Book Image

Hands-On Data Analysis with Pandas

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas

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)
Free Chapter
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

Summary

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 exploratory data analysis on the simulated data to see if 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...