Sprint 9-10 - Figure Factory Pattern for Reusable Charts

Date: 2026-01-17 Tags: plotly, dash, design-patterns, python, visualization, reusability, code-organization

Context

Creating multiple chart types across 5 dashboard tabs, with consistent styling and behavior needed across all visualizations.

Problem

Without a standardized approach, each callback would create figures inline with:

Duplicated styling code (colors, fonts, backgrounds)
Inconsistent hover templates
Hard-to-maintain figure creation logic
No reuse between tabs

Solution

Created a figures/ module with factory functions:

figures/
├── __init__.py           # Exports all factories
├── choropleth.py         # Map visualizations
├── bar_charts.py         # ranking_bar, stacked_bar, horizontal_bar
├── scatter.py            # scatter_figure, bubble_chart
├── radar.py              # radar_figure, comparison_radar
└── demographics.py       # age_pyramid, donut_chart

Factory pattern benefits:

Consistent styling - dark theme applied once
Type-safe interfaces - clear parameters for each chart type
Easy testing - factories can be unit tested with sample data
Reusability - same factory used across multiple tabs

Example factory signature:

def create_ranking_bar(
    data: list[dict],
    name_column: str,
    value_column: str,
    title: str = "",
    top_n: int = 5,
    bottom_n: int = 5,
    top_color: str = "#4CAF50",
    bottom_color: str = "#F44336",
) -> go.Figure:

Prevention

Create factories early - before implementing callbacks
Design generic interfaces - factories should work with any data matching the schema
Apply styling in one place - use constants for colors, fonts
Test factories independently - with synthetic data before integration