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Home > Data > Machine Learning > Python Feature Engineering Cookbook
Python Feature Engineering Cookbook
Python Feature Engineering Cookbook

Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models

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Python Feature Engineering Cookbook

Imputing Missing Data

Missing data refers to the absence of values for certain observations and is an unavoidable problem in most data sources. Scikit-learn does not support missing values as input, so we need to remove observations with missing data or transform them into permitted values. The act of replacing missing data with statistical estimates of missing values is called imputation. The goal of any imputation technique is to produce a complete dataset that can be used to train machine learning models. There are multiple imputation techniques we can apply to our data. The choice of imputation technique we use will depend on whether the data is missing at random, the number of missing values, and the machine learning model we intend to use. In this chapter, we will discuss several missing data imputation techniques.

This chapter...

Technical requirements

In this chapter, we will use the Python libraries: pandas, NumPy and scikit-learn. I recommend installing the free Anaconda Python distribution (https://www.anaconda.com/distribution/), which contains all these packages.

For details on how to install the Python Anaconda distribution, visit the Technical requirements section in Chapter 1, Foreseeing Variable Problems When Building ML Models.

We will also use the open source Python library called Feature-engine, which I created and can be installed using pip:

pip install feature-engine

To learn more about Feature-engine, visit the following sites:

Check that you have installed the right versions of the numerical Python libraries, which...

Removing observations with missing data

Complete Case Analysis (CCA), also called list-wise deletion of cases, consists of discarding those observations where the values in any of the variables are missing. CCA can be applied to categorical and numerical variables. CCA is quick and easy to implement and has the advantage that it preserves the distribution of the variables, provided the data is missing at random and only a small proportion of the data is missing. However, if data is missing across many variables, CCA may lead to the removal of a big portion of the dataset.

How to do it...

Let's begin by loading pandas and the dataset:

  1. First, we'll import the pandas library:
import pandas...

Performing mean or median imputation

Mean or median imputation consists of replacing missing values with the variable mean or median. This can only be performed in numerical variables. The mean or the median is calculated using a train set, and these values are used to impute missing data in train and test sets, as well as in future data we intend to score with the machine learning model. Therefore, we need to store these mean and median values. Scikit-learn and Feature-engine transformers learn the parameters from the train set and store these parameters for future use. So, in this recipe, we will learn how to perform mean or median imputation using the scikit-learn and Feature-engine libraries and pandas for comparison.

Use mean imputation if variables are normally distributed and median imputation otherwise. Mean and median imputation may distort the distribution of the...

Implementing mode or frequent category imputation

Mode imputation consists of replacing missing values with the mode. We normally use this procedure in categorical variables, hence the frequent category imputation name. Frequent categories are estimated using the train set and then used to impute values in train, test, and future datasets. Thus, we need to learn and store these parameters, which we can do using scikit-learn and Feature-engine's transformers; in the following recipe, we will learn how to do so.

If the percentage of missing values is high, frequent category imputation may distort the original distribution of categories.

How to do it...

To begin, let's make a few imports and prepare the data:

  1. Let&apos...

Replacing missing values with an arbitrary number

Arbitrary number imputation consists of replacing missing values with an arbitrary value. Some commonly used values include 999, 9999, or -1 for positive distributions. This method is suitable for numerical variables. A similar method for categorical variables will be discussed in the Capturing missing values in a bespoke category recipe.

When replacing missing values with an arbitrary number, we need to be careful not to select a value close to the mean or the median, or any other common value of the distribution.

Arbitrary number imputation can be used when data is not missing at random, when we are building non-linear models, and when the percentage of missing data is high. This imputation technique distorts the original variable distribution.

In this recipe, we will impute missing data by arbitrary numbers using pandas, scikit...

Capturing missing values in a bespoke category

Missing data in categorical variables can be treated as a different category, so it is common to replace missing values with the Missing string. In this recipe, we will learn how to do so using pandas, scikit-learn, and Feature-engine.

How to do it...

To proceed with the recipe, let's import the required tools and prepare the dataset:

  1. Import pandas and the required functions and classes from scikit-learn and Feature-engine:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from feature_engine.missing_data_imputers import CategoricalVariableImputer
  1. Let's load the dataset:
data = pd.read_csv('creditApprovalUCI...

Replacing missing values with a value at the end of the distribution

Replacing missing values with a value at the end of the variable distribution is equivalent to replacing them with an arbitrary value, but instead of identifying the arbitrary values manually, these values are automatically selected as those at the very end of the variable distribution. The values that are used to replace missing information are estimated using the mean plus or minus three times the standard deviation if the variable is normally distributed, or the inter-quartile range (IQR) proximity rule otherwise. According to the IQR proximity rule, missing values will be replaced with the 75th quantile + (IQR * 1.5) at the right tail or by the 25th quantile - (IQR * 1.5) at the left tail. The IQR is given by the 75th quantile - the 25th quantile.

