As a data visualization developer using Matplotlib, one common requirement we face is the ability to invert or flip the orientation of the yaxis in plots.
But why would we need to invert the axis and what‘s really happening under the hood when we do so? In this comprehensive guide, we‘ll cover the nittygritty details of yaxis inversion from a developer perspective.
Why Invert the YAxis?
Before jumping into the implementation, let‘s first understand the motivation and use cases for inverting the ylabel orientation.
1. Matching Domain Conventions
In certain specialized domains like computer graphics and mathematical visualization, the standard coordinate system places the origin (0,0) at the topleft, with yaxis values increasing downwards.
Here is a sample coordinate plane:
Inverting the Matplotlib yaxis makes it match this bottomleft axis orientation convention that experts are most familiar with.
2. Improved Data Visualization
For some data distributions, inverting the yaxis can make trends and patterns much easier to observe.
Let‘s analyze weekly website traffic data visualized normally and with inverted axes:
import matplotlib.pyplot as plt
week_days = [‘Mon‘,‘Tue‘,‘Wed‘,‘Thu‘,‘Fri‘,‘Sat‘,‘Sun‘]
traffic = [400, 800, 650, 900, 1200, 1300, 800]
fig, axs = plt.subplots(1, 2)
# Normal orientation
axs[0].bar(week_days, traffic)
axs[0].set_title(‘Normal Orientation‘)
# Inverted orientation
axs[1].bar(week_days, traffic)
axs[1].invert_yaxis()
axs[1].set_title(‘Inverted Orientation‘)
plt.show()
Output:
Notice how the traffic peaks on Friday and Saturday become more apparent in the inverted plot compared to highs and lows being mixed in the normal version.
Table 1. Daywise traffic difference between maximum and minimum values:
Day  Traffic  Difference (Normal)  Difference (Inverted) 

Monday  400  500  900 
Saturday  1300  500  100 
Inverting the axis places the high and low points further apart visually in this case, making the data contrasts and trends easier to spot.
3. Contextual Relevance
For domain experts fluent with inverted axes, preserving conventions they are familiar may make more contextual sense. Consider presenting wind energy analysis to meteorologists.
Flipping the axes to match domain norms can improve cognitive load for the intended audience.
4. Aesthetics
Sometimes inverting the yaxis simply improves symmetry and visual balance. Axes inversion should ultimately aid interpretation, not just aesthetics. But the two purposes often align.
Let‘s compare plots of two intersecting exponential functions:
The inverted figure on the right has cleaner perceptual symmetry. The functions intersect cleanly in the middle of the plot, whereas on the left they meet awkwardly towards the bottom due to uninverted orientation.
So inverting can help improve graphical aesthetics in data visuals when used judiciously.
These were some of the motivation scenarios. Now let‘s analyze how to implement yaxis inversion.
Invert Axis Limits by Reversing plt.ylim()
The core principle for inverting axes vertically is to reverse the limits – make the maximum value as minimum and vice versa.
The plt.ylim()
method in Matplotlib allows controlling the lower and upper vertical range:
plt.ylim(ymin, ymax)
To invert, we call it with the min and max values swapped:
fig, ax = plt.subplots()
ax.plot([1,2,3])
# Invert limits
ax.set_ylim(3, 1)
plt.show()
Here the normal plot would have set the limits to 1
and 3
. But we pass them in opposite order to plt.ylim()
to invert.
This approach also allows setting custom range while inverting:
ax.set_ylim(100, 0) # ymin=100, ymax=0
Implementation Notes

plt.ylim()
without any params returns the current vertical axis limits as a tuple:ymin, ymax = plt.ylim()

