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 10: Data Modeling – Modeling Basics

In this chapter, you will learn how to discover patterns in data using resampling and smoothing. The .resample(), .rolling(), and .ewm() pandas methods will be introduced and you will learn how to use them to filter out the noise and perform other useful explorations of data series. You will learn how sampling can sometimes include data from future times, which is a problem for predictive modeling, and how to address that. At the end of the chapter, you will see how a combination of scaling (introduced in Chapter 9, Data Modeling – Preprocessing), and smoothing can show interesting similarities between different data series, which might otherwise be overlooked.

By the end of this chapter, you will be skilled at applying scaling, sampling, and smoothing in a variety of ways to your data analyses.

This chapter covers the following topics:

  • Learning the modeling basics
  • Predicting future values of time series
  • ...