#### Overview of this book

Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.
Preface
Chapter 3: Transforming Numerical Variables
Chapter 4: Performing Variable Discretization
Chapter 5: Working with Outliers
Chapter 6: Extracting Features from Date and Time Variables
Chapter 7: Performing Feature Scaling
Chapter 8: Creating New Features
Chapter 9: Extracting Features from Relational Data with Featuretools
Chapter 10: Creating Features from a Time Series with tsfresh
Chapter 11: Extracting Features from Text Variables
Index
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# Transforming variables with the reciprocal function

The reciprocal function is defined as 1/x. The reciprocal transformation is often useful when we have ratios – that is, values resulting from the division of two variables. Examples of this are population density – that is, people per area – and, as we will see in this recipe, house occupancy – that is, the number of occupants per house.

The reciprocal transformation is not defined for the value 0, and although defined for negative values, it is mainly useful for transforming positive variables.

In this recipe, we will implement the reciprocal transformation using NumPy, scikit-learn, and Feature-engine, and compare its effect on variable distribution using histograms and a Q-Q plot.

## How to do it...

Let’s begin by importing the libraries and getting the dataset ready:

1. Import the required Python libraries and data:
```import numpy as np
import pandas as pd
import matplotlib.pyplot...```