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)
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

Chapter materials

The materials for this chapter can be found at https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/tree/master/ch_11. In this chapter, we will be revisiting attempted login data; however, the simulate.py script has been updated to allow additional command-line arguments. We won't be running the simulation this time, but be sure to take a look at the script and check out the process that was followed to generate the data files and create the database for this chapter in the 0-simulating_the_data.ipynb notebook. The user_data/ directory contains the files used for this simulation, but we won't be using them directly in this chapter.

The simulated log data we will be using for this chapter can be found in the logs/ directory. The logs_2018.csv and hackers_2018.csv files are logs of login attempts and a record of hacker activity from all 2018 simulations, respectively. Files with the hackers prefix are treated as the labeled data we...