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

Exploring the history and evolution of pandas

pandas, in its basic version, was open sourced in 2009 by Wes McKinney, an MIT graduate with experience in quantitative finance. He was unhappy with the tools available at the time, so he started building a tool that was intuitive and elegant and required minimal code. pandas went on to become one of the most popular tools in the data science community, so much so that it even helped increase Python's popularity to a great extent.

One of the primary reasons for the popularity of pandas is its ability to handle different types of data. pandas is well suited for handling the following:

  • Tabular data with columns that are capable of storing different types of data (such as numerical data and text data)
  • Ordered and unordered series data (an arbitrary sequence of numbers in a list, such as [2,4,8,9,10])
  • Multi-dimensional matrix data (three-dimensional, four-dimensional, and so on)
  • Any other form of observational/statistical data (such as SQL data and R data)

Besides this, a large repertoire of intuitive and easy-to-use functions/methods makes pandas the go-to tool for data analytics. In the next section, we'll cover the components of pandas and their main applications.