Show HN: Combining Python data analysis with ClojureScript UI (browser-based)

About This Project

This is the second installment in my exploration of browser-native development with Scittle. In my first article, Building Browser-Native Presentations with Scittle, I shared how I’ve been using Scittle to create interactive presentations for local meetups, spreading the love of Clojure and ClojureScript by showing how accessible it can be without build tools. That project came from a desire to make ClojureScript more approachable and, honestly, more fun.

After presenting those interactive ClojureScript demos to the community, I kept thinking: “How do I reach Python developers? How do I show them what ClojureScript brings to the table?” The Python community has incredible data science tools like Pandas and Matplotlib, but they’re typically tied to server-side execution. I wanted to bridge these two worlds in a way that would be both impressive and fun - showing Python developers that ClojureScript isn’t just another JavaScript framework, while giving Clojure developers a way to leverage Python’s rich ecosystem.

This project is my answer - and my way of keeping the joy of programming alive. By combining Pyodide (Python compiled to WebAssembly) with Scittle’s zero-build approach, I can demonstrate something pretty cool: you can write Python for data science, wrap it in elegant ClojureScript UIs, and deploy everything as a single HTML file. No Python backend, no Node.js build pipeline, no complexity. Just fun, interactive data science in the browser.

Whether you’re a Python developer curious about ClojureScript, a Clojure developer wanting to leverage Python libraries, or someone like me who just loves exploring what’s possible when you remove unnecessary complexity, I hope these examples inspire you. The source code is fully available for you to learn from, adapt, and build upon. Let’s keep programming fun and accessible.

Now let’s explore how ClojureScript and Python can work together, beautifully.

The Vision

Imagine being able to:

• Write Python data science code in your browser
• Visualize data with matplotlib without any backend
• Process data with pandas entirely client-side
• All while orchestrating everything with ClojureScript
• Without any build tools or server

This isn’t science fiction. It’s Pyodide + Scittle.

What is Pyodide?

Pyodide is a Python distribution compiled to WebAssembly that runs entirely in the browser. It includes:

• Full Python 3.11 interpreter
• NumPy, Pandas, Matplotlib, SciPy
• 100+ scientific packages
• Full file system (in-memory)
• ~8MB download (compressed)

Think of it as Python’s REPL, but in your browser.

Loading Pyodide

Before we can run any demos, we need to load Pyodide once. This happens automatically when you visit this page - the scripts below will load Pyodide from the CDN and initialize our shared bridge that all demos will use.

Simple Example: Hello Python

Let’s start with the simplest possible example - loading Pyodide and running Python code.

The demo below shows a basic Python REPL where you can:

• Load Pyodide from CDN
• Execute Python expressions
• See results instantly
• Handle errors gracefully

Try It Live

How It Works

The integration uses a shared bridge pattern:

1. pyodide_bridge.cljs - Manages the Pyodide instance and state
2. hello_python.cljs - Uses the bridge to run Python code
3. The bridge is loaded first, then the demo can use its functions

This prevents multiple Pyodide instances from conflicting!

Interactive Code Editor

Now let’s build something more powerful - an interactive code editor where you can write and run your own Python code!

Key Features

• Live code editing with syntax highlighting
• Run button to execute code
• Output display for results and errors
• Pre-filled with example code

Try It Live

Data Visualization with Matplotlib

Now for the exciting part - creating beautiful data visualizations with Python’s matplotlib library!

The demo below captures matplotlib plots as base64 PNG images and displays them inline. This technique:

• Uses matplotlib’s non-interactive ‘Agg’ backend
• Captures plots as PNG data
• Displays high-quality visualizations
• Includes pre-loaded examples (line, bar, scatter, pie charts)

Try It Live

DataFrame Analysis with Pandas

Beyond visualization, Pyodide includes the full Pandas library for powerful data analysis and manipulation!

Why Pandas in the Browser?

Pandas is the standard tool for data analysis in Python. With Pyodide, you can:

• Load and manipulate tabular data
• Perform statistical analysis
• Filter, group, and transform datasets
• All without a backend server!

The demo includes examples of:

• Creating DataFrames - Build tables from Python dictionaries
• Statistical Analysis - Summary statistics and correlations
• Data Transformations - Filter, calculate, sort, and rank
• GroupBy Operations - Aggregate data by categories

Try It Live

Complete Data Science Workflows

Now for the most powerful demonstration - combining Pandas and Matplotlib for complete end-to-end data science workflows!

Why Combine Them?

