  Python data science libraries such as Pandas and NumPy have recently gained
immense popularity. Although these libraries are feature-rich and easy to use,
their scalability limitations require more robust computational resources. In
this paper, we present PyTond, an efficient approach to push the processing of
data science workloads down into the database engines that are already known
for their big data handling capabilities. Compared to the previous work, by
introducing TondIR, our approach can capture a more comprehensive set of
workloads and data layouts. Moreover, by doing IR-level optimizations, we
generate better SQL code that improves the query processing by the underlying
database engine. Our evaluation results show promising performance improvement
compared to Python and other alternatives for diverse data science workloads.
