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

Dealing with messy data

Messy data can occur for a wide variety of reasons. For example, there are various forms of missing data, such as N/A, NA, None, Null, or any arbitrary number (in other words, -1, 999, 10,000, and more). It is important for analysts to understand the business meaning of the dataset they are handling during the data preparation process. By knowing the nature of missing values, the way that missing values are shown, and the data collection procedures that have triggered the occurrence of missing values, they can choose the best way to interpret this type of data.

Working on data without column headers

Often, the column headers in your data hold the preliminary information and business meaning. However, there is a chance that the column headers will be absent. This results in no specific information that can be derived to help understand the relationship between the headers and the content of the data.

Let's start with the example that we previously...