Python Feature Engineering Cookbook will help machine learning practitioners improve their data preprocessing and manipulation skills, empowering them to modify existing variables or create new features from existing data. You will learn how to implement many feature engineering techniques with multiple open source tools, streamlining and simplifying code while adhering to coding best practices. Thus, to make the most of this book, you are expected to have an understanding of machine learning and machine learning algorithms, some previous experience with data processing, and a degree of familiarity with datasets. In addition, working knowledge of Python and some familiarity with Python numerical computing libraries such as NumPy, pandas, Matplotlib, and scikit-learn will be beneficial. You are required to be experienced in the use of Python through Jupyter Notebooks, in iterative Python through a Python console or Command Prompt, or have experience using a dedicated Python IDE, such as PyCharm or Spyder.

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the Support tab.
3. Click on Code Downloads.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Python-Feature-Engineering-Cookbook. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781789806311_ColorImages.pdf.

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The nunique() method ignores missing values by default."

A block of code is set as follows:

import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

X_train['A7'] = np.where(X_train['A7'].isin(frequent_cat), X_train['A7'], 'Rare')
X_test['A7'] = np.where(X_test['A7'].isin(frequent_cat), X_test['A7'], 'Rare')

Any command-line input or output is written as follows:

$ pip install feature-engine

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Click the Download button."