NumPy and arrays

NumPy ('Numerical Python') is a Python package for data analysis and scientific computing. It provides a powerful n-dimensional array object and tools for working with it. Install it with pip install numpy, and use it with import numpy as np (np is a short alias).

What an array is. An array stores many values under one name.

• Every element has the same data type.
• Each element has an index; the first is at index 0, the second at 1, and so on: zero-based indexing, as with lists.
• The whole array is stored contiguously (side by side) in memory, which makes operations fast.
• NumPy's array class is officially ndarray, usually just called an array.

Lists versus arrays:

• A list can mix types, like [1, 3.4, 'hello', 'a@']; an array holds one type only, like an array of floats.
• List elements may be scattered in memory; array elements are contiguous.
• A list cannot do arithmetic on every element at once; an array can: if A1 is an array, A1/3 divides each element by 3 (element-wise operation). Trying the same on a list gives an error.
• Arrays take less memory, because the type is not stored with every element.

Example (new): prices = np.array([120, 80, 45]) can be given a 10 per cent discount in one step, prices * 0.9, giving [108.0, 72.0, 40.5]; with an ordinary list you would need a loop to change each price.