How do NumPy and Pandas compare in terms of memory usage?

How do NumPy and Pandas compare in terms of memory usage?

NumPy is generally more memory efficient than Pandas for numerical data due to its fixed-size data types. Pandas, while more flexible, can consume more memory because it stores data in a more complex structure.

Can you give an example of when to use NumPy over Pandas?

Sure! If you're performing heavy numerical computations on large arrays, NumPy is preferable due to its lower memory overhead and faster performance.

What about the performance differences in data manipulation?

Pandas is optimized for data manipulation and analysis, making it faster for operations like filtering and grouping. NumPy excels in numerical computations but may require more code for similar tasks.

Are there any scenarios where Pandas might be more efficient?

Yes, if you're working with heterogeneous data types or need to perform complex data manipulations, Pandas can be more efficient despite its higher memory usage.