Understanding NumPy as the engine behind Python data science and bioinformatics

In this recipe, we’ll learn about a critical numerical library in Python and explore it using a dataset on adverse responses to vaccines.Most of your analysis will make use of NumPy, even if you don’t use it explicitly. NumPy is an array manipulation library that is behind libraries such as pandas, matplotlib, BioPython, and scikit-learn, among many others. While much of your bioinformatics work may not require explicit direct use of NumPy, you should be aware of its existence as it underpins almost everything you do, even if only indirectly via the other libraries.You should take a moment now to learn more about arrays in Python. Arrays are sets of data that can be one or more dimensional. For example:

arr1 = [“a”, “b”, c”]

... would give you a one-dimensional array with 3 elements, the letters a, b, and c.

arr1[0]

...would give you the zeroeth element...