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

Series

The Series is the other fundamental pandas data structure. You can consider a DataFrame to be an organized collection of series, where each column is, in fact, a Series. Looking at the food_cons column of the food_taste DataFrame, you can see this relationship. The following line of code calls the type() method on the food_cons column of food_taste:

type(food_taste['food_cons'])

This generates the following output:

pandas.core.series.Series

So, every DataFrame column is a pandas Series, once separated and on its own. This would also be the case if you separated a single row from a DataFrame. Recall that you can use ? in Jupyter to get the help documentation. Try to do that and look at the first part of the Series documentation. You can use the following code to get the documentation:

?pd.Series

This provides the following output (truncated for brevity):

Figure 2.26 – The first portion of the help documentation for pandas...