#### 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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# Scaling with the median and quantiles

When scaling variables to the median and quantiles, the median value is removed from the observations, and the result is divided by the Inter-Quartile Range (IQR). The IQR is the difference between the 1st quartile and the 3rd quartile, or, in other words, the difference between the 25th quantile and the 75th quantile:

This method is known as robust scaling because it produces more robust estimates for the center and value range of the variable and is recommended if the data contains outliers. In this recipe, we will implement scaling with the median and IQR by utilizing scikit-learn.

## How to do it...

To begin, we will import the required packages, load the dataset, and prepare the train and test sets:

1. Import pandas and the required scikit-learn classes and functions:
```import pandas as pd
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn...```