Tracking the ISS in real time

Streaming data refers to real-time data which is repeatedly flowing from a source to a goal. It includes audio, video, text, or numerical data that’s generated by sources resembling social media platforms, sensors, and servers. The info transmission is in a gentle stream with no fixed starting or end. Streaming data is significant in fields resembling healthcare, finance, and transportation, and it forms a key component of the Web of Things (IoT).

The flexibility to handle streaming data is a vital skill for a knowledge scientist. On this Quick Success Data Science project, we’ll use streaming data to trace the International Space Station (ISS) because it orbits the Earth. For coding we’ll use Python and Plotly Express in a Jupyter Notebook.

Telemetry is the in-situ collection and automatic transmission of distant sensor data to receiving equipment for monitoring. While there are many sources for ISS telemetry, we’ll use the WTIA REST API (WTIA stands for Where the ISS at?).

This API was written by Bill Shupp to incorporate more features than are provided by typical ISS tracking/notification sites. At the tip of the article, I’ll list some additional sources for ISS telemetry and tracking, in case you wish to try them out or compare our results to theirs.

Plotly Express is a built-in a part of the Plotly graphing library. As an easier, higher-level version of Plotly, it’s the really helpful start line for creating commonest figures.

Plotly Express comprises greater than 30 functions for creating entire figures directly, and the API for these functions was rigorously designed to be as consistent and simple to learn as possible. This makes it easy to change between figure types during a knowledge exploration session.

While Plotly Express is simple to make use of and creates beautiful, interactive plots, they’re not as customizable as plots generated in lower-level libraries like Plotly or matplotlib. As at all times, you could have to offer up some control for ease of use.