#### 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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# Visualizing outliers with boxplots

In this recipe, we will identify outliers using boxplots. Boxplots produce a box that encloses the observations within the 75th and 25th quantiles, or in other words, within the Inter-Quartile Range (IQR). The IQR is given through the following equation:

According to the IQR proximity rule, a value is an outlier if it falls outside the following boundaries:

In a boxplot, these boundaries are indicated by their whiskers. Thus, values outside the whiskers are considered outliers. Outliers are highlighted with asterisks.

## How to do it...

We will create the boxplots utilizing the `Seaborn` library. Let’s begin the recipe by importing the Python libraries and loading the dataset as follows:

1. Import the required Python libraries as follows:
```import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="darkgrid")
from sklearn.datasets import fetch_california_housing...```