e69cb 100644 --- a/pcweb/templates/storypage.py +++ b/pcweb/templates/storypage.py @@ -386,7 +386,7 @@ def wrapper(*children, **props) -> rx.Component: rx.el.div( rx.el.div( contents(*children, **props), - class_name="flex flex-col gap-4 flex-1 xl:max-w-2xl w-full", + class_name="flex flex-col gap-4 flex-1 w-full xl:max-w-[45rem]", ), story_table_of_contents(toc, study.route, study.company), class_name="flex flex-row gap-24 max-w-(--docs-layout-max-width) mx-auto w-full lg:pb-24 pb-12 pt-8 max-lg:px-6 justify-between",