This project is an exercise in using Python for data analysis. It is for my personal purpose of becoming more familiar with Python's statistical and econometric applications.
- data.py handles the loading, processing, and cleaning of data
- inflation_tracker.ipynb is an IPython Notebook where I develop my models
- I have opted not to save files with output, to save space
- .env.example is where you should store your FRED API Key, make sure to change it to .env
- dashboard.py is a dashboard that can be ran locally via Streamlit
- Streamlit uses port 8501 by default
streamlit run dashboard.py
- Clone the repository:
git clone https://github.com/anagkan/inflation-tracker.git
cd inflation-tracker- Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies:
pip install -r requirements.txt-
Get a FRED API key:
- Go to https://fred.stlouisfed.org/docs/api/api_key.html
- Request a free API key
-
Create a
.envfile in the project root using .env.example as a guide.
inflation_tracker/
├── data.py # Data fetching and processing functions
├── dashboard.py # Streamlit dashboard
├── inflation_tracker.ipynb # Jupyter notebook with detailed analysis
├── requirements.txt # Python dependencies
├── .env.example # Example environment file
├── .gitignore
└── README.md
Update later...