Steam Game Data Collection and Visualization Based on Python Crawlers

With the booming development of digital game market, Steam platform, as the world's largest digital game distribution and sales platform, covers a huge amount and multi-dimensional game data. In this paper, based on Python crawler technology, we collect and organize the information of thousands of games on Steam platform, such as price, ratings, reviews, genres and tags, as well as release time. By visualizing and analyzing the game price distribution, the correlation of ratings and reviews, the characteristics of genres and popular tags, and the trend of release time and other dimensions, we reveal the price structure of the Steam game market, the pattern of user ratings, the popular genres and tags preferences, and the development dynamics of the game industry. The results of this paper not only help game developers to accurately grasp the market pricing strategy, understand the positioning and audience characteristics of games in different price ranges, but also provide consumers with more intuitive reference for their purchases, and at the same time, provide academics with empirical research cases for the digital game market.