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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas

By : Stefanie Molin
4.7 (11)
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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas

4.7 (11)
By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with 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 powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able 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. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Table of Contents (21 chapters)
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1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications - Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Machine Learning Anomaly Detection

For our final application chapter, we will be revisiting anomaly detection on login attempts. Let's imagine we work for a company that launched its web application in the beginning of 2018. This web application has been collecting log events for all login attempts since it launched. We know the IP address that the attempt was made from, the result of the attempt, when it was made, and which username was entered. What we don't know is whether the attempt was made by one of our valid users or a nefarious party.

Our company has been expanding and, since data breaches seem to be in the news every day, has created an information security department to monitor the traffic. The CEO saw our rule-based approach to identifying hackers from Chapter 8, Rule-Based Anomaly Detection, and was intrigued by our initiative, but wants us to move beyond...

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