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

Chapter materials

In this chapter, we will be working with three datasets. The first two come from data on wine quality donated to the UCI Machine Learning Data Repository ( by P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, and contain information on the chemical properties of various wine samples along with a rating of the quality from a blind tasting session by a panel of wine experts. These files can be found in the data/ folder inside this chapter's folder in the GitHub repository ( as winequality-red.csv and winequality-white.csv for red and white wine, respectively.

Our third dataset was collected using the Open Exoplanet Catalogue database, at, which provides data in XML format. The parsed planet data can be found in the data/planets.csv file. For the exercises...