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

Implementing rule-based anomaly detection

It's time to catch those hackers. After the EDA in the previous section, we have an idea of how we might go about this. In practice, this is much more difficult to do, as it involves many more dimensions, but we have simplified it here. We want to find the IP addresses with excessive amounts of attempts accompanied by low success rates, and those attempting to log in with more unique usernames than we would deem normal (anomalies). To do this, we will employ threshold-based rules as our first foray into anomaly detection; then, in Chapter 11, Machine Learning Anomaly Detection, we will explore a few machine learning techniques as we revisit this scenario.

Since we are interested in flagging IP addresses that are suspicious, we are going to arrange the data so that we have hourly aggregated data per IP address (if there was activity for that hour):

>>> hourly_ip_logs = log.assign(
...     failures...