Technical requirements

We extensively use the Pandas library, a popular and useful Python library for working with DataFrames and series. Pandas offers numerous functions to analyze, summarize, explore, normalize, and manipulate them. Series are one-dimensional array-like objects, and DataFrames are two-dimensional table structures with rows and columns. We use Pandas throughout this book’s exercises to perform the aforementioned activities.

If you haven’t installed Pandas yet, you can do so with the following code snippet:

pip install pandas.

The documentation for Pandas is available at https://pandas.pydata.org/docs/.

For data visualization, we use the Matplotlib and Seaborn libraries. Matplotlib provides a wide range of tools and control over the images we build. Seaborn is built on top of Matplotlib and is more user-friendly but has less flexibility.

The documentation for both libraries can be found at https://seaborn.pydata.org/ and https://matplotlib.org/, respectively.

You can find all the data and code files for this chapter in the book’s GitHub repository at https://github.com/PacktPublishing/Data-Science-for-Web3/tree/main/Chapter06. We recommend that you read through the code files in the Chapter06 folder to follow along.