Some users will also identify the minimum...

Implementing random sample imputation

Random sampling imputation consists of extracting random observations from the pool of available values in the variable. Random sampling imputation preserves the original distribution, which differs from the other imputation techniques we've discussed in this chapter and is suitable for numerical and categorical variables alike. In this recipe, we will implement random sample imputation with pandas and Feature-engine.

How to do it...

Let's begin by importing the required libraries and tools and preparing the dataset:

  1. Let's import pandas, the train_test_split function from scikit-learn, and RandomSampleImputer from Feature-engine:
import pandas as pd
from...

Adding a missing value indicator variable

A missing indicator is a binary variable that specifies whether a value was missing for an observation (1) or not (0). It is common practice to replace missing observations by the mean, median, or mode while flagging those missing observations with a missing indicator, thus covering two angles: if the data was missing at random, this would be contemplated by the mean, median, or mode imputation, and if it wasn't, this would be captured by the missing indicator. In this recipe, we will learn how to add missing indicators using NumPy, scikit-learn, and Feature-engine.

Getting ready

For an example of the implementation of missing indicators, along with mean imputation...

Performing multivariate imputation by chained equations

Multivariate imputation methods, as opposed to univariate imputation, use the entire set of variables to estimate the missing values. In other words, the missing values of a variable are modeled based on the other variables in the dataset. Multivariate imputation by chained equations (MICE) is a multiple imputation technique that models each variable with missing values as a function of the remaining variables and uses that estimate for imputation. MICE has the following basic steps:

  1. A simple univariate imputation is performed for every variable with missing data, for example, median imputation.
  2. One specific variable is selected, say, var_1, and the missing values are set back to missing.
  3. A model that's used to predict var_1 is built based on the remaining variables in the dataset.
  4. The missing values...

Assembling an imputation pipeline with scikit-learn

Datasets often contain a mix of numerical and categorical variables. In addition, some variables may contain a few missing data points, while others will contain quite a big proportion. The mechanisms by which data is missing may also vary among variables. Thus, we may wish to perform different imputation procedures for different variables. In this recipe, we will learn how to perform different imputation procedures for different feature subsets using scikit-learn.

How to do it...

To proceed with the recipe, let's import the required libraries and classes and prepare the dataset:

  1. Let's import pandas and the required classes from scikit-learn:
import...

Assembling an imputation pipeline with Feature-engine

Feature-engine is an open source Python library that allows us to easily implement different imputation techniques for different feature subsets. Often, our datasets contain a mix of numerical and categorical variables, with few or many missing values. Therefore, we normally perform different imputation techniques on different variables, depending on the nature of the variable and the machine learning algorithm we want to build. With Feature-engine, we can assemble multiple imputation techniques in a single step, and in this recipe, we will learn how to do this.

How to do it...

Let's begin by importing the necessary Python libraries and preparing the data:

  1. Let&apos...
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Key benefits

  • Discover solutions for feature generation, feature extraction, and feature selection
  • Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets
  • Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries

Description

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.

Who is this book for?

This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.

What you will learn

  • Simplify your feature engineering pipelines with powerful Python packages
  • Get to grips with imputing missing values
  • Encode categorical variables with a wide set of techniques
  • Extract insights from text quickly and effortlessly
  • Develop features from transactional data and time series data
  • Derive new features by combining existing variables
  • Understand how to transform, discretize, and scale your variables
  • Create informative variables from date and time
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Length: 372 pages
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Language : English
ISBN-13 : 9781789806311
Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
NumPy

