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

Simulating login attempts

Since we can't easily find login attempt data from a breach (it's not typically shared due to its sensitive nature), we will be simulating it. Simulation requires a strong understanding of statistical modeling, estimating probabilities of certain events, and identifying appropriate assumptions to simplify where necessary. In order to run the simulation, we will build a Python package (login_attempt_simulator) to simulate a login process requiring a correct username and password (without any extra authentication measures, such as two-factor authentication) and a script ( that can be run on the command line, both of which we will discuss in this section.


Before we jump into the code that handles the simulation, we need to understand the assumptions. It is impossible to control for every possible variable when we make a simulation, so we must identify some simplifying assumptions to get started.

The simulator makes the...