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 materials

The materials for this chapter can be found on GitHub at There are five notebooks that we will work through, each numbered according to when they will be used, and two directories, data/ and exercises/, which contain all the CSV files necessary for the aforementioned notebooks and end-of-chapter exercises, respectively. The following files are in the data/ directory:

Figure 3.1 – Breakdown of the datasets used in this chapter

We will begin in the 1-wide_vs_long.ipynb notebook by discussing wide versus long format data. Then, we will collect daily temperature data from the NCEI API, which can be found at, in the 2-using_the_weather_api.ipynb notebook. The documentation for the Global Historical Climatology Network – Daily (GHCND) dataset we will be using can be found at https://www1.ncdc...