VizzBreeze

A lightweight, high-performance transactional web-dashboard designed for advanced data flow analytics, multi-stage routing visualization, and statistical risk audit. Built natively on top of Streamlit, Pandas, and Plotly.

Key Features

Flat Transaction Processing Engine: Optimized for fast processing of un-aggregated logs (e.g., Client ➔ Fund ➔ Asset).
Global Session Synchronization: Seamlessly locks metric column selections, typography sizes, and alignments across all workspace views.
Auto-Scroll Suppression: Enhanced layout architecture prevents viewport jumps during widget updates and data updates.
Advanced Graph Matrix Analytics: Includes high-fidelity stacked charts, multi-dimensional Parcats layouts, Density Matrices, and automated IQR risk audit profiling tools.

Quick Start

You can install and launch the interactive analytical control room globally using just two commands in your terminal:

pip install vizzbreeze
vizzbreeze-run

Test Dataset: To explore the dashboard features instantly, you can use the pre-configured spreadsheet sample_data_unaggregated.xlsx located in the root folder of this repository. Just drag and drop it into the sidebar upload zone!

Jupyter Notebook Integration

VizzBreeze is fully compatible with Jupyter Notebooks. You can import individual chart engines independently to render interactive Plotly visualizations directly within your notebook cells:

import pandas as pd
import vizzbreeze as vb

# Load your un-aggregated logging vectors
df = pd.read_excel('sample_transactions.xlsx')

# Expose and build a high-fidelity interactive flow canvas on the fly
fig = vb.generate_parcats(
    df=df,
    display_nodes=['client_id', 'investment_fund', 'assets'],
    target_node='assets',
    value_col='invested_amount'
)

# Render the interactive chart inside your notebook cell viewport
fig.show()

Core Analytical Tabs

Flows: Explore multi-stage multi-dimensional category paths with thin, light axis labels and custom high-contrast hover tooltips.
Funnel: Track progressive conversion drops along specific workflow layers.
Structural Breakdown: Classic stacked column bar charts configured with responsive axis titles that update on the fly based on active filters.
Bento: An asymmetrical modular tile framework designed to compress complex multi-level dimensions into a clean, prioritized grid dashboard.
Density Matrix: High-density dashboard to scan cluster intersections instantly.
Anomaly & Risk Audit: Automated statistical anomaly profiling that detects process outliers without freezing the browser engine.

Requirements

Python >= 3.9
Streamlit >= 1.35.0
Pandas >= 2.1.0
Plotly >= 5.18.0

License

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

Metadata

Release files for vizzbreeze 1.0.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for vizzbreeze 1.0.3
File	Size	Uploaded
vizzbreeze-1.0.3.tar.gz	29.7 kB	Jul 22, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for vizzbreeze 1.0.3
File	Interpreter	ABI	Platform	Reset
vizzbreeze-1.0.3-py3-none-any.whl 24.9 kB Jul 22, 2026	Python 3	none	any	Details

Total release size: 54.6 kB

Release files / vizzbreeze-1.0.3.tar.gz

Download URL	vizzbreeze-1.0.3.tar.gz
Size	29.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`1e379693e4ced6404f0e9d443dd31a9f6533d560751a8105a0dc45ce78c630f2`
BLAKE2b-256 checksum How to use checksums	`244c813a8e913925df0e935da9267493faea5e3fddc3067d8f10ee92d1c79ef8`
Upload date	Jul 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.12.7`

Release files / vizzbreeze-1.0.3-py3-none-any.whl

Download URL	vizzbreeze-1.0.3-py3-none-any.whl
Size	24.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`98d621cbcb062fdb80ab6104780a4c9ee3d3baa743eb3382c6332357b7f6e8b3`
BLAKE2b-256 checksum How to use checksums	`d95b92410171e5bf02b8ddc5eb92fee9722b7f632190cd6446a16287287c980b`
Upload date	Jul 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.12.7`