F1

Despite all the controversial moments, scandals, and the dominance of one team, I have harbored a deep affection for Formula 1 for almost a year. It is the roar of the engines, the drama on the track, and the images of legendary drivers that sparked my passion for motorsport.

In recent years, particularly in the era of hybrid engines, many races seem less spectacular and memorable compared to the classic events of past years. I became interested in studying precisely which factors influence the spectacle and success of Formula 1 race weekends.

For the data analysis, I chose the dataset «Formula 1 Dataset (1950–2021)» containing data from Formula 1 races. I found this dataset to be the most suitable. This dataset includes information on race results, driver standings, and constructor rankings from 1950 to 2021. I will be analyzing data such as the winning teams, victory frequencies, team standings, which driver won the most, which car scored the most points, and how many laps were completed during the season, among others.

For the typeface, I chose Bank Gothic Medium—it’s highly legible on charts and evokes a nostalgia for the era of Senna, Prost, and Schumacher. It’s powerful, brutal, and features square dots. It screams «speed» and «power.»

The visual style of the charts was inspired by my favorite teams and the colors of the race leaders (Red Bull, Ferrari, McLaren). The color palette is highlighted by shades of racing cars and speed.

Before working with the data, I imported all the necessary libraries:

Pandas — for data manipulation Matplotlib. pyplot — for data visualization Numpy — for mathematical operations

Loading font and data

For the first graph, I dedicated it to the top 10