There are many different tools for making data visualizations in Python, but some of the most common are

  • Jupyter Notebooks for running code and viewing results
  • pandas for loading and organizing data
  • matplotlib and seaborn for making data visualizations

We’ll be using all of these in this course, so let’s take some time to go over each one.

Jupyter Notebook

A Jupyter Notebook is a web-based environment for writing code and displaying results. We’ve loaded an example notebook to the right. Each Jupyter Notebook consists of a sequence of cells. There is one cell for this exercise, corresponding to the Checkpoint 1 exercise below.


Most data scientists who work in Python also use pandas, which is a library specifically designed for data analysis. Conventionally, pandas is shortened to pd when it’s imported.

matplotlib and seaborn

matplotlib and seaborn are Python libraries that are specifically designed to make data visualizations. To make data viz with matplotlib, we import just one module (called pyplot) from the library, and shorten it to plt. seaborn is conventionally imported as sns.

In this exercise, we’ll learn how to run and test some pandas code in the Jupyter notebook on the right. In the next exercise, we’ll see how matplotlib comes into play!



We’ll dig into the details of importing a dataset in the next exercise. For now, let’s practice running code in a Jupyter Notebook.

  1. Select the code cell beginning import pandas (click anywhere in the cell)
  2. Click the Run button or the Shift+Enter/Return keys (see image below)
  3. Click the Save button or use the Control/Command+S keys to save your work Screenshot of the buttons at the top of a Jupyter Notebook. The Run and Save buttons are highlighted
  4. Click the Test Work button below the Jupyter Notebook to check if you’ve completed the Checkpoint!

If you’ve successfully completed the Checkpoint, you’ll get a green check at the top of the checkpoint. When all checkpoints are complete, the Next button at the bottom right will become clickable.

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