In real-world data science, you rarely use just one tool. You:

1. Load and clean data with Pandas
2. Analyze and transform to extract insights
3. Visualize results with Matplotlib
4. Present findings with tables and charts together

Complete Workflow Examples

Sales Trend Analysis:

• Load monthly sales data into DataFrame
• Calculate profit and margins
• Generate statistical summary
• Create trend lines and bar charts
• Display both tables and visualizations

Product Performance:

• Analyze product sales across quarters
• Aggregate by product category
• Compare units sold and revenue
• Show horizontal and vertical bar charts

Regional Breakdown:

• Multi-dimensional analysis by region and quarter
• Create pivot tables
• Generate 4 different chart types
• Display comprehensive dashboard

Time Series Analysis:

• 90 days of daily sales data
• Calculate 7-day and 30-day moving averages
• Aggregate to monthly totals
• Visualize trends and patterns

Key Features

• Side-by-side results - See both tables and charts
• Multiple visualizations - Up to 4 charts per workflow
• Real analysis - Statistical summaries, aggregations, transformations
• Complete code - Fully editable Python workflows

Try It Live

Interactive Data Analysis

Want to analyze your own data? Upload CSV files and explore them interactively with Pandas!

Key Features

• CSV Upload - Drag & drop or click to upload your data files
• Data Preview - See your data in formatted tables
• Statistical Summary - Automatic descriptive statistics
• Data Info - Column types, null counts, memory usage
• Export Results - Download processed data as CSV

What You Can Do

The demo processes your CSV data through Pandas and displays:

• First 10 rows of your dataset
• Complete statistical summary (mean, std, min, max, quartiles)
• Data types for each column
• Null value counts
• Memory usage information

Perfect for quick exploratory data analysis without installing anything!

Try It Live

Time Series Analysis

Analyze temporal patterns with moving averages, trends, and volatility using Pandas and Matplotlib!

Two Complete Examples

📈 Stock Market Analysis:

• Price trends with 5-day and 10-day moving averages
• Daily returns (percentage changes)
• Trading volume analysis
• 5-day rolling volatility (risk measurement)

🌡️ Sensor Data Analysis:

• Temperature and humidity patterns over 24 hours
• 3-hour moving averages for trend smoothing
• Correlation analysis (temperature vs humidity)
• Daily pattern visualization

Time Series Techniques

• Moving Averages - Smooth out noise to reveal trends
• Returns Analysis - Calculate daily percentage changes
• Volatility Metrics - Measure fluctuation using rolling standard deviation
• Correlation Analysis - Understand relationships between variables
• Pattern Recognition - Detect daily/seasonal patterns

What You’ll Learn

• How to calculate and visualize moving averages
• Identifying trend direction and potential reversals
• Measuring market/data stability and risk
• Understanding inverse correlations in environmental data
• Analyzing complete 24-hour cycles

What’s Possible?

With Pyodide + Scittle, you can:

• 📊 Data Visualization - Create interactive charts and plots with matplotlib
• 🐍 Python REPL - Run any Python code in the browser
• 🧮 Scientific Computing - Use NumPy, SciPy for calculations
• 🐼 Data Analysis - Process and analyze data with Pandas DataFrames
• 📈 Statistical Analysis - Descriptive statistics, correlations, grouping
• 🎯 Complete Workflows - Combine pandas analysis with matplotlib visualizations
• 📂 File Upload & Processing - Analyze your own CSV data files
• 📉 Time Series Analysis - Moving averages, trends, volatility, and patterns
• 🎨 Custom UIs - Wrap Python functionality in beautiful ClojureScript UIs

Next Steps

Want to dive deeper? Try:

• Experimenting with the Interactive Data Analysis demo above - upload your own CSV files!
• Exploring the Time Series Analysis demo - switch between stock and sensor data
• Modifying the code in any demo to create your own visualizations

Or explore advanced topics:

• Advanced pandas operations (joins, pivots, time series decomposition)
• Machine learning with scikit-learn
• Custom Python packages and modules
• Building complete data science applications

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Acknowledgments

A special thanks to Timothy Pratley for creating ClojureCivitas - a welcoming space where the Clojure community can come together to share knowledge, experiences, and our collective love for the Clojure/ClojureScript ecosystem. Projects like this thrive when we have platforms to showcase what’s possible and inspire others to explore.

Huge appreciation to Daniel Slutsky for co-creating and maintaining the incredible Scicloj ecosystem of tools - including Clay, Kindly, and many other libraries that make sharing and exploring data in Clojure not just easier, but genuinely fun. This article wouldn’t exist without Clay’s elegant approach to literate programming and data visualization.

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Questions? Find me on Clojurians Slack (@agilecreativity)

Built with Scittle, Pyodide, Reagent, and ❤️