In the Frequency Distributions Lesson, we learned that while frequency tables can be useful, we have to look up the frequency of each unique value or class interval and at the same time compare the frequencies. This process can become time-consuming for tables with many unique values or class intervals, or when the frequency values are large and hard to compare against each other.

We can solve this problem by visualizing the data in the tables with the help of graphs. Graphs make it much easier to scan and compare frequencies, providing us with a single picture of the entire distribution of a variable.

Because they are easy to grasp and also eye-catching, graphs are a better choice over frequency tables if we need to present our findings to a non-technical audience.

In this mission, we'll learn about three kinds of graphs:

  • Bar plots
  • Pie charts
  • Histograms

By the end of the mission, we'll know how to generate the graphs ourselves, and we'll know when it makes sense to use each one. 

As you work through each concept, you’ll apply what you’ve learned from within your browser; there's no need to use your own machine to do the exercises. The Python environment inside of this course includes answer-checking to ensure you've fully mastered each concept before moving on to the next.


  • Learn why visualizing distributions is important.
  • Learn how to generate bar plots, pie charts, and histograms.
  • Learn when to use bar plots, pie charts, and histograms.

Lesson Outline

  1. Visualizing Distributions
  2. Bar Plots
  3. Proportions with Bar Chats
  4. Stacked Bar Charts
  5. Pie Charts
  6. Histograms
  7. Binning for Histograms
  8. The Statistics Behind Histograms
  9. Improving Axis Tick Marks
  10. Histograms as Modified Bar Plots
  11. Skewed Distributions
  12. Symmetrical Distributions
  13. Next Steps
  14. Takeaways