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


In this chapter, we learned how to use pandas for the data collection portion of data analysis and to describe our data with statistics, which will be helpful when we get to the drawing conclusions phase. We learned the main data structures of the pandas library, along with some of the operations we can perform on them. Next, we learned how to create DataFrame objects from a variety of sources, including flat files and API requests. Using earthquake data, we discussed how to summarize our data and calculate statistics from it. Subsequently, we addressed how to take subsets of data via selection, slicing, indexing, and filtering. Finally, we practiced adding and removing both columns and rows from our dataframe.

These tasks also form the backbone of our pandas workflow and the foundation for the new topics we will cover in the next few chapters on data wrangling, aggregation, and data visualization. Be sure to complete the exercises provided in the next section before moving...