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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
2. Introduction to Data Analysis FREE CHAPTER 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

Data Wrangling with Pandas

In the previous chapter, we learned about the pandas data structures, how to bring our collected data into DataFrame objects, and various ways to inspect, summarize, filter, select, and work with DataFrame objects. Now that we are well-versed in the initial data collection and inspection stage, we can begin our foray into the world of data wrangling.

As mentioned in Chapter 1, Introduction to Data Analysis, preparing data for analysis is often the largest portion of the job time-wise for those working with data, and often the least enjoyable. On the bright side, pandas is well-equipped to help with these tasks, and, by mastering the skills presented in this book, we will be able to get to the more interesting parts sooner.

It should be noted that data wrangling isn't something we do merely once in our analysis; it is highly likely that we will do...

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