Portfolio

Weather App
Weather App

A simple weather application that utilizes the weather.gov open API to display up-to-date weather patterns

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Weather Forecast App

Data Integration & Visualization

This application architecture facilitates real-time meteorological data ingestion by bridging the api.weather.gov REST endpoint with a localized PostgreSQL backend, delivering scalable 7-day forecasts through an optimized data visualization pipeline.

Key Features

  • Multi-Modal Geographic Search:
    Supports high-precision lookups via ZIP code or 'City, State' parameters, leveraging a localized database of over 42,000 U.S. locales.
  • High-Fidelity Meteorological Data:
    Delivers real-time atmospheric conditions, including precipitation metrics, wind velocity, and humidity, refreshed directly from federal endpoints.
  • Adaptive User Interface:
    Features a dynamic UI with automated asset mapping, matching real-time API weather codes to specific animated visual states.

Technical Breakdown

  • Asynchronous Fontend:
    Implements HTMX's 'filter-as-you-type' functionality to reduce server load and enhance user experience without the overhead of a full SPA framework.
  • Pandas Analytical Pipeline}:
    Orchestrates the ingestion of raw JSON payloads into structured DataFrames to perform rapid aggregation of 150+ hourly data points.
  • Statistical Trend Modeling:
    Utilizes vectorized max() and min() operations to calculate daily temperature trends and forecast variations with high computational efficiency.
Pandas Requests API HTMX
Zipcode Polygon Merger
Zipcode Polygon Merger

Combines zip codes to create new polygons. Displays a map showing zip code boundaries.

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Geospatial Polygon Merger

GIS Data Processing & Visualization

This tool processes and merges US Zip Code Tabulation Areas (ZCTAs) into interactive geospatial visualizations, demonstrating a complete pipeline from structured spatial data to automated HTML map rendering.

Key Features

  • Advanced Data Ingestion:
    Supports batch processing of Zip Code Tabulation Areas (ZCTAs) via comma-separated zip code entry or bulk .csv uploads.
  • dynamic Spatial Visualization:
    Leverages Folium and other plugins to provide interactive, fullscreen maps with adaptive zoom and custom tooltips.
  • Interoperable Data Export:
    Enables seamless collaboration by exporting merged geographic boundaries into industry-standard KML formats.

Technical Breakdown

  • Dynammic HTML Injection:
    Eliminates disk I/O overhead by utilizing Folium’s _repr_html_() method to generate map objects in-memory, which are then seamlessly injected into Django templates.
  • Pandas Transformation}:
    Employs ast.literal_eval to sanitize and parse complex string-based geometry into structured Python lists for real-time spatial processing.
  • KML Serialization:
    Translates internal coordinate objects into XML-based KML schemas to ensure interoperability with external GIS software.
GeoPandas Shapely PostGis DB
Superstore Dashboard
Superstore Dashboard

Sample dashboard that shows superstore sales with dynamic updating charts.

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Dynamic Analytics Dashboard

Asynchronous Business Intelligence

This Business Intelligence (BI) platform provides an interactive environment for exploring 'Superstore' retail datasets. By utilizing an HTMX-driven 'HTML-over-the-wire' architecture, the application delivers seamless KPI updates and diverse spatial analytics, enabling deep-dive data exploration without full-page reloads.

Key Features

  • Dynamic Metric Orchestration:
    Enables fluid date-range filtering and toggling between primary metrics (Sales vs. Profit vs. Orders) through asynchronous DOM injections.
  • Multi-variate Data Exploration:
    Renders a diverse suite of visualizations including Choropleth maps, Sunburst hierarchies, and Donut charts—to provide a multi-dimensional view of regional performance.
  • Synchronized Visual Analytics:
    Features integrated Stacked Bar and Line charts that update in tandem, allowing users to identify trends and categorical distributions across different time scales.

Technical Breakdown

  • Server-Side Analytical Aggregation:
    Offloads heavy computational lifting to the database layer using Django's .annotate(), Sum(), and Count() functions for optimized query performance.
  • Decoupled Endpoint Architecture}:
    Implements multiple JsonResponse and HTML-fragment endpoints to serve granular dashboard components independently.
  • Dynamic Visual Serialization:
    Programmatically converts Plotly figures into raw HTML strings via Plotly Express, enabling seamless, script-safe injection into the dashboard grid.
HTMX Plotly Express Django ORM