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. Ask five colleagues or classmates to provide a definition for the term data.

    a) Record these definitions and notice the similarities among them.

    b) In your own words, define the all-encompassing definition of data put forth in this chapter.

    c) Indicate the two important aspects of the definition in b).

    d) Compare the five definitions of data from your colleagues with the all-encompassing definitions and indicate their similarities and differences.

  2. In this exercise, we are going to use covid_impact_on_airport_traffic.csv. Answer the following questions. This dataset is from Kaggle.com – use this link to see its page:

    https://www.kaggle.com/terenceshin/covid19s-impact-on-airport-traffic

    The key attribute of this dataset is PercentOfBaseline, which shows the ratio of air traffic in a specific day compared to a pre-pandemic time range (February 1 to March 15, 2020).

    a) What is the best definition of the data object for this dataset?

    b) Are there any attributes in the...