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Table of Contents

12 Chapters
Foreseeing Variable Problems When Building ML Models Chevron down icon Chevron up icon
Foreseeing Variable Problems When Building ML Models
Technical requirements
Identifying numerical and categorical variables
Quantifying missing data
Determining cardinality in categorical variables
Pinpointing rare categories in categorical variables
Identifying a linear relationship
Identifying a normal distribution
Distinguishing variable distribution
Highlighting outliers
Comparing feature magnitude
Imputing Missing Data Chevron down icon Chevron up icon
Imputing Missing Data
Technical requirements
Removing observations with missing data
Performing mean or median imputation
Implementing mode or frequent category imputation
Replacing missing values with an arbitrary number
Capturing missing values in a bespoke category
Replacing missing values with a value at the end of the distribution
Implementing random sample imputation
Adding a missing value indicator variable
Performing multivariate imputation by chained equations
Assembling an imputation pipeline with scikit-learn
Assembling an imputation pipeline with Feature-engine
Encoding Categorical Variables Chevron down icon Chevron up icon
Encoding Categorical Variables
Technical requirements
Creating binary variables through one-hot encoding
Performing one-hot encoding of frequent categories
Replacing categories with ordinal numbers
Replacing categories with counts or frequency of observations
Encoding with integers in an ordered manner
Encoding with the mean of the target
Encoding with the Weight of Evidence
Grouping rare or infrequent categories
Performing binary encoding
Performing feature hashing
Transforming Numerical Variables Chevron down icon Chevron up icon
Transforming Numerical Variables
Technical requirements
Transforming variables with the logarithm
Transforming variables with the reciprocal function
Using square and cube root to transform variables
Using power transformations on numerical variables
Performing Box-Cox transformation on numerical variables
Performing Yeo-Johnson transformation on numerical variables
Performing Variable Discretization Chevron down icon Chevron up icon
Performing Variable Discretization
Technical requirements
Dividing the variable into intervals of equal width
Sorting the variable values in intervals of equal frequency
Performing discretization followed by categorical encoding
Allocating the variable values in arbitrary intervals
Performing discretization with k-means clustering
Using decision trees for discretization
Working with Outliers Chevron down icon Chevron up icon
Working with Outliers
Technical requirements
Trimming outliers from the dataset
Performing winsorization
Capping the variable at arbitrary maximum and minimum values
Performing zero-coding – capping the variable at zero
Deriving Features from Dates and Time Variables Chevron down icon Chevron up icon
Deriving Features from Dates and Time Variables
Technical requirements
Extracting date and time parts from a datetime variable
Deriving representations of the year and month
Creating representations of day and week
Extracting time parts from a time variable
Capturing the elapsed time between datetime variables
Working with time in different time zones
Performing Feature Scaling Chevron down icon Chevron up icon
Performing Feature Scaling
Technical requirements
Standardizing the features
Performing mean normalization
Scaling to the maximum and minimum values
Implementing maximum absolute scaling
Scaling with the median and quantiles
Scaling to vector unit length
Applying Mathematical Computations to Features Chevron down icon Chevron up icon
Applying Mathematical Computations to Features
Technical requirements
Combining multiple features with statistical operations
Combining pairs of features with mathematical functions
Performing polynomial expansion
Deriving new features with decision trees
Carrying out PCA
Creating Features with Transactional and Time Series Data Chevron down icon Chevron up icon
Creating Features with Transactional and Time Series Data
Technical requirements
Aggregating transactions with mathematical operations
Aggregating transactions in a time window
Determining the number of local maxima and minima
Deriving time elapsed between time-stamped events
Creating features from transactions with Featuretools
Extracting Features from Text Variables Chevron down icon Chevron up icon
Extracting Features from Text Variables
Technical requirements
Counting characters, words, and vocabulary
Estimating text complexity by counting sentences
Creating features with bag-of-words and n-grams
Implementing term frequency-inverse document frequency
Cleaning and stemming text variables
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6
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4 star 22.2%
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2 star 11.1%
1 star 22.2%
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Kevin Nov 29, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As other reviews have stated the book delivers what it says it will; Python code that generates a lot of feature-engineering. I find this book to be fantastic, and Sole's work overall, as it gives life to new feature-engineering possibilities and does it fast. Long gone are the days of writing your own custom transformers or unique time-series features. This book automates a lot of that headache and will absolutely be the first reference I go to when I need to handle a new feature. I personally hadn't dealt with tsfresh prior to reading through and it brought to life instantaneous time-series features I no longer have to write scripts for. A very happy customer on that knowledge alone! Per usual, Sole continues to advance the ML community for the betterment of all.
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Muhammad Zohaib Khan Mar 31, 2021
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I bought the kindle verison of book and in introduction the book was good to read but then the display is as in picture , vertically displayed erroneous text . I cannot continue within this impossible display of text . the author need to take care of these issues . Paper verison could be ok i guess i havent read whole book though as i am returning this version now !
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Omar Pasha Mar 26, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was exactly what I needed to know!
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P. Sebastien Dec 08, 2020
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Franchement l eau chaude serait une revolution a cote de ce livre
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Amazon Customer Nov 14, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Thorough recollection of feature transformations to tackle multiple aspects of data quality and to extract features from different data formats, like text, time series and transactions. Great resource to have at hand when in front of a new dataset.
Amazon Verified review Amazon

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About the author

Profile icon Galli
Galli
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Soledad Galli is a bestselling data science instructor, author, and open-source Python developer. As the leading instructor at Train in Data, she teaches intermediate and advanced courses in machine learning that have enrolled over 64,000 students worldwide and continue to receive positive reviews. Sole is also the developer and maintainer of the Python open-source library Feature-engine, which provides an extensive array of methods for feature engineering and selection. With extensive experience as a data scientist in finance and insurance sectors, Sole has developed and deployed machine learning models for assessing insurance claims, evaluating credit risk, and preventing fraud. She is a frequent speaker at podcasts, meetups, and webinars, sharing her expertise with the broader data science community.
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Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela