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

Introducing pandas dtypes

When working with pandas, it is vital to make sure that you assign the correct data types to the values you're working with. Otherwise, you may end up getting unexpected results or errors when running certain operations or calculating aggregations. Having a good understanding of every data type in pandas will save you a lot of time and energy as you will considerably reduce the number of errors in your code.

Data types in pandas are internal labels that a programming language uses to understand how to store and manipulate data. For example, a program needs to understand that you can add two numbers together, such as 1 + 2, to get 3. Or, if you have two strings such as "data" and "frame," they can be concatenated to get "DataFrame."

Data types in pandas are called dtypes and should not be confused with Python's data type. We shall be using both data types and dtypes interchangeably throughout this chapter.

Obtaining...