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

Chapter 9: Data Modeling – Preprocessing

In this chapter, you will learn two important processes used to prepare data for modeling – splitting and scaling. You will learn how to use the sklearn methods – .StandardScaler and .MinMaxScaler for scaling, and .train_test_split for splitting. You will also be introduced to the reasons behind scaling and exactly what these methods do. As part of exploring splitting and scaling, you will use sklearn LinearRegression and statsmodels to create simple linear regression models.

By the end of this chapter, you will be comfortable preparing datasets to begin modeling. The main ideas you will learn in this chapter are as follows:

  • Exploring independent and dependent variables
  • Understanding data scaling and normalization
  • Activity 9.01 – Data splitting, scaling, and modeling