Getting started with NumPy

The NumPy library revolves around its multidimensional array object, numpy.ndarray. NumPy arrays are collections of elements of the same data type; this fundamental restriction allows NumPy to pack the data in a way that allows for high-performance mathematical operations.

Creating arrays

You can create NumPy arrays using the numpy.array function. It takes a list-like object (or another array) as input and, optionally, a string expressing its data type. You can interactively test array creation using an IPython shell, as follows:

    import numpy as np 
    a = np.array([0, 1, 2]) 

Every NumPy array has an associated data type that can be accessed using the dtype attribute. If we inspect the a array, we find that its  dtype is int64, which stands for 64-bit integer:

    a.dtype 
    # Result: 
    # dtype('int64') 

We may decide to convert those integer numbers to float type. To do this, we can either pass the dtype argument at array initialization or cast the array...