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Federal Tax Revenue Visualizations - Streamlit Dashboard

Overview

This project provides tools to automatically download, clean, and visualize federal tax data from government sources (Office of Management and Budget, Congressional Budget Office, Tax Policy Center).

Project Structure

federal-tax-dashboard/
├── app.py                  # Streamlit dashboard (main entry point)
├── src/
│   ├── data_pipeline.py    # Download, clean, and cache all datasets
│   └── charts.py           # Plotly chart functions
├── .streamlit/
│   └── config.toml         # Streamlit theme configuration
├── requirements.txt
└── README.md

How to Use

View Dashboard App

Or see directions below for running locally

Prerequisites

  • Python 3.10+

(see requirements.txt for all requirements)

Installation

git clone https://github.com/ellie-cothren/federal-tax-figures.git
cd federal-tax-figures
pip install -r requirements.txt

Run Locally

streamlit run app.py

On first run, it downloads and caches data from OMB, TPC, and FRED (takes ~10–15 seconds)

Data Sources

Dataset Source Years Description
Effective Tax Rates Tax Policy Center 1979-2020 Average rates by income group & tax type
Revenue by Source (Amounts) OMB Historical Tables 2.1 1934-2024 Revenue in millions USD
Revenue by Source (%) OMB Historical Tables 2.2 1934-2024 Revenue as % of total
Consumer Price Index FRED Series CPIAUCSL 1947-2025 Consumer Price Index, all items

Original sources:

  • Office of Management and Budget (OMB) - Budget Historical Tables
  • Tax Policy Center - Reformatted data from CBO publications
    • Supplemental data for a Congressional Budget Office (CBO) specific report from November 2022, The Distribution of Household Income, 2019 linked here
  • Bureau of Labor Statistics (BLS) - CPI data used to adjust tax revenue amount for inflation

Note: Government URLs may change when new fiscal year budgets are released. Check error messages for updated links.

License

MIT License

Author

Ellie Cothren
Data Scientist

PhD Candidate, Economics, Indiana University

LinkedIn | GitHub | Personal Website