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


In our second application chapter, we learned how to simulate events in Python and got additional exposure to writing packages. We also saw how to write Python scripts that can be run from the command line, which we used to run our simulation of the login attempt data. Then, we performed some EDA on the simulated data to see whether we could figure out what would make hacker activity easy to spot.

This led us to zero in on the number of distinct usernames attempting to authenticate per IP address per hour, as well as the number of attempts and failure rates. Using these metrics, we were able to create a scatter plot, which appeared to show two distinct groups of points, along with some other points connecting the two groups; naturally, these represented the groups of valid users and the nefarious ones, with some of the hackers not being as obvious as others.

Finally, we set about creating rules that would flag the hacker IP addresses for their suspicious activity. First...