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Hands-On Data Preprocessing in Python

You're reading from   Hands-On Data Preprocessing in Python Learn how to effectively prepare data for successful data analytics

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072137
Length 602 pages
Edition 1st Edition
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Author (1):
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Roy Jafari Roy Jafari
Author Profile Icon Roy Jafari
Roy Jafari
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Table of Contents (24) Chapters Close

Preface 1. Part 1:Technical Needs
2. Chapter 1: Review of the Core Modules of NumPy and Pandas FREE CHAPTER 3. Chapter 2: Review of Another Core Module – Matplotlib 4. Chapter 3: Data – What Is It Really? 5. Chapter 4: Databases 6. Part 2: Analytic Goals
7. Chapter 5: Data Visualization 8. Chapter 6: Prediction 9. Chapter 7: Classification 10. Chapter 8: Clustering Analysis 11. Part 3: The Preprocessing
12. Chapter 9: Data Cleaning Level I – Cleaning Up the Table 13. Chapter 10: Data Cleaning Level II – Unpacking, Restructuring, and Reformulating the Table 14. Chapter 11: Data Cleaning Level III – Missing Values, Outliers, and Errors 15. Chapter 12: Data Fusion and Data Integration 16. Chapter 13: Data Reduction 17. Chapter 14: Data Transformation and Massaging 18. Part 4: Case Studies
19. Chapter 15: Case Study 1 – Mental Health in Tech 20. Chapter 16: Case Study 2 – Predicting COVID-19 Hospitalizations 21. Chapter 17: Case Study 3: United States Counties Clustering Analysis 22. Chapter 18: Summary, Practice Case Studies, and Conclusions 23. Other Books You May Enjoy

Example 1 – unpacking columns and reformulating the table

In this example, we will use the level I cleaned speech_df dataset to create the following bar chart. We cleaned this DataFrame in the Example 1 – unwise data collection section of Chapter 9, Data Cleaning Level I – Cleaning Up the Table. The level I cleaned speech_df database only has two columns: FileName and Content. To be able to create the following visual, we need columns such as the month of the speech and the number of times the four words (vote, tax, campaign, and economy) have been repeated in each speech. While the level I cleaned speech_df dataset contains all this information, it is somewhat buried inside the two columns.

The following is a list of the information we need and the column of speech_df that this information is stored in:

  • The month of the speech: This information is in the FileName column.
  • The number of times the words vote, tax, campaign, and economy have been repeated...
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