Building function compositions for data processing

One of the major parts of any machine learning system is the data processing pipeline. Before data is fed into the machine learning algorithm for training, we need to process it in different ways to make it suitable for that algorithm. Having a robust data processing pipeline goes a long way in building an accurate and scalable machine learning system. There are a lot of basic functionalities available, and data processing pipelines usually consist of a combination of these. Instead of calling these functions in a nested or loopy way, it's better to use the functional programming paradigm to build the combination. Let's take a look at how to combine these functions to form a reusable function composition. In this recipe, we will create three basic functions and look at how to compose a pipeline.

How to do it…

1. Create a new Python file, and add the following line:

   import numpy as np

2. Let's define a function to add 3 to each element of the array...