https://github.com/ejw-data/sqlalchemy-flask-api-weather

SQLite Database accessed with SQLAlchemy to perform analysis and create Flask API of the data
https://github.com/ejw-data/sqlalchemy-flask-api-weather

flask pandas python sqlalchemy sqlite

Last synced: 3 months ago
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SQLite Database accessed with SQLAlchemy to perform analysis and create Flask API of the data

• Host: GitHub
• URL: https://github.com/ejw-data/sqlalchemy-flask-api-weather
• Owner: ejw-data
• Created: 2019-10-26T15:23:37.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-11-02T06:18:17.000Z (over 3 years ago)
• Last Synced: 2025-03-15T16:44:29.892Z (over 1 year ago)
• Topics: flask, pandas, python, sqlalchemy, sqlite
• Language: Jupyter Notebook
• Homepage:
• Size: 1010 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# sqlalchemy-flask-api-weather  

Author:  Erin James Wills, ejw.data@gmail.com 

![Weather API](./images/weather-api.png)

Photo by [NASA](https://unsplash.com/@nasa?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/s/photos/weather?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)  




## Overview  



The purpose of this repo is to become familiarized with SQLAlchemy's Object Relational Mapper (ORM).  To do this I will be using a SQLite database that has two tables about Hawaii weather (measurements and stations).  The code associated with this project can be found in the analysis folder within a jupyter notebook named `climate_analysis.ipynb`. In addtion to analyzing the data, a Flask API was created that connects to the database and accepts five different API requests - three of the requests return a predefined query result but the other two API's allow for the user to include one or two user inputs that are used within the API request as database filters.   




## Technologies

* Python

* Pandas

* SQLAlchemy

* Flask

* SQLite




## Data Source

Dataset generated by Trilogy Education Services. Origins beyond this is unknown. 




## Setup and Installation  

1. Environment needs the following:  

    * Python 3.6+

    * pandas

    * flask

    * sqlalchemy.ext.automap

    * sqlalchemy.orm

    * datetime

    * numpy

1. Activate your environment

1. Clone the repo to your local machine

1. Start Jupyter Notebook within the environment from the repo

1. Run `climate_analysis.ipynb` to test connections and queries

1. To run the flask app:

    *  In terminal, navigate to the top-level repo folder

    *  Activate your environment

    *  In terminal run:  `python app.py`

    *  The routes that can be used are listed on the page.




## Examples  

![API Routes](./images/api-options.png)

Fig.1 - API route options displayed in a browser




![API Results](./images/api-results.png)

Fig.2 - API results returning a single day's precipation and temperature measurements

  




![Analysis Histogram](./images/temperature-output-histogram.png)

Fig.3 - Matplotlib histogram of temperatures returned from a date range query

  




![Analysis Stacked Area Plot](./images/temperature-output-stacked-area-plot.png)

Fig.4 - Matplotlib stacked area chart of temperatures returned from a date range query