Hawaii Climate Data API

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).

Table of contents

• Technologies Used
• How to run locally
  • Run on Windows
  • Run on Docker
• Screenshots
  • API
    • Desktop
    • Mobile
  • Analysis

Technologies used

• Python
• HTML
• CSS
• Jupyter Notebook
• Matplotlib
• Pandas
• Flask
• Black
• VS Code
• Docker

How to run locally

• 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.

Run on Windows

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

Run on Docker

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.

Screenshots

Climate API

Desktop

Home (Desktop)

Home (Desktop) - Hover Effect

Mobile

Home (Mobile)

Home (Mobile) - Hover Effect

/api/v2/precipitation

Returns precipitation data for the most recent 12 months of dataset.

/api/v2/stations

Return station data.

/api/v2/tobs

Returns temperature observation data (tobs) from 12 most recent months of dataset.

/api/v2/aggregate/start-date/end-date

Returns minimum temperature, average temperature, and the max temperature for a given start or start-end range.

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Analysis

Precipitation summary statistics (within 12 month range)

Last 12 months of temperature observation data (tobs)