In this project, I created a Python-based weather application that integrates geocoding, weather data retrieval, and database management. The application is designed to track and store historical weather data for various cities and countries using SQLite, Tkinter (for GUI), and the Open-Meteo API. 🗺️📊
I started by setting up an SQLite database to store weather-related data. The database includes several tables for countries, cities, and daily weather entries. The goal was to create an efficient and scalable database to store weather data for future queries. 🗃️
I connected to the Open-Meteo API to fetch real-time weather data. Using this API, we retrieved data like temperature, precipitation, and other weather-related statistics for various cities. 🌍
The weather data fetched from the API was stored in the SQLite database for future querying. This helped maintain a historical record of weather data over time. 🌦️
Using SQL queries, I fetched weather data from the SQLite database. This data was analyzed to calculate important metrics like:
- Average Annual Temperature for cities 🌡️
- 7-Day Precipitation for specific cities 🌧️
- Average Mean Temperature by city 🌞
- Average Annual Precipitation for countries 🌍
I performed several analyses on the weather data, such as:
- Calculating average annual temperature for specific cities. ❄️
- Finding average 7-day precipitation for cities over a given period. 🌧️
- Determining average mean temperature for cities within specific date ranges. 🌡️
- Calculating average annual precipitation for countries. 🌦️
I created a GUI application using Tkinter that allows users to easily query the weather data. The interface was built to display the weather data in a user-friendly format, allowing users to:
- Fetch all countries 🌍
- Fetch all cities 🏙️
- View average annual temperature for a given city 🌡️
- Display 7-day precipitation averages 🌧️
In Phase 2, I extended the project by adding data visualization features. Using the matplotlib library, we created several types of charts, such as:
- Bar Charts 📊
- Line Charts 📈
- Pie Charts 🍰
This allowed users to visualize trends in temperature and precipitation across different cities and countries. 🔄
Throughout the project, I ensured proper error handling for various issues like:
- API Errors: Ensuring the Open-Meteo API responds correctly. 🌐
- Database Errors: Making sure the SQLite database is accessible and contains the necessary tables. 🗄️
- Missing Packages: Installing missing dependencies like requests and matplotlib using pip. ⚙️
I used the geocoder library to retrieve the latitude and longitude of cities, helping with the accurate retrieval of weather data for specific locations. 📍
I integrated SQLAlchemy for ORM-based database management, making it easier to interact with the database and handle the weather data using a more structured approach. 🖧
I created scripts for:
- Inserting weather data into the database after retrieving it from the API.
- Querying and retrieving historical weather data from the database. 🔄
I packaged all the scripts and ensured that the entire project could be easily run through terminal commands or an IDE:
python GUI.py