How to Scrape and Visualize 20 Years of TIOBE Language Rankings with Python

In the world of programming languages, debates about the "best" language never cease, but the TIOBE Programming Language Index provides a data‑driven way to track language popularity over time.

In this article we demonstrate how to scrape the TIOBE rankings from May 2001 to the present, clean the data, and build dynamic visualizations that show the evolution of language popularity over nearly twenty years.

01. Data Acquisition

We start by opening the TIOBE index page at https://www.tiobe.com/tiobe-index/ . The required information is embedded directly in the page’s source code, as illustrated below.

02. Data Cleaning

After fetching the HTML, we use regular expressions to extract language names and their popularity scores for each month, then store the results in a nested dictionary such as:

{"2020-01-12": {"Java": 16, "C++": 14, "Python": 10, ...}, "2020-02-13": {"Java": 16.3, "C++": 15.6, ...}, ...}

03. Setting Bar Colors

To distinguish each language in the bar chart, we assign a unique color to every bar and sort the nested dictionary chronologically.

04. Final Visualization

We clear the figure’s active axes, sort languages by their monthly scores, and map each language to its assigned color before rendering the chart.

05. Dynamic Display

The chart is displayed in a loop with a pause, creating an animated bar‑chart that updates month by month.

06. More Fancy Animations

If a more polished look is desired, the data can be rendered with JavaScript or tools like Flourish, as shown below.

Conclusion

The animated visualizations reveal that Java and C have consistently held the top two spots, while Python, driven by the AI boom, surged from the bottom to third place, widening the gap with the fourth‑ranked language. Conversely, C++ and PHP have seen declining popularity. Despite fluctuations, every language retains value, and mastering any of them can be beneficial.