Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics

Get your raw data cleaned up and ready for processing to design better data analytic solutions
Key Features

Develop the skills to perform data cleaning, data integration, data reduction, and data transformation
Make the most of your raw data with powerful data transformation and massaging techniques
Perform thorough data cleaning, including dealing with missing values and outliers

Book Description

Hands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects.

With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data.

You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment.

The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data.

By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.
What you will learn

Use Python to perform analytics functions on your data
Understand the role of databases and how to effectively pull data from databases
Perform data preprocessing steps defined by your analytics goals
Recognize and resolve data integration challenges
Identify the need for data reduction and execute it
Detect opportunities to improve analytics with data transformation

Who this book is for

This book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.
Table of Contents

Review of the Core Modules of NumPy and Pandas
Review of Another Core Module - Matplotlib
Data – What Is It Really?
Databases
Data Visualization
Prediction
Classification
Clustering Analysis
Data Cleaning Level I - Cleaning Up the Table
Data Cleaning Level II - Unpacking, Restructuring, and Reformulating the Table
Data Cleaning Level III- Missing Values, Outliers, and Errors
Data Fusion and Data Integration
Data Reduction
Data Transformation and Massaging
Case Study 1 - Mental Health in Tech
Case Study 2 - Predicting COVID-19 Hospitalizations
Case Study 3: United States Counties Clustering Analysis
Summary, Practice Case Studies, and Conclusions