Efficiently processing large datasets in Python often involves taking advantage of multi-core processing and leveraging libraries that provide parallel computing capabilities. For instance, using the multiprocessing module in Python allows for distributing workload across multiple cores. Moreover, Python's integration with GPU computing libraries like CUDA or PyTorch can be beneficial for computationally intensive tasks, enabling faster data processing and analysis.

Handling large datasets efficiently in Python requires careful consideration of memory management and utilizing appropriate libraries. Tools like Dask or PySpark provide distributed computing capabilities for processing data in parallel across multiple machines. Furthermore, adopting techniques such as data partitioning and using efficient algorithms for aggregation and filtering can significantly improve performance when dealing with massive datasets.

When working with large datasets in Python, it's crucial to optimize memory usage and leverage parallelization. Libraries like pandas allow for chunked processing or using distributed computing frameworks like PySpark can significantly speed up computations. Additionally, employing data compression techniques, such as using efficient file formats like Parquet or utilizing memory-mapped files, can further enhance performance.

Python offers several libraries and tools for processing and analyzing large datasets, such as pandas, NumPy, and Dask. These libraries provide efficient data structures and algorithms, allowing for tasks like filtering, aggregating, and transforming data to be performed with ease. Additionally, Python's ability to integrate with other languages and tools, such as Apache Spark or Hadoop, further expands its capabilities in big data processing.