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)
1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications – Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Chapter 3: Data Wrangling with Pandas

In the previous chapter, we learned about the main pandas data structures, how to create DataFrame objects with our collected data, and various ways to inspect, summarize, filter, select, and work with DataFrame objects. Now that we are well versed in the initial data collection and inspection stage, we can begin our foray into the world of data wrangling.

As mentioned in Chapter 1, Introduction to Data Analysis, preparing data for analysis is often the largest portion of the job time-wise for those working with data, and often the least enjoyable. On the bright side, pandas is well equipped to help with these tasks, and, by mastering the skills presented in this book, we will be able to get to the more interesting parts sooner.

It should be noted that data wrangling isn't something we do merely once in our analysis; it is highly likely that we will do some data wrangling and move on to another analysis task, such as data visualization...