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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

We will be building a simulation package to generate the data for this chapter; it is on GitHub at https://github.com/stefmolin/login-attempt-simulator/tree/2nd_edition. This package was installed from GitHub when we set up our environment back in Chapter 1, Introduction to Data Analysis; however, you can follow the instructions in Chapter 7, Financial Analysis – Bitcoin and the Stock Market, to install a version of the package that you can edit.

The repository for this chapter, which can be found at https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/tree/master/ch_08, has the notebook we will use for our actual analysis (anomaly_detection.ipynb), the data files we will be working with in the logs/ folder, the data used for the simulation in the user_data/ folder, and the simulate.py file, which contains a Python script that we can run on the command line to simulate the data for the chapter.

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