This project focuses on API integration and data visualization using the Python programming language. The main objective of this task is to understand how to fetch real-time data from an external source and represent it in a graphical format for better understanding and analysis. In this project, weather data is collected using the API provided by OpenWeatherMap, which allows users to access live weather information such as temperature, humidity, and weather conditions of different cities. To perform this task, I used Python along with important libraries like requests and matplotlib. The requests library is used to send HTTP requests to the API and retrieve data in JSON format, while matplotlib is used to create visual representations such as bar graphs. For writing and executing the code, I used Visual Studio Code, which is a powerful and user-friendly code editor that supports Python programming. Additionally, I used GitHub to store and manage my project files, which also helps in version control and sharing the project with others.
The working of this project is divided into two main parts. In the first part, weather data of a single city, such as Mumbai, is fetched using the API key provided by OpenWeatherMap. The program sends a request to the API and receives a response in JSON format. From this response, specific data such as temperature, humidity, and weather description are extracted and displayed. A bar graph is then created using matplotlib to visually represent temperature and humidity, making the data easier to understand. In the second part, the project is extended to handle multiple cities such as Mumbai, Delhi, Chennai, and Kolkata. A loop is used to fetch weather data for each city, and the temperature values are stored in a list. These values are then plotted on a bar graph to compare the temperatures of different cities. This helps in analyzing and comparing weather conditions across multiple locations in a simple and effective way.
This project has several real-world applications. It can be used in weather monitoring systems, data analysis dashboards, and smart city applications where real-time environmental data is important. It is also useful for learning purposes, especially for beginners who want to understand how APIs work and how data visualization is performed using Python. The use of graphs improves clarity and makes the information more visually appealing. The README file created for this project plays an important role as it provides a complete description of the project, including the objective, tools used, working process, and applications. It helps others understand the project easily without reading the entire code. Overall, this task helped in developing practical skills in API integration, data handling, and visualization, which are essential in many real-world software and data science applications.
