A Day Weather Dashboard: How I Built A Python Project for API Integration and Cloud Storage

This Python application, the 30-Day Weather Dashboard, efficiently retrieves weather data for multiple locations via the OpenWeather API and securely stores it in an AWS S3 bucket. This project showcases best practices in API interaction, cloud resource management, and secure credential handling.

Key Features:

• Real-time weather data retrieval for specified cities.
• Displays crucial weather metrics: temperature, humidity, and conditions.
• Securely saves weather information to an AWS S3 bucket.
• Robust error handling for API and cloud service interactions.

Technology Stack:

• Language: Python
• API: OpenWeather API
• Cloud: AWS S3
• Libraries: boto3 (AWS interaction), requests (HTTP requests), dotenv (environment variable management)

Project Structure:

<code>weather-dashboard/
  src/
    __init__.py
    weather_dashboard.py
  tests/
  data/
  .env
  .gitignore
  requirements.txt</code>

• src/weather_dashboard.py: Core script for data fetching and AWS S3 interaction.
• .env: Securely stores API keys and AWS bucket details.
• requirements.txt: Lists project dependencies.

Setup Guide:

Prerequisites:

• Python 3.8
• AWS CLI
• Git

Steps:

1. Clone the repository: git clone https://github.com/onlyfave/30days-weather-dashboard.git && cd 30days-weather-dashboard
2. Install dependencies: pip install -r requirements.txt
3. Configure .env with your API key and AWS bucket information (example below):

<code>OPENWEATHER_API_KEY=YOUR_OPENWEATHER_API_KEY
AWS_BUCKET_NAME=your-weather-bucket
AWS_DEFAULT_REGION=your-aws-region</code>

4. Configure AWS credentials using aws configure.
5. Run the application: python3 src/weather_dashboard.py

Visuals:

1. Application Output:

2. AWS S3 Bucket Configuration:

3. Error Handling Example:

4. AWS S3 Output:

Lessons Learned:

• AWS S3 bucket management and Python integration.
• Secure credential handling using environment variables.
• Effective API integration techniques.
• Robust error handling in distributed systems.
• Git version control best practices.

Future Improvements:

• Add weather forecasting capabilities.
• Develop a user-friendly graphical user interface (GUI).
• Integrate advanced data visualization (Matplotlib, Plotly).
• Support for more cities and internationalization.
• Automated testing (Pytest).
• CI/CD pipeline implementation.

Acknowledgements:

Thanks to the DevOps and Python communities, OpenWeather, and AWS.

Contact:

LinkedIn: Onyeneke Favour GitHub: onlyfave

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