Have you ever thought of speeding up your data analysis in pandas with a compiler?

In general, a Data Scientist spends significant efforts in transforming the raw data into a more digestible format before training an AI model or creating visualizations. Traditional tools such as pandas have long been the linchpin in this process, offering powerful capabilities but not without limitations. With the pitfall of its single-core implementation and inefficient data structures, often we face performance issues when dealing with pandas for relatively larger data, but its performance is also highly impacted by the choice of APIs, their parameters and execution orders.