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

Changing DataFrame values using bracket or dot notation

Many of the methods we've discussed can be used to change the values in a DataFrame, as well as select slices or ranges. In the following screenshot, we can see the GDP data that we have been working with for 2015:

Figure 5.50 – The new GDP_2015 DataFrame

Now, suppose that as part of the economic analysis, we want to increase all the GDP values by 5,000. We can do this by selecting the GDP column using bracket notation on the left, and then doing the same and adding 5,000 on the right:

GDP_2015['GDP'] = GDP_2015['GDP'] + 5000
GDP_2015

This will produce the following output:

Figure 5.51 – The GDP_2015 DataFrame with every value in the GDP column increased by 5,000

Here, we can see the expected result – that is, all our GDP figures have been increased by 5,000. Thus, using bracket notation, we can choose where new data goes into...