Awesome Plotly with code series (Part 7): Cropping the y-axis in bar charts.
Is there ever a good reason for starting a bar chart above zero?
Welcome to the seventh post in my “Plotly with code” series! If you missed the first one, you can check it out in the link below, or browse through my “one post to rule them all” to follow along with the entire series or other topics I have previously written about.
A short summary on why am I writing this series
My go-to tool for creating visualisations is Plotly. It’s incredibly intuitive, from layering traces to adding interactivity. However, whilst Plotly excels at functionality, it doesn’t come with a “data journalism” template that offers polished charts right out of the box.
That’s where this series comes in — I’ll be sharing how to transform Plotly’s charts into sleek, professional-grade charts that meet data journalism standards.
Intro - A half truth is a whole lie.
I don’t know if you have ever seen the Fox news bar chart pruning “scandal”. If you haven’t, take a look at the screenshot below. Fox decided to scale the y-axis in order to make it seem as if Obamacare was gaining a lot of traction over time (needless to say that a time category should be a line plot, but that is beyond the scope of this post). By breaking the “best practice” rule of “always starting your bar charts at zero”, the chart tailored to a story through a ‘half-truth’.
Just to be clear, this is not a political statement, it is bad visualisation practice statement.
Why is cropping the y-axis a bad practice? When the y-axis is cropped, the viewer loses context for the data. For example, cropping can make it unclear if the charted values are close to a "natural baseline" (like zero) or if the values themselves are relatively high or low.
What will we cover in this blog?
Having understood that you should always start a bar chart at 0, there are instances where having the full range of y-axis values (from 0 to whatever value you want to show), can actually be harmful for the story to tell. So what can we do in these cases?
Scenario 1: Where a bar chart doesn’t start at y=0.
Scenario 2: Deviation plots - where your baseline is not y=0.
Scenario 3: Where starting at y=0 would hide important differences.
As always, code and links to my GitHub repository will be provided along the way. Let’s get started!
Scenario 1. Can a bar not start at 0?
A bar in a bar chart tends to be read from bottom to top, as if it was a building. That is why, in our human minds - following the building analogy - we would expect a bar chart to begin at 0.
There are not many scenarios where a bar starts somewhere different that is not 0 and it still easily understood at a glance. I can think of 2:
Waterfall charts
Bar charts that reference elements such as geographical metrics where the baseline can be different levels - sea level, stratosphere or the earth cortex.
Let’s see some examples.
Example 1. Waterfall chart
Waterfall charts are super common in the financial world. You can see below a mock-up waterfall chart that I created with Plotly.
What are the characteristics of waterfall charts?
You can see that there are bars that are not starting at 0.
However, there is a continuous flow from bar to bar, which makes it very similar to a stacked bar chart.
In addition, for every step there is a new baseline drawn (with a black line), which kind of makes each bar as if it started from a “0 type” baseline.
Finally, the overall chart does actually start at 0. We are not breaking the rule that a bar chart should always start at 0.
Tips on how to create this plot
How to build a waterfall chart in Plotly?
Thankfully, you don’t need Plotly magic to create this chart. A
go.Waterfall()method exists for us to use!
fig = go.Figure(
go.Waterfall(
orientation="v",
measure=["relative", "relative", "relative", "relative", "total"],
x=["Sales", "Consulting", "Purchases", "Other expenses", "Profit before tax"],
textposition="outside",
text=["+60", "+80", "-40", "-20", "80"],
y=[60, 80, -40, -20, 0],
connector={"line": {"color": "rgb(63, 63, 63)"}},
)
)
fig.update_layout(
title=dict(text="Profit and loss statement 2018",
font=dict(family="Helvetica Neue", size=18),
),
showlegend=False,
margin=dict(t=100, pad=0),
height = 500,
width=600,
font=dict(family="Helvetica Neue"),
)Example 2. Geographical measurements
Now imagine that you are a geography teacher. You would like to challenge your students with the following questions: (1) What are the tallest natural structures on Earth? (2) What are the tallest mountains on Earth?
You purposely want to ask these questions as most students would answer “Mount Everest” for both. But you would like to shock your students by presenting geographical elements such as trenches and mountains that are below sea level. This is the data you collate for them to plot.
In the screenshot above, I have highlighted in green how Mauna Kea is the tallest mountain, but, it surfaces less over sea level than Mount Everest. How would you plot this data?
You can see below my take on plotting all this information in 1 chart.
What are the characteristics of this chart?
The chart does actually have a 0 line (representing sea level). In addition, I have added 2 auxiliary lines to represent the maximum values above and below this 0 line.
The bars are ordered in descending order. You can easily detect that Mount Everest is the tallest mountain above sea level.
By having bars with negative values, we make it easy for the reader to understand that there are geographical features that are below sea level.
I have paid double attention to Mauna kea. First, I added 2 data labels: where does the base start below sea level, but also, what is the total height. In addition, I included a grey box clearly mentioning that Mauna Kea is the tallest mountain.
Scenario 2: Deviation plots
Imagine a scenario where you want to compare categories, where the differences between them are small and, in addition, the values of the y-axis are big. Check the dataframe below:
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