If we are looking for a reliable solution to retrieving weather data on regular intervals we need a more robust solution. Most importantly, scraping data is against the usage terms of almost all web sites. When the web site changes, even in small ways, the scraping code will almost certainly need changing. Programmatic scraping of weather data can be difficult to implement and then even more difficult to maintain. Scraping weather data means we simply visit a web site and either manually or programmatically copy the data from that web page. Why shouldn’t we scrape weather data from our favorite web site? The switch between history and forecast is seamless – there is no change in the query or data format. If you query a date range in the past, you will retrieve historical data.

If you query a date range in the future, you will retrieve the weather forecast. We are going to use the Timeline Weather API so that the code within this article can be used to seamlessly retrieve forecast, history data, or both within Python. We will use the Visual Crossing Weather API because it offers completely free access that includes past weather data as well as forecast. Since historical weather data is often just as critical for data science and analytics applications, in this article we demonstrate how to load weather history data using Python. In the article How to Import Weather Data in MySQL, we demonstrated how to load weather forecast data into MySQL using a python script that was run on regular intervals.

Wunderground download historical data