Hawaii-Climate-Analysis: SurfsUp!

In this project, I analyzed and explored the climate of Honolulu, Hawaii using Python, SQLAlchemy, Pandas, and Matplotlib. The goal was to help with trip planning by conducting a climate analysis of the area.

Project Overview

I divided the project into two main parts:

Part 1: Analyze and Explore the Climate Data

1. I used Python and SQLAlchemy to connect to the SQLite database and reflected the tables into classes.
2. I performed a precipitation analysis to get the previous 12 months of data.
3. I performed a station analysis to calculate the total number of stations and find the most active station.

Part 2: Design a Climate App

1. I designed a Flask API based on the queries developed in Part 1.
2. I created routes for precipitation, stations, temperature observations, and specified date ranges.

Requirements

• Jupyter Notebook Database Connection
• Precipitation Analysis
• Station Analysis
• API SQLite Connection & Landing Page
• API Static Routes
• API Dynamic Route
• Coding Conventions and Formatting
• Deployment and Submission
• Comments

Deployment

I deployed the project to a GitHub repository, and it includes the necessary files for analysis and app development.

Acknowledgements

This project uses climate data from the Global Historical Climatology Network-Daily Database, which has been converted to metric units in Pandas.