Book Image

Hands-On Data Preprocessing in Python

By : Roy Jafari
5 (2)
Book Image

Hands-On Data Preprocessing in Python

5 (2)
By: Roy Jafari

Overview of this book

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.
Table of Contents (24 chapters)
1
Part 1:Technical Needs
6
Part 2: Analytic Goals
11
Part 3: The Preprocessing
18
Part 4: Case Studies

Chapter 3: Data – What Is It Really?

This chapter presents a conceptual understanding of data and introduces data concepts, definitions, and theories that are essential for effective data preprocessing. First, the chapter demystifies the word "data" and presents a definition that best serves data preprocessing. Next, it puts forth the universal data structure, table, and the common language everyone uses to describe it. Then, we will talk about the four types of data values and their significance for data preprocessing. Finally, we will discuss the statistical meanings of the terms information and pattern and their significance for data preprocessing.

The following topics will be covered in this chapter:

  • What is data?
  • The most universal data structure: a table
  • Types of data values
  • Information versus pattern