#### 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 to vector unit length

When scaling to vector unit length, we scale individual samples or observations so that the transformed vector has a length of 1, or in other words, a norm of 1. Note that this scaling technique scales each individual observation and not each individual variable. To be clear, in all the scaling methods that we discussed so far in the chapter, the algorithms learned some parameters from each variable and then used those parameters to shift or rescale the distribution of the variables. On the contrary, when we scale to the unit length, we seek to normalize each observation individually, contemplating their values across all features.

Scaling to the unit norm is achieved by dividing each observation vector by either the Manhattan distance (l1 norm) or the Euclidean distance (l2 norm) of the vector. The Manhattan distance is given by the sum of the absolute components of the vector:

The Euclidean distance is given by the square...