Climate Analysis and Weather API

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Table of Contents

• Description
• Data Files
• Features
• Installation
• Results

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Description

This project conducts a climate analysis on weather data in Hawaii and builds a simple Flask-based API to deliver the results. Using Python and SQLAlchemy, the project interacts with a SQLite database containing weather station data and temperature measurements. A Jupyter Notebook is used for exploratory data analysis and visualizations.

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Data Files

The project includes the following files:

File Name	Description
hawaii_measurements.csv	Contains weather measurements like precipitation and temperature.
hawaii_stations.csv	Contains information about weather stations in Hawaii.
hawaii.sqlite	SQLite database file containing weather data.
climate_starter.ipynb	Jupyter Notebook for data exploration and analysis.
app.py	Flask application to set up weather data API routes.

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Features

1. Exploratory Data Analysis:

   • Analyze weather data (precipitation, temperatures) in Hawaii.
   • Visualize trends using Matplotlib.

2. Database Interaction:

   • Use SQLAlchemy to query the SQLite database for climate data.
   • Perform data retrievals for weather stations and measurements.

3. Flask Weather API:

   • Create API routes to query weather information:
     • /api/v1.0/precipitation - Precipitation data.
     • /api/v1.0/stations - List of weather stations.
     • /api/v1.0/tobs - Temperature observations for the most active station.
     • /api/v1.0/<start> and /api/v1.0/<start>/<end> - Min, average, and max temperatures for a date range.

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Installation

1. Prerequisites:

   • Python 3.x
   • SQLite
   • Flask
   • Jupyter Notebook

2. Setup:

   • Clone this repository or download the project files.

   • Install dependencies (if needed):

     pip install -r requirements.txt

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Results

Key Insights:

1. Precipitation Trends:
   • Analysis shows seasonal precipitation patterns in Hawaii.
2. Temperature Observations:
   • Temperature observations indicate consistent warmth, with peak observations in the most active weather station.

Example Outputs:

• Visualization: Line charts for precipitation trends over time.
• Flask API: JSON responses with climate data for specific routes.