{"version":"1.0","type":"rich","title":"W209 Async 11.3 with pivot_table transform","description":"This Vue-based visualization analyzes Lekagul sensor data using parallel coordinates, faceted by monitor. The code leverages Python and pandas within a Jupyter notebook: `pd.read_excel()` loads the sensor readings, then a `pivot_table` reshapes data with Monitor and Date Time as the index and Chemical as columns, enabling a parallel-coordinates plot for each monitor via Pandas's `groupby()` and loop over groups. The graphic highlights how chemical readings vary across monitors, with each monitor's parallel coordinates shown as a separate facet.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-0a2fa48c94e61840bc2306b368e243c1?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}