Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas - Second Edition

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 practice, detecting attackers isn't easy. Real-life hackers are much savvier than the ones in this simulation. Attacks are also much less frequent, creating a huge class imbalance. Building machine learning models that will catch everything just isn't possible. That is why it is so vital that we work with those who have domain knowledge; they can help us squeeze some extra performance out of our models by really understanding the data and its peculiarities. No matter how experienced we become with machine learning, we should never turn down help from someone who often works with the data in question.

Our initial attempts at anomaly detection were unsupervised while we waited for the labeled data from our subject matter experts. We tried LOF and isolation forest using scikit-learn. Once we received the labeled data and performance requirements from our stakeholders, we determined that the isolation forest model was better for our data.

However, we didn&apos...