The popularity of Python has skyrocketed over the years, especially after 2012; a lot of this can be attributed to the popularity of pandas. Python-related questions make up around 12% of the total questions asked from high-income countries on Stack Overflow, a popular platform for developers to ask questions and get answers from other people in the community about how to get things done and fix bugs in different programming languages. Given that there are hundreds of programming languages, one language occupying 12% of market share is an extraordinary achievement:

According to this survey conducted by Kaggle, 60% of the respondents said that they were aware of or have used Python for their data science jobs.

According to the data recorded by Stack Overflow about the types of question asked on their platform, Python and pandas have registered steady growth year on year, while some of the other programming languages, such as Java and C, have declined in popularity and are playing catch-up. Python has almost caught up with the number of questions asked about Java on the platform, while the number for Java has shown a negative trend. pandas has been showing constant growth in numbers.

The following chart is based on data gathered from the SQL API exposed by Stack Overflow. The y axis represents the number of questions asked about that topic on Stack Overflow in a particular year:

Google Trend also shows a surge in popularity for pandas, as demonstrated in the following chart. Numbers represent surge in interest for pandas relative to the highest point (historically) on the chart for the given region and time.

The geographical split of the popularity of pandas is even more interesting. The highest interest has come from China, which might be an indicator of the high adoption of open source tools and/or a very high inclination towards building powerful tech for data science:

Apart from the popularity with its users, pandas (owing to its open source origins) also has a thriving community that is committed to constantly improving it and making it easier for the users to get answers about the issues. The following chart shows the weekly modifications (additions/deletions) to the pandas source code by the contributors: