You would use Polars over Pandas when working with very large datasets that don't fit into memory or when performance is critical, as Polars is designed to be faster and use resources more efficiently. For example, if you're processing a large log file containing millions of entries for analysis, Polars can manage this more effectively than Pandas by leveraging its parallel processing capabilities.
Pandas is particularly useful when working on data analysis projects where you need extensive functionality, community resources, and integration with other libraries. For instance, if you're conducting exploratory data analysis (EDA) with visualization libraries like Matplotlib or Seaborn, Pandas provides seamless compatibility and is well-optimized for smaller datasets typical of initial explorations.