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 missing data

You might have already encountered missing data in your studies or career while performing any kind of data analysis. Missing data is a very common issue that you will encounter in most datasets. It's extremely rare to find a "perfect" dataset. Missing data is not just a nuisance. It is a serious problem that you need to account for as it can affect your results.

What is missing data?

Before you can learn how to deal with missing data, first, you need to understand each of its three types:

  • Missing at Random (MAR): This refers to data that is missing due to other variables you have information about. For example, in a survey, if you found that some specific demographics show a tendency to not reply to a question, then the missing data is considered to be MAR. An easy way to remember this is that if you can explain why the data is missing by using other variables, but not to the value of the variable with missing values itself, then...