Chapter 2. NumPy Arrays and Vectorized Computation

NumPy is the fundamental package supported for presenting and computing data with high performance in Python. It provides some interesting features as follows:

• Extension package to Python for multidimensional arrays (ndarrays), various derived objects (such as masked arrays), matrices providing vectorization operations, and broadcasting capabilities. Vectorization can significantly increase the performance of array computations by taking advantage of Single Instruction Multiple Data (SIMD) instruction sets in modern CPUs.
• Fast and convenient operations on arrays of data, including mathematical manipulation, basic statistical operations, sorting, selecting, linear algebra, random number generation, discrete Fourier transforms, and so on.
• Efficiency tools that are closer to hardware because of integrating C/C++/Fortran code.

NumPy is a good starting package for you to get familiar with arrays and array-oriented computing in data analysis. Also, it is the basic step to learn other, more effective tools such as Pandas, which we will see in the next chapter. We will be using NumPy version 1.9.1.