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)
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 6: Plotting with Seaborn and Customization Techniques

In the previous chapter, we learned how to create many different visualizations using matplotlib and pandas on wide-format data. In this chapter, we will see how we can make visualizations from long-format data, using seaborn, and how to customize our plots to improve their interpretability. Remember that the human brain excels at finding patterns in visual representations; by making clear and meaningful data visualizations, we can help others (not to mention ourselves) understand what the data is trying to say.

Seaborn is capable of making many of the same plots we created in the previous chapter; however, it also makes quick work of long-format data, allowing us to use subsets of our data to encode additional information into our visualizations, such as facets and/or colors for different categories. We will walk through some implementations of what we did in the previous chapter that are easier (or just more aesthetically...