In the previous lessons in this course, we explored trends in unemployment data using line charts and creating multiple plots with Python. The unemployment data we worked with had 2 columns: `DATE` which had a monthly time stamp, and `VALUE`, which represented the unemployment rate (in percent)

Line charts were an appropriate choice for visualizing this dataset because the rows had a natural ordering to it. Each row reflected information about an event that occurred after the previous row. Changing the order of the rows would make the line chart inaccurate. The lines from one marker to the next helped emphasize the logical connection between the data points.

In this lesson, we'll learn about bar plots and scatter plots while working with movie review data that FiveThirtyEight compiled to investigate whether there was any bias to Fandango's ratings. In addition to aggregating ratings for films, Fandango is unique in that it also sells movie tickets, and so it has a direct commercial interest in showing higher ratings. After discovering that a few films that weren't good were still rated highly on Fandango, the team investigated and published their findings. Throughout this lesson, we'll work on something similar and put our own spin on their investigation.

As with every lesson at Dataquest, you'll be given an opportunity to practice each concept using our code editor with built-in answer checking to ensure that you've mastered a concept before moving on to this next.

Objectives

  • Learn how to use appropriate graphs for your data.
  • Learn how to create bar and scatter plots using Python.
  • Learn how to find hidden bias in the data.

Lesson Outline

1. Recap
2. Introduction to the data
3. Bar Plot
4. Creating Bars
5. Aligning Axis Ticks And Labels
6. Horizontal Bar Plot
7. Scatter plot
8. Switching axes
9. Benchmarking correlation
10. Next steps
11. Takeaways