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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

Showing and comparing trends

Trends can be visualized when the data objects are described by attributes that are highly related to one another. A great example of such datasets is time series data. Time series datasets have data objects that are described by time attributes and with an equal duration of time between them. For instance, the following dataset is a time series dataset that shows the daily closing prices of Amazon and Apple stocks for the first 10 trading days of 2020. In this example, you can see that all of the attributes of the dataset have a time nature and they have an equal duration of a day between them:

Figure 5.24 – Time series data example (daily stock prices of Amazon and Apple)

The best way to visualize time series data is using line plots. Figure 2.9 from Chapter 2, Review of Another Core Module – Matplotlib, is a great example of using line plots to show and compare trends.

Line plots are very popular in stock market...

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