https://github.com/shivabajelan/hawaii-climate-analysis-surfsup-with_sqlalchemy
The analysis in this project aims to provide insight into the climate patterns of Honolulu in Hawaii and inform decisions regarding the best time to visit and what activities to plan.
https://github.com/shivabajelan/hawaii-climate-analysis-surfsup-with_sqlalchemy
pandas python sql sqlalchemy sqlalchemy-orm sqlite
Last synced: 11 days ago
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The analysis in this project aims to provide insight into the climate patterns of Honolulu in Hawaii and inform decisions regarding the best time to visit and what activities to plan.
- Host: GitHub
- URL: https://github.com/shivabajelan/hawaii-climate-analysis-surfsup-with_sqlalchemy
- Owner: Shivabajelan
- License: mit
- Created: 2024-02-04T01:07:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-05T14:41:19.000Z (over 1 year ago)
- Last Synced: 2025-10-13T23:13:56.185Z (11 days ago)
- Topics: pandas, python, sql, sqlalchemy, sqlalchemy-orm, sqlite
- Language: Jupyter Notebook
- Homepage:
- Size: 348 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Hawaii-Climate-Analysis-SurfsUp-with_SQLAlchemy
In this project, I analysed 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
I used Python and SQLAlchemy to connect to the SQLite database and reflected the tables into classes.
I performed a precipitation analysis to get the previous 12 months of data.
I performed a station analysis to calculate the total number of stations and find the most active station.
#### Part 2: Design a Climate App
I designed a Flask API based on the queries developed in Part 1.
I created static routes for precipitation, stations, temperature observations, and two other dynamic routs for 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.