How are NumPy arrays better than Python’s lists?

1.	How are NumPy arrays better than Python’s lists?
Answer» Python lists support storing heterogeneous data types whereas NumPy arrays can store datatypes of one nature itself. NumPy provides extra functional capabilities that make operating on its arrays easier which makes NumPy array advantageous in comparison to Python lists as those functions cannot be operated on heterogeneous data. NumPy arrays are treated as objects which results in minimal memory usage. Since Python keeps track of objects by creating or deleting them based on the requirements, NumPy objects are also treated the same way. This results in lesser memory wastage. NumPy arrays support multi-dimensional arrays. NumPy provides various powerful and efficient functions for complex computations on the arrays. NumPy also provides various range of functions for BitWise Operations, String Operations, Linear Algebraic operations, Arithmetic operations etc. These are not provided on Python’s default lists.

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