Book Image

The Pandas Workshop

By : Blaine Bateman, Saikat Basak, Thomas V. Joseph, William So
5 (1)
Book Image

The Pandas Workshop

5 (1)
By: Blaine Bateman, Saikat Basak, Thomas V. Joseph, William So

Overview of this book

The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects. You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you’ll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services. By the end of this data analytics book, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.
Table of Contents (21 chapters)
1
Part 1 – Introduction to pandas
6
Part 2 – Working with Data
11
Part 3 – Data Modeling
15
Part 4 – Additional Use Cases for pandas

Summary

In this chapter, you learned about the pandas methods for data indexing and selection by using the primary pandas data structure – the DataFrame. You compared the DataFrame.loc() and DataFrame.iloc() methods to access items in DataFrames by labels and integer locations, respectively. You also looked at some pandas shortcut methods, including bracket notation, dot notation, and extended indexing. Along the way, you saw how the pandas index is used behind the scenes to align data, and how that can be changed by changing or resetting the index. In addition, we showed you that in many cases, you can assign new values to a subset of data by using it on the left-hand side of an assignment statement (using the equals operator). This creates a very compact and easy-to-read coding style. We saw that an important pandas capability that involved using labels for the row or column index produced more robust code – instead of "hardcoding" the column numbers, they...