Fetch Live Weather Data Using Flask

Before we dive into the implementation, let’s understand a few important concepts:

1. Data Source: The data source refers to the provider or service that offers real-time data. It can be an API (Application Programming Interface), a database, a streaming service, or any other source that provides live data.
2. Python Libraries: We will utilize various Python libraries to fetch and process live data. Common libraries for handling web requests and APIs include requests and urllib. Data manipulation and analysis pandas can be quite useful.
3. Data Parsing: After fetching the data, we need to parse and extract relevant information from it. Python provides built-in capabilities for parsing JSON or XML data using libraries such as json or xml.etree.ElementTree.
4. Website Content Updates: To update the website content with live data, we can employ web frameworks or template engines. Popular frameworks like Flask or Django enable us to dynamically generate web pages. For static websites, we can use a templating engine like Jinja2 to render data into HTML templates.
5. Automation: To ensure regular updates, we need to automate the process. This involves scheduling or triggering our Python script at regular intervals using tools like cron jobs on Unix-like systems or task schedulers on Windows. Alternatively, we can deploy the script on cloud platforms like AWS Lambda or Heroku.

File Structure

Automating the process of fetching and displaying live data ensures that your website remains up-to-date with the latest information from a data source. We will cover the concepts related to this topic, provide examples with proper output screenshots, and outline the necessary steps to achieve automation.