blazingplot 0.1.0

A Fast and Fully Controlled Data Visualization Library

BlazingPlot

A Fast and Fully Controlled Data Visualization Library built on top of Plotly, Dash, and Polars.

Features

• Interactive dashboards in both Jupyter Notebooks and standalone web applications
• High-performance data transformations using Polars
• Customizable visualizations with full control over chart elements
• Export capabilities for charts (PNG, HTML) and data (CSV)
• Customizable color palettes
• Modular, object-oriented design for extensibility

Installation

pip install blazingplot

Quick Start

import polars as pl
from blazingplot import Dashboard

# Create a sample DataFrame
data = pl.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [10, 20, 15, 25, 30],
    'category': ['A', 'B', 'A', 'B', 'A']
})

# Create a dashboard
dashboard = Dashboard(data, title="My Dashboard")

# Run the dashboard in a Jupyter notebook
dashboard.run(mode="jupyter")

# Or run as a standalone web application
# dashboard.run(mode="standalone", port=8050)

Visualization Types

BlazingPlot supports the following visualization types:

• Scatter Plot
• Line Chart
• Bar Chart
• Bubble Chart
• Pie Chart
• Box Plot
• Histogram
• 2D Histogram
• Time Series
• Stock Chart
• Error Bars
• Map Visualizations (Bubble Map, Tile Choropleth, Line on Tiles)
• Tables (Simple Table, Pivot Table)

Using Individual Visualizations

You can also use the visualization classes directly:

from blazingplot.visualizations.base import ScatterPlot

# Create a scatter plot
scatter = ScatterPlot(data, x='x', y='y', color='category', title="My Scatter Plot")

# Plot the visualization
fig = scatter.plot()

# Export to HTML
scatter.export_html("scatter.html")

# Export to PNG
scatter.export_png("scatter.png")

Dashboard Layout

The dashboard is divided into three main sections:

1. Column Selector: Select columns from your dataset
2. Visualization Controls: Configure your visualization
3. Visualization Display: View and interact with your visualization

Performance

BlazingPlot is designed for performance, leveraging Polars for data transformations and Plotly for rendering. It can handle large datasets efficiently.

Customization

You can customize the appearance of your visualizations using color palettes:

# Create a dashboard with a custom color palette
dashboard = Dashboard(data, title="My Dashboard", custom_palette="viridis")

Development

Setup

1. Clone the repository

git clone https://github.com/username/blazingplot.git
cd blazingplot

2. Install development dependencies

pip install -e ".[dev]"

Testing

pytest

Building

python -m build

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.