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

Activity 7.01 – data analysis using pivot tables

In this activity, you will build pivot tables in order to perform data analysis. We will work on the Student Performance dataset from the GitHub repository.

Note

More details about the Student Performance dataset can be found at https://archive.ics.uci.edu/ml/datasets/Student+Performance.

Your tasks will be to do the following:

  1. Open a Jupyter notebook.
  2. Import the pandas package.
  3. Load the CSV file (using the ; delimiter to separate the columns) as a DataFrame.
  4. Modify the DataFrame to contain only these columns: school, sex, age, address, heath, absences, G1, G2, and G3.
  5. Display the first 10 rows of the DataFrame.
  6. Build a pivot table that is indexed on school.
  7. Build a pivot table that is indexed on school and age.
  8. Build a pivot table that is indexed on school, sex, and age, with the mean and sum aggregation on the absences column.

The expected output is as follows:

...