Editing plot axes
1. Editing plot axes
In this lesson, you will learn how to adjust and format the axes of your plots. Let's get started!2. Our dataset
For this lesson, we have aggregated the penguin data by species, averaging their flipper lengths. But the column names aren't exactly presentation-ready.3. The default axis titles
Let's create a basic bar chart. We pass the correct column names to avoid an error. The plot works - but it could use more polished axis titles.4. Editing axis titles
Plotly offers shortcut methods to format elements like axes. One option is using update_xaxes() and update_yaxes() with the title_text argument. Another way is using update_layout() with a dictionary that targets layout elements - like the axis titles. For consistency, we'll stick with update_layout().5. Cleaning up our plot
Both approaches produce cleaner, more professional-looking axes titles.6. Which method to use?
So, which one should you use? The shortcut methods are quick and simple for basic updates. But update_layout() gives you complete control - letting you adjust font size, style, color, angle, and more. For a full list of styling options, check the Plotly documentation.7. Editing axes ranges
Sometimes, we want to set a specific axis range instead of using Plotly's default. In our bar chart, the values are so close together that the differences are hard to spot. To overcome this, let's set the y-axis to start at 150 and end after the maximum of all values (with a small buffer). We use the general update_layout method and edit the yaxis argument, which has a range argument. This is a list of two values, the min and max of the range.8. Our new axes ranges
We now get our bar chart with specific axes. Nice stuff.9. Data scale issues
Sometimes, instead of being very close, you may want to visualize data where the values of the categories are very different. Let's use this recent data of the top 10 countries by the number of billionaires. You can already see there is a huge gap between the top and bottom entries,10. Our scale problem
If we just plot the values as-is, the top few bars dominate, and the rest are barely visible.11. The log scale
This is where the logarithmic, or log scale, can help. It's often used to plot data with large value differences. Here's an example showing the number of Internet hosts between 1981 and 2012. Notice how the y-axis ticks increase exponentially - each is ten times the previous.12. Using log with our data
Let's improve our billionaire chart using a log scale. Plotly has convenient arguments for easily transforming the scale to a log scale. We will use the log_y argument, as our x-axis is categorical. Much better. Now we can clearly see the differences between all the countries.13. Log scale: a word of warning
Just remember - log scales can be misleading for audiences unfamiliar with them. People may think the values are closer together than they actually are. Always consider your audience when choosing how to visualize your data.14. Let's practice!
Let's practice editing the axes of some plots in Plotly!Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.