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The tutorial focuses on visualizing pitstop and tyre strategies in Formula 1 races using Python libraries such as Fastf1, Pandas, and Matplotlib. It uses the 2021 Russian GP as an example and guides users through setting up the environment, collecting data, and plotting the data.
2 sie 2023 · In this article, we will learn how to analyze the data of the 2022 Bahrain Grand Prix using Python. Step 1: Data Collection. Firstly, we will use the ergast API to access the results and race...
"f1-analysis" is a GitHub repository dedicated to harnessing the power of the FastF1 library in Python for comprehensive analysis of Formula 1 data, facilitating in-depth insights into race performance and strategies.
FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry. Main Features. Access to F1 timing data, telemetry, sessions results and more. Full support for Ergast to access current and historical F1 data.
29 sty 2022 · You can play around with this by, for example, inspecting the race results by running print(race.results).
FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry. Main Features. Access to F1 timing data, telemetry, sessions results and more. Full support for Ergast to access current and historical F1 data.
24 paź 2021 · This tutorial will get you started with everything need to go analyze Formula 1 data yourself. It’ll show you through the basics of setting up your Python environment and help you to set up...
