Understanding Differences between List and Numpy | Codegnan | Data Analysis Tutorial | Part-5
NumPy is extremely popular because it dramatically improves the ease and performance of working with multidimensional arrays.
A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.
The Python core library provides Lists. A list is the Python equivalent of an array, but is resizeable and can contain elements of different types.
A common beginner question is what is the real difference here. The answer is performance. Numpy data structures perform better in:
Size - Numpy data structures take up less space
Performance - they have a need for speed and are faster than lists
Functionality - SciPy and NumPy have optimized functions such as linear algebra operations built-in.
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