Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources

Chapter 8: Rule-Based Anomaly Detection

It's time to catch some hackers trying to gain access to a website using a brute-force attack—trying to log in with a bunch of username-password combinations until they gain access. This type of attack is very noisy, so it gives us plenty of data points for anomaly detection, which is the process of looking for data generated from a process other than the one we deem to be typical activity. The hackers will be simulated and won't be as crafty as they can be in real life, but it will give us great exposure to anomaly detection.

We will be creating a package that will handle the simulation of the login attempts in order to generate the data for this chapter. Knowing how to simulate is an essential skill to have in our toolbox. Sometimes, it's difficult to solve a problem with an exact mathematical solution; however, it might be easy to define how small components of the system work. In these cases, we can model the small...