What are the differences between Python collections and NumPy?

What are the main differences between Python collections and NumPy?

Python collections are built-in data types like lists, tuples, and dictionaries, while NumPy is a library for numerical computing that provides support for large, multi-dimensional arrays and matrices.

When should I use NumPy instead of collections?

Use NumPy when you need to perform mathematical operations on large datasets efficiently, as it offers better performance and functionality for numerical data.

Can you give an example of when to use a list versus a NumPy array?

Sure! Use a list for storing a collection of items where you need flexibility, like a list of names. Use a NumPy array for numerical data where you need to perform calculations, like an array of temperatures.

Are there any performance differences between them?

Yes, NumPy arrays are more memory efficient and faster for numerical operations compared to Python lists, especially with large datasets.