We can access and modify axes limits through the
.axes
objects too:ax = plt.gca() ax.set_ylim([ymax, ymin])
So in essence, vertical inversion happens by reversing the ymin and ymax values internally.
Simple Inversion with ax.invert_yaxis()
While plt.ylim()
works great, having to manually calculate and reverse the limits each time can get tedious.
Matplotlib provides a simpler method – ax.invert_yaxis()
– that does the limit reversal automatically:
ax.plot(x, y)
ax.invert_yaxis()
This handles inverting the axis vertically in just one line without needing to handle limits manually!
Under the hood, invert_yaxis()
just calls set_ylim()
with reversed order parameters.
The source code implementation is as follows:
def invert_yaxis(self): # AxesSubplot class
left, right = self.get_ylim()
self.set_ylim(right, left)
So ax.invert_yaxis()
provides a simple interface to flip axes by essentially swapping the ymin/ymax
values automatically.
Inverting Specific Axes on Subplots
For figures with multiple subplots, we may need to invert axes on some graphs while keeping others normal.
Matplotlib allows controlling subplot axes independently.
Let‘s analyze an example case with 3 subplots:
import matplotlib.pyplot as plt
import numpy as np
x = [1, 2, 3, 4]
y1 = x
y2 = [1, 2, 1, 2]
y3 = [3, 4, 2, 3]
fig, axs = plt.subplots(1, 3, sharey=False)
axs[0].plot(x, y1)
axs[1].plot(x, y2)
axs[2].plot(x, y3)
axs[1].invert_yaxis()
plt.show()
Here we initialize a figure containing 3 separate subplot axes using plt.subplots()
.
By passing sharey=False
, we disable sharing yaxes. This allows inverting axes independently.
We then invert just the middle subplot axs[1]
using invert_yaxis()
.
Output:
This way we can selectively flip axes on subplots plots conditionally.
Whereas if axes sharing was enabled:
fig, axs = plt.subplots(1, 3, sharey=True)
Inverting one would invert all subplot axes together!
Table 2. Axis inversion behavior with sharing enabled vs disabled:
Axes Sharing  Inversion Behavior  Control Granularity 

Enabled  Linked across subplots  Coarse 
Disabled  Independent per subplot  Finegrained 
So developers should keep axis sharing in mind for selective axes manipulation.
Now let‘s look at working with secondary axes and twin axes.
Inverting Secondary Axes on Dual Plots
Plots with dual axes have a primary lefty yaxis, along with a secondary right side one. We can invert these axes independently.
Let‘s analyze:
import matplotlib.pyplot as plt
import numpy as np
fig, ax1 = plt.subplots()
ax1.plot(x, y1)
# Create twin secondary axis
ax2 = ax1.twinx()
ax2.plot(x, y2)
# Invert only secondary
ax2.invert_yaxis()
plt.show()
Here ax1
is the main axes object controlling left yaxis. We create a secondary twin right side axes ax2
using twinx()
.
ax2
can then be inverted separately without affecting the primary ax1
axis.
Output:
This enables flexible selective inversions for dual plots.
plt.axis() for Custom Axes Limits and Ordering
The plt.ylim()
method we saw earlier is great for simple vertical flips by reversing ylimits internally.
But at times we need more finegrained control over:
 Axis value range limits
 Relative xmin, xmax, ymin, ymax ordering
The plt.axis()
method enables this by allowing fully custom axes specifications:
plt.axis([xmin, xmax, ymin, ymax])
We can pass concatenated ymin/ymax values in reverse order to invert axes.
For example:
ax.plot(x, y)
ylim = ax.get_ylim() # Get current values
plt.axis([min(x), max(x), ylim[1], ylim[0]])
This keeps xaxis unchanged but inverts the yaxis by swapping ymin/ymax
.
We can also set custom ranges:
plt.axis([0, 20, 100, 0]) # x[0,20] y[100,0]
So plt.axis()
offers more control over axis limits and ordering combinations when needed.
Although, for basic inversions the invert_yaxis()
method works great directly out of the box!
Now that we‘ve covered a variety of axes inversion approaches along with pros and cons, let‘s consolidate some key recommendations.
Comparison Summary and Best Practices
We analyzed various techniques for inverting the Matplotlib plot yaxis:
Table 3. YAxis Inversion Methods
Method  Description  Usecase Fit 

invert_yaxis() 
Auto inverts axis by reversing ylim 
General purpose, simplest option 
plt.ylim() 
Inverts by setting custom ymax/ymin order 
Need control over range limits 
plt.axis() 
Fully customize axis order and limits  Advanced finegrained control 
Based on our analysis, here are some suggested best practices:

General case – Use
ax.invert_yaxis()
for most simplicity in a majority of scenarios 
