For this project, I used Python to perform analysis on a climate database before mapping said database to a custom built REST-API. The database features Hawaii Climate data between 2010-01-01 and 2017-08-23. I used Python and SQLAlchemy to do climate analysis and exploration on a hawaii.sqlite database, all analysis was completed using SQLAlchemy ORM queries, Pandas, and Matplotlib. The REST-API was developed using Python (Flask).
This project was for an assignment at Washington University's Data Analytics Boot Camp (2019).
- Python
- HTML
- CSS
- Jupyter Notebook
- Matplotlib
- Pandas
- Flask
- Black
- VS Code
- Docker
- If you are trying to run this application directly on a Windows OS, you will need to install
Python 3.11. - Otherwise, you will need to install Docker so you can run the application through Docker.
Assumes you are using a modern Windows client OS such as Windows 11 or Windows 10 and that Python 3.11 is installed.
It is assumed the user is at the root of this project and is using a UNIX style command line environment when referencing the CLI commands below.
Open terminal at root of this project then move into application/ directory:
cd application/
Create venv folder in application folder using Python 3.11:
python3.11 -m venv venv
Activate venv:
source venv/Scripts/activate
Install python packages to venv:
pip install -r requirements.txt
Start application:
python application.py
Firstly, confirm that Docker is installed and running. Next confirm that no other application is using port 5000 as port 5000 is needed for the Flask server. If you need to run Flask on an alternative port, you can modify the last line in the application/application.py file.
It is assumed the user is at the root of this project and is using a UNIX style command line environment when referencing the CLI commands below.
Open terminal at root of this project then move into docker/ directory:
cd docker/
Build Docker image and start Docker container:
docker compose up --build
Visit: http://localhost:5000 to use the application.
Returns precipitation data for the most recent 12 months of dataset.
Return station data.
Returns temperature observation data (tobs) from 12 most recent months of dataset.
Returns minimum temperature, average temperature, and the max temperature for a given start or start-end range.
