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 levels, tools, and purposes of data cleaning – a roadmap to chapters 9, 10, and 11

One of the most exciting moments in any data analytics project is when you have one dataset that you believe contains all the data you need to effectively meet the goals of the project. This moment comes normally in one of the following situations:

  • You are done collecting data for the analysis you have in mind.
  • You have done extensive data integration from different data sources. Data integration is a very important skillset and we will cover it in Chapter 12, Data Fusion and Data Integration.
  • The dataset is just shared with you and it contains everything that you need.

Regardless of how you got your hands on the dataset, this is an exciting moment. But beware that more often than not, you still have many steps to take before you can analyze the data. First, you need to clean the dataset.

To learn about and perform data cleaning, we need to fully understand the following...