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Hands-On Data Analysis with Pandas

You're reading from   Hands-On Data Analysis with Pandas Efficiently perform data collection, wrangling, analysis, and visualization using Python

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Product type Paperback
Published in Jul 2019
Publisher
ISBN-13 9781789615326
Length 740 pages
Edition 1st Edition
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Author (1):
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Stefanie Molin Stefanie Molin
Author Profile Icon Stefanie Molin
Stefanie Molin
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Getting Started with Pandas FREE CHAPTER
2. Introduction to Data Analysis 3. Working with Pandas DataFrames 4. Section 2: Using Pandas for Data Analysis
5. Data Wrangling with Pandas 6. Aggregating Pandas DataFrames 7. Visualizing Data with Pandas and Matplotlib 8. Plotting with Seaborn and Customization Techniques 9. Section 3: Applications - Real-World Analyses Using Pandas
10. Financial Analysis - Bitcoin and the Stock Market 11. Rule-Based Anomaly Detection 12. Section 4: Introduction to Machine Learning with Scikit-Learn
13. Getting Started with Machine Learning in Python 14. Making Better Predictions - Optimizing Models 15. Machine Learning Anomaly Detection 16. Section 5: Additional Resources
17. The Road Ahead 18. Solutions
19. Other Books You May Enjoy Appendix

Modeling performance

The goal of this section is to give you a taste of how to build some models; as such, the following examples are not meant to be the best possible model, but rather a simple and relatively quick implementation for learning purposes. Once again, the stock_analysis package has a class for this section's task: StockModeler.

Note that to fully understand the statistical elements of this section and modeling in general, we need a solid understanding of statistics; however, the purpose of this discussion is to show how modeling techniques can be applied to financial data without dwelling on the underlying mathematics.

The StockModeler class

The StockModeler class will make it easier for us to build and...

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