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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

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...

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