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

Recap of the preprocessing steps

Unlike the previous chapters, in this chapter, we will only be reinforcing the skills that were taught in the previous chapters. This will be in the form of various exercises and an activity.

This section will help you recap some of the important preprocessing steps covered in this book so far and also go through some techniques that will be used in the exercises:

  1. Reading CSV files
    pd.read_csv('file path' , delimiter=';')

As you may recall, the pd.read_csv function is used to read the data from a CSV file available at the specified path.

  1. Recasting data

One of the most frequent transformation steps is changing the format from wide format to long format. For example, the following figure shows some data in wide format. You can see that the data for each month is spread across the columns:

Figure 14.1 – Wide format data

Often, when we have to preprocess data, we need data...