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

The distinction between data reduction and data redundancy

In the previous chapter, Chapter 12, Data Fusion and Data Integration, we discussed and saw an example of the data redundancy challenge. While data redundancy and data reduction have very similar names and their terms use words that have connected meanings, the concepts are very different. Data redundancy is about having the same information presented under more than one attribute. As we saw, this can happen when we integrate data sources. However, data reduction is about reducing the size of data due to one of the following three reasons:

  • High-Dimensional Visualizations: When we have to pack more than three to five dimensions into one visual, we will reach the human limitation of comprehension.
  • Computational Cost: Datasets that are too large may require too much computation. This might be the case for algorithmic approaches.
  • Curse of Dimensionality: Some of the statistical approaches become incapable of finding...