{"id":46639,"date":"2021-04-02T00:00:00","date_gmt":"2021-04-02T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/creating-a-performance-tracker-for-car-races-collecting-race-data-part-2\/"},"modified":"2025-11-13T12:55:17","modified_gmt":"2025-11-13T20:55:17","slug":"creating-a-performance-tracker-for-car-races-collecting-race-data-part-2","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/creating-a-performance-tracker-for-car-races-collecting-race-data-part-2\/","title":{"rendered":"Creating a Performance Tracker for Car Races \u00e2\u20ac\u201c Collecting Race Data (Part 2)"},"content":{"rendered":"

Introduction<\/h2>\n

Data Analysis is the process of gaining meaningful data from raw data. Without proper analytics, any data set will be simply a bunch of information. In this article, we will learn how to transform structured data into meaningful data using data visualization methods.<\/p>\n

In the previous article<\/a>, we learned how to store data inside a database in a structured manner and also how to retrieve data from the database. This article will focus on what we can achieve through data visualization and how to obtain data from the database to match our analytics needs.<\/p>\n

Prerequisites<\/h2>\n

We will use Python as the programming language and utilize pandas, matplotlib, and folium libraries for data analysis and visualization for the purpose of this tutorial. The GridDB Python connector will be used to interact with the database.<\/p>\n

Additionally, we need to have a GridDB instance configured, and all the required collections created from the Formula 1 World Championship (1950 – 2020)<\/a> data set obtained from Kaggle<\/a>.<\/p>\n

Data Structure<\/h2>\n

Following are the collections within the GridDB instance, which we will be using for data visualization.<\/p>\n