What are some common challenges faced by Python data scientists when working with large datasets?

Python Data Scientists often encounter challenges related to processing and analyzing large datasets, such as memory limitations and slow computation times. To address these, professionals typically use libraries like Pandas, Dask, or PySpark to optimize data handling and leverage parallel computing. Collaborating closely with data engineers and IT teams can also help in setting up efficient data pipelines and scalable infrastructure. Staying updated with best practices in data preprocessing and model optimization is crucial for managing these challenges effectively.