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Hands-On Data Preprocessing in Python
Hands-On Data Preprocessing in Python

Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics

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Hands-On Data Preprocessing in Python

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Key benefits

  • Develop the skills to perform data cleaning, data integration, data reduction, and data transformation
  • Make the most of your raw data with powerful data transformation and massaging techniques
  • Perform thorough data cleaning, including dealing with missing values and outliers

Description

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.

Who is this book for?

This book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don’t need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.

What you will learn

  • Use Python to perform analytics functions on your data
  • Understand the role of databases and how to effectively pull data from databases
  • Perform data preprocessing steps defined by your analytics goals
  • Recognize and resolve data integration challenges
  • Identify the need for data reduction and execute it
  • Detect opportunities to improve analytics with data transformation

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 21, 2022
Length: 602 pages
Edition : 1st
Language : English
ISBN-13 : 9781801079952
Vendor :
Apache
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Product Details

Publication date : Jan 21, 2022
Length: 602 pages
Edition : 1st
Language : English
ISBN-13 : 9781801079952
Vendor :
Apache
Category :
Concepts :
Tools :

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Table of Contents

23 Chapters
Part 1:Technical Needs Chevron down icon Chevron up icon
Chapter 1: Review of the Core Modules of NumPy and Pandas Chevron down icon Chevron up icon
Chapter 2: Review of Another Core Module – Matplotlib Chevron down icon Chevron up icon
Chapter 3: Data – What Is It Really? Chevron down icon Chevron up icon
Chapter 4: Databases Chevron down icon Chevron up icon
Part 2: Analytic Goals Chevron down icon Chevron up icon
Chapter 5: Data Visualization Chevron down icon Chevron up icon
Chapter 6: Prediction Chevron down icon Chevron up icon
Chapter 7: Classification Chevron down icon Chevron up icon
Chapter 8: Clustering Analysis Chevron down icon Chevron up icon
Part 3: The Preprocessing Chevron down icon Chevron up icon
Chapter 9: Data Cleaning Level I – Cleaning Up the Table Chevron down icon Chevron up icon
Chapter 10: Data Cleaning Level II – Unpacking, Restructuring, and Reformulating the Table Chevron down icon Chevron up icon
Chapter 11: Data Cleaning Level III – Missing Values, Outliers, and Errors Chevron down icon Chevron up icon
Chapter 12: Data Fusion and Data Integration Chevron down icon Chevron up icon
Chapter 13: Data Reduction Chevron down icon Chevron up icon
Chapter 14: Data Transformation and Massaging Chevron down icon Chevron up icon
Part 4: Case Studies Chevron down icon Chevron up icon
Chapter 15: Case Study 1 – Mental Health in Tech Chevron down icon Chevron up icon
Chapter 16: Case Study 2 – Predicting COVID-19 Hospitalizations Chevron down icon Chevron up icon
Chapter 17: Case Study 3: United States Counties Clustering Analysis Chevron down icon Chevron up icon
Chapter 18: Summary, Practice Case Studies, and Conclusions Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rodrigo Oct 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Its really practical and its explanations are quite clear. I like that this uses different techniques that could be applied to real problems.
Subscriber review Packt
Machiel Kruger Feb 22, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Young Apr 04, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Totally worth reading. There are lots of example code and charts within the book which make every concept hands-on.
Amazon Verified review Amazon
Javad Seif Mar 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In almost every problem-solving subject in schools, the data and all the probabilities are "given." In the real world, however, you need to process whatever data that is available to you, *before* you begin solving a problem. You need to prepare those givens. That is indeed the gap between academia and industry. I highly recommend this book by Dr. Roy Jafari, which addresses this very problem. It gives the reader all the necessary background, introduces the highly important aspects of ML and walks the reader through various scenarios for data preprocessing. It is really great that Python is used as the programming language because not only Python is probably the easiest language to follow, but also this book serves as a great resource for improving the reader's programming skills. I have just started reading this book but I have fully examined its outline and sampled various parts of it. I will edit this review with more detailed descriptions once I finish reading it.
Amazon Verified review Amazon
Heather Feb 12, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I found this book to be an all encompassing guide on how to properly sort, preprocess, prepare, and use your datasets. Whether you are new to python and data analytics or a seasoned professional, I believe this book has value for any skillset. The level of detail in each chapter really aides in a wholistic understanding of each topic that takes the reader's learning further than just memorization, allowing the reader to have a complete understanding of the "why" behind each concept in the book. In particular, I found it extremely helpful that nearly half the book focused on data preprocessing methods when other textbooks would have covered this in too few of pages. Data preprocessing is truly the most important step in any analysis and this book definitely highlights the importance of the strong foundation.My favorite part of the book were the hands on exercises at the end of each chapter and case studies at the end of the book. I am a hands on learner, and these really helped me test each concept and fully grow as a data enthusiast. I highly recommend this book as an addition to anyone's library!
Amazon Verified review Amazon
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