Use libraries like Pandas for data manipulation, NumPy for numerical operations, and Dask for parallel processing of large datasets. Consider chunking data to manage memory usage.

Managing large datasets in Python requires careful consideration of memory usage and performance. Libraries like Pandas provide powerful data manipulation capabilities and can handle reasonably large datasets efficiently. However, for datasets that exceed memory limits, consider using Dask, which allows for parallel processing of data in chunks. Dask's DataFrame and Array structures mimic Pandas and NumPy, respectively, but are designed to operate on data too large to fit into memory. When working with large files, use chunking techniques to load and process data in manageable pieces, which can reduce memory consumption significantly. Additionally, explore data formats like HDF5 or Parquet that allow for efficient storage and retrieval of large datasets. By employing these strategies, you can work effectively with large datasets in Python while maintaining performance and memory efficiency.