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Hands-On Data Analysis with Pandas - Second Edition

You're reading from  Hands-On Data Analysis with Pandas - Second Edition

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Pages 788 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Stefanie Molin Stefanie Molin
Profile icon Stefanie Molin

Table of Contents (21) Chapters

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

Chapter materials

The materials for this chapter can be found on GitHub at https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/tree/master/ch_03. There are five notebooks that we will work through, each numbered according to when they will be used, and two directories, data/ and exercises/, which contain all the CSV files necessary for the aforementioned notebooks and end-of-chapter exercises, respectively. The following files are in the data/ directory:

Figure 3.1 – Breakdown of the datasets used in this chapter

We will begin in the 1-wide_vs_long.ipynb notebook by discussing wide versus long format data. Then, we will collect daily temperature data from the NCEI API, which can be found at https://www.ncdc.noaa.gov/cdo-web/webservices/v2, in the 2-using_the_weather_api.ipynb notebook. The documentation for the Global Historical Climatology Network – Daily (GHCND) dataset we will be using can be found at https://www1.ncdc...

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