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

This book will make the link between data cleaning and preprocessing to help you design effective data analytic solutions

Key Features

• Develop the skills to perform data cleaning, data integration, data reduction, and data transformation
• Get ready to make the most of your data with powerful data transformation and massaging techniques
• Perform thorough data cleaning, such as dealing with missing values and outliers

Book Description

Data preprocessing is the first step in data visualization, data analytics, and machine learning, where data is prepared for analytics functions to get the best possible insights. Around 90% of the time spent on data analytics, data visualization, and machine learning projects is dedicated to performing data preprocessing.

This book will equip you with the optimum data preprocessing techniques from multiple perspectives. 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. This book will provide a comprehensive articulation of data preprocessing, its whys and hows, and help you identify opportunities where data analytics could lead to more effective decision making. It also demonstrates 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.

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

Who 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 will find this book useful. Basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are assumed.

Table of Contents

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