An interactive Python application that analyzes the economic impact of COVID-19 across US states, combining public health data with economic indicators to reveal pandemic trends and regional disparities.
This data analysis tool processes and visualizes multi-source datasets to help understand how the COVID-19 pandemic affected different states economically. By integrating COVID-19 statistics with unemployment rates, income data, and demographics, the application provides comparative insights through interactive visualizations.
- Comprehensive Statistics: View total cases, deaths, confirmed vs. probable cases for any US state
- Population-Based Metrics: Calculate infection and mortality rates relative to state population
- Comparative Visualizations: Automatic bar chart comparisons with states having highest/lowest case counts
- Real-Time Context: Displays last updated date for COVID data
- Per Capita Income Tracking: Compare 2019 vs. 2020 per capita personal income changes
- Unemployment Trends: Visualize unemployment rate shifts during the pandemic year
- Year-over-Year Analysis: Dual subplot graphs showing economic indicator trends
- Impact Assessment: Quantifies economic changes with percentage calculations
- Flexible Input: Accept state names or two-letter state codes
- Interactive Terminal Interface: Simple menu-driven navigation
- Smart Validation: Input validation with helpful error messages
- Dynamic Visualizations: Auto-generated matplotlib charts based on data patterns
Data Processing
- NumPy array operations for efficient multi-file data manipulation
- Custom indexing algorithms for cross-referencing datasets
- Dynamic data type handling (string/numeric conversion)
Object-Oriented Design
Areaclass encapsulating state information- Modular helper functions for reusable data operations
Visualization
- Conditional plotting logic based on comparative analysis
- Matplotlib subplots for multi-metric visualization
- Custom formatting for improved readability
- Python 3.x - Core programming language
- NumPy - Array operations and numerical processing
- Matplotlib - Data visualization and plotting
- Pandas - Listed as dependency (available for extensions)
- Clone the repository:
git clone https://github.com/rxmox/Economy-and-Covid.git
cd Economy-and-Covid- Install required dependencies:
pip install numpy matplotlib pandas- Run the application:
python Final_project.py- Select a State: Enter either the full state name (e.g., "California") or state code (e.g., "CA")
- Choose Analysis Type:
- A - COVID-19 statistics and health impact
- B - Economic indicators and trends
- View Results: The application displays statistics and generates comparison visualizations
Please select a US State: New York
***Requested Area Information***
State: New York
State Code: NY
Region: Northeast
Choose a letter corresponding to a topic:
A - Covid
B - Economics
Comparative visualization showing case counts across states:
Nevada COVID-19 case comparison with other states
Death count comparisons across different states
Dual-axis graphs showing unemployment and income trends:
Per capita income and unemployment rate trends (2019-2020)
Interactive command-line interface with statistical output
This project integrates data from authoritative public sources:
- COVID-19 Data: NYTimes COVID-19 Dataset
- Unemployment Statistics: U.S. Bureau of Labor Statistics
- Demographics: USDA Economic Research Service
- Per Capita Income: FRED Economic Data
- State Mapping: Kaggle US States Dataset
| File | Description | Key Columns |
|---|---|---|
covid.csv |
COVID-19 cases and deaths | date, state, cases, deaths, confirmed/probable breakdowns |
unemployment.csv |
Unemployment rates | state, 2019 rate, 2020 rate |
state_per_capita.csv |
Per capita personal income | state, 2019 income, 2020 income |
state_demographics.csv |
Population by decade | state, population (1990-2020) |
states_to_region.csv |
State metadata | state, code, region, division |
- Data Integration: Merging multiple CSV datasets with different schemas
- Statistical Analysis: Calculating percentages, trends, and comparative metrics
- Data Visualization: Creating meaningful charts from complex datasets
- User Interface Design: Building intuitive terminal-based interactions
- Input Validation: Robust error handling and user guidance
- Code Organization: Clean, documented, maintainable code structure
- Problem Solving: Translating real-world questions into data queries
- Add time-series analysis for COVID trends over multiple months
- Include vaccination rate data and correlations
- Export analysis reports to PDF/CSV
- Web-based dashboard interface
- Machine learning predictions for economic recovery
- Regional comparison views (e.g., all Western states)
Developed as a final project for ENDG 233 (Computing for Engineers) at the University of Calgary, Fall 2021. This project demonstrates proficiency in Python programming, data analysis, and visualization techniques applied to real-world pandemic data.
Data accurate as of November 2021. This is an educational project showcasing data analysis and visualization capabilities.