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

Preprocessing the data

The very first step in preprocessing for clustering analysis is to be clear about which data objects will be clustered, and that is clear here: counties. So, at the end of the data preprocessing, we will need to have a dataset whose rows are counties, and with columns based on how we want to group the counties. As shown in the following screenshot, which is a summary of the data preprocessing that we will perform during this chapter, we will get to county_df, which has the characteristics that were just described.

Figure 17.2 – Schematic of the data preprocessing

As shown in the preceding summarizing screenshot, we will first transform election_df into partisan_df, and then integrate the partisan_df, edu_df, pov_df, pop_df, and employ_df DataFrames. Of course, there will be more detail to all of these steps than the preceding screenshot shows; however, this serves as a great summary and a general map for our understanding.

Let...