How do NumPy and Pandas handle memory management differently?

How do NumPy and Pandas handle memory management differently?

NumPy uses contiguous blocks of memory for its arrays, which makes it more efficient for numerical operations. Pandas, on the other hand, is built on top of NumPy and uses a more complex data structure that can handle different data types, which can lead to higher memory usage.

Can you explain why NumPy is faster in certain operations?

NumPy is faster because it operates on homogeneous data types and uses optimized C libraries for computations, allowing for better cache utilization and reduced overhead.

What are some memory management techniques I can use with Pandas?

You can use techniques like downcasting data types, using categorical data for strings, and filtering data to reduce memory usage in Pandas.

Are there any tools to monitor memory usage in Python?

Yes, you can use tools like memory_profiler, objgraph, and tracemalloc to monitor memory usage in Python applications.