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

Exercises

  1. In your own words, answer the following two questions. Use 200 words (at most) to answer each question:

    a) What is the difference between classification and prediction?

    b) What is the difference between classification and clustering?

  2. Consider Figure 8.6 regarding the necessity of normalization before performing clustering analysis. With your new appreciation for this process, would you like to change your answer to the first exercise question from the previous chapter?
  3. In this chapter, we used WH Report_preprocessed.csv to form meaningful clusters of countries using 2019 data. In this exercise, we want to use the data from 2010-2019. Perform the following steps to do this:

    a) Use the .pivot() function to restructure the data so that each combination of the year and happiness index has a column. In other words, the data of the year is recorded in long format, and we would like to change that into wide format. Name the resulting data pvt_df. We will not need the Population...