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The Pandas Workshop

The Pandas Workshop

By : Blaine Bateman, Saikat Basak , Thomas Joseph, William So
4.8 (16)
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The Pandas Workshop

The Pandas Workshop

4.8 (16)
By: Blaine Bateman, Saikat Basak , Thomas 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)
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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

Missing data types

While working with real-world datasets, you are bound to encounter missing data quite frequently during data analysis. Understanding how pandas displays missing data for each dtype is crucial to ensure that your data analysis is correct.

The missing alphabet soup

In the previous section, we learned about the different data types and how to convert them if needed. Here, we will learn about how to represent missing data for each data type.

We will continue with our previous example. However, this time, we will replace some values with None, as follows:

data_frame.drop(['year','month','day'], axis = 1, inplace=True)
data_frame.iloc[0,0] = None
data_frame.iloc[4,1] = None
data_frame.iloc[2,2] = None
data_frame.iloc[3,3] = None
data_frame.iloc[3,4] = None
data_frame.iloc[1,5] = None
data_frame.iloc[2,6] = None
data_frame

Upon running this snippet, you should see the following output:

Figure 4.27 –...

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