Skip to content

👟 Quick Start Tutorial

This tutorial will guide you through creating a Vega-Lite barchart visualization using the dataflow interface. We'll connect nodes to visualize a simple dataset, demonstrating Curio's core functionality.

Before You Begin

Make sure you have Curio installed and running. If you haven't installed Curio yet, check out our Installation Guide.

🌱 Getting Started

Step 1: Launch Curio

After installation, start Curio and open your browser to access the interface. You'll see a blank canvas ready for building your first dataflow.

Step 2: Understanding the Blank Canvas

When you first open Curio, you'll see a blank canvas like this:

Blank canvas

The icons on the left-hand side can be used to instantiate different nodes, including visualization ones.

📊 Creating a Barchart

In this tutorial, we are going to learn how Curio can easily help with visualizing a simple dataset using a Vega-Lite barchart.

Step 3: Add Data Loading Node

Let's start by instantiating a Data Loading node:

  1. Drag the Data Loading icon from the left sidebar.
  2. Change the view to Code.
  3. Enter the following synthetic dataset:

import pandas as pd

d = {'a': ["A", "B", "C", "D", "E", "F", "G", "H", "I"], 
     'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]}
df = pd.DataFrame(data=d)

return df
4. Hit run - The Python return will output df for the next node.

Data loading

External Files

External files can be referenced through regular Python file handling functions, given that the file was uploaded to the server. For simplicity, we're using synthetic data here.

Step 4: Add Vega-Lite Visualization Node

Now we'll create the visualization:

  1. Drag a Vega-Lite node.
  2. Connect it to the Data Loading node.
  3. Switch to the Grammar view and enter the Vega-Lite specification.

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "description": "A simple bar chart with embedded data.",
  "mark": "bar",
  "encoding": {
    "x": {"field": "a", "type": "nominal", "axis": {"labelAngle": 0}},
    "y": {"field": "b", "type": "quantitative"}
  }
}
4. Hit run

Vega lite

Data Flow Magic

Curio handles the data-flow automatically - Vega-Lite has access to the DataFrame outputted by the previous node.

Step 5: See Your Results

After hitting run, you can see that a barchart was created:

Final result

Congratulations!

You created your first data-flow using Curio! 🎉

🔍 What You've Learned

In this tutorial, you've learned:

  • Node Creation - How to instantiate different types of nodes.
  • Code vs Grammar Views - Different ways to configure nodes.
  • Data Flow - How data passes between connected nodes.
  • Vega-Lite Integration - Creating visualizations with grammar of graphics.

🎉 Next Steps

Congratulations! You've created your first Curio workflow. Here's what to explore next:

Continue Learning

  • Explore Examples: Check out pre-built workflows in the examples gallery.
  • Try Advanced Features: Experiment with UTK integration and interactive dashboards.
  • Build Complex Workflows: Connect multiple nodes for urban analysis pipelines.

Common Use Cases

Curio excels at:

  • Visual Analytics - Integrating heterogeneous urban datasets with interactive visualizations.
  • Scenario Planning - What-if analysis and real-time urban development simulations.
  • Machine Learning Workflows - Human-in-the-loop model training and evaluation.
  • Energy & Environment - Building efficiency analysis and green infrastructure mapping.
  • Interactive Dashboards - Multi-view coordinated exploration with UTK and Vega-Lite.

🆘 Need Help?

If you encounter any issues:

  1. Check Node Outputs - Look for error messages in node execution results.
  2. Validate Connections - Ensure data types match between connected nodes.
  3. Community Support - Ask questions on Discord
  4. Report Issues - Open an issue on GitHub

Ready to dive deeper? Explore our curated Examples for advanced capabilities of Curio! 🚀