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:

Jupyter Notebook Advanced Integration

VizzBreeze functions return native plotly.graph_objects.Figure instances, making them fully compatible with Jupyter Notebook visualization viewports.

Accessing Built-in Color Palettes

You don't need to hardcode HEX styles. Access the corporate design system directly from the package configuration:

import vizzbreeze as vb

# View all available palette names
print(vb.COLOR_PALETTES.keys())

# Extract a specific synchronized list of colors
chosen_colors = vb.COLOR_PALETTES["Warm Amber"]

Core Functions API Reference & Parameter Mapping

All visualization engines are fully modular, accept un-aggregated raw pandas.DataFrame inputs, and return native plotly.graph_objects.Figure interactive objects.

1. Multi-Stage Category Flows

fig = vb.generate_parcats(
    df=df,                          # pandas.DataFrame: Source transaction logs (un-aggregated)
    stage_nodes=['col1', 'col2'],   # list: Ordered column names defining the routing stages
    target_node='col3',             # str: The terminal endpoint column name
    value_col='amount',             # str: Volume metric column name (or "Number of rows (Sample size)")
    selected_palette=chosen_colors, # list/str: List of HEX codes or a built-in palette name string
    unit_divider=1.0,               # float: Optional division scaling factor (e.g., 1000 for Thousands)
    force_shuffle=False,            # bool/int: Seed integer to randomize color distribution maps
    chart_title="Flow Canvas",      # str: Custom graph header text
    title_size=20,                  # int: Font size of the chart title
    width_px=1050,                  # int: Holster canvas width (1050 fits Jupyter viewports perfectly)
    height_px=500                   # int: Holster canvas height
)

2. Conversion Pipeline

fig = vb.generate_funnel_chart(
    df=df,
    stage_nodes=['col1', 'col2'],   # list: Sequence of progressive funnel workflow steps
    target_node='col3',             # str: Terminal checkpoint layer name
    value_col='amount',             # str: Volume metric target column
    selected_route_dict=route_dict, # dict: Active routing coordinate filters (e.g., {'col1': 'Client_1'})
    selected_palette=chosen_colors,
    unit_divider=1.0,
    force_shuffle=False,
    chart_title="Pipeline Drops",
    width_px=1050,
    height_px=500
)

3. Composition Stacked Bars

fig = vb.generate_stacked_bar_chart(
    df=df,
    stage_nodes=['col1', 'col2'],   # list: Categories to aggregate and display on the horizontal X-axis
    target_node='col3',             # str: Legend segment categorical target breakdown
    value_col='amount',             # str: Numeric target volume metric
    selected_palette=chosen_colors,
    unit_divider=1.0,
    force_shuffle=False,
    chart_title="Composition",
    width_px=1050,
    height_px=500
)

4. Bento Treemap Tiles

fig = vb.generate_bento_treemap(
    df=df,
    id_col='target_dimension',      # str: Unique column name to rank and partition into tiles
    value_col='amount',             # str: Target numeric metric column name
    selected_palette=chosen_colors,
    unit_divider=1.0,
    force_shuffle=False,
    chart_title="Bento Allocation Top-15", # Automatically truncates grid layout to Top 15 nodes
    width_px=1050,
    height_px=500
)

5. Intersection Density Matrix

fig = vb.generate_heatmap(
    df=df,
    x_col='category_x',             # str: Categorical split column for horizontal partitions (X-Axis)
    y_col='category_y',             # str: Categorical split column for vertical partitions (Y-Axis)
    value_col='amount',             # str: Numeric target volume or "Number of rows (Sample size)"
    selected_palette=chosen_colors,
    unit_divider=1.0,
    force_shuffle=False,
    chart_title="Density Matrix",
    show_annot=True,                # bool: Inject clean, non-zero numeric labels inside active cells
    width_px=1050,
    height_px=500
)

6. Statistical Risk Audit Profiler

fig = vb.generate_outliers_chart(
    df=df,
    stage_col='origin_node',         # str: Operational source checkpoint step
    target_col='destination_node',   # str: Operational destination checkpoint step
    value_col='amount',              # str: Numeric target weight column to compute strict IQR bounds
    selected_palette=chosen_colors,
    unit_divider=1.0,
    force_shuffle=False,
    chart_title="Statistical Anomaly Profile", # Automatically plots a dashed line at Q3 + 1.5*IQR upper boundary
    width_px=1050,
    height_px=500
)

Core Analytical Tabs

1. Flows: Explore multi-stage multi-dimensional category paths with thin, light axis labels and custom high-contrast hover tooltips.
2. Funnel: Track progressive conversion drops along specific workflow layers.
3. Structural Breakdown: Classic stacked column bar charts configured with responsive axis titles that update on the fly based on active filters.
4. Bento: An asymmetrical modular tile framework designed to compress complex multi-level dimensions into a clean, prioritized grid dashboard.
5. Density Matrix: High-density dashboard to scan cluster intersections instantly.
6. 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.