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

The materials for this chapter can be found on GitHub at We will be working with three datasets once again, all of which can be found in the data/ directory. In the fb_stock_prices_2018.csv file, we have Facebook's stock price for all trading days in 2018. This data is the OHLC data (opening, high, low, and closing price), along with the volume traded. It was gathered using the stock_analysis package, which we will build in Chapter 7, Financial Analysis – Bitcoin and the Stock Market. The stock market is closed on the weekends, so we only have data for the trading days.

The earthquakes.csv file contains earthquake data pulled from the United States Geological Survey (USGS) API ( for September 18, 2018, through October 13, 2018. For each earthquake, we have the magnitude (the mag column), the scale it was measured...