Maserati Charts 🏁
A time series chart making use of Grafana, a useful dashboarding tool for MLOps Engineers.
Two years ago I extraced some raw data from a wikipedia link using google sheets, which contained the displacement and power output for a range of Mesarati automomobile models, manufactured from 1946 till 2022.
In the past, Maserati competed in F1 as “Officine Alfieri Maserati”, they retired in the 1950s due to financial issues but may still return to the Sport. The data used here is that of their sporty commercial automobiles
I prepared the data for visualisation by seperating metrics from values leveraging the “split-to-columns” option from the data tab on googlesheets. The displacement is in cubic metres and power is given in brake horse power (bhp). The values are formatted as numbers and then downloaded to a root repo.
By looking closely, some things can be observed from the video above. You may have to pause it periodically to observe better. The first dashboard that comes up shows the power of each car, in yellow, and its corresponding displacement in green. The Maserati MC20 Cielo, MC12 and the Grecale Trofeo stands out with the highest power output with 621bhp. An upward trend in power exerted is seen over the years. The displacement on the other hand is sometimes random and somtimes consistent. It could be seen that the team at Maserati were able to increase power ouput numerous times whilst keeping the size of the engine constant or even smaller. This must have also helped in minimizing the overall car size making it quicker
It is a paramount to observe trends as an engineer, hence the data is simply wasted.
Displacement is habitually used as an expression of an engine’s size, and by extension, a loose indicator of the power an engine is capable of producing as well as the volume of fuel it is expected to consume. The brake horse power on the other hand indicates the useable power after taking account losses due to friction and other factors.
Displacement might not necessarily determine total power output
To get started on a similar project:
Install grafana (“brew install grafana” for macs)'.
Start the service (“brew services start grafana”).
Install Sqlalchemy to create an engine to upload the data to PostgreSql using a script (python in my case).
Select a new data source on Grafana, select PostgreSql, then input the name of the database, usernane and password.
On the Grafana dashboard, adjust the time and date range on the top right corner to the range within the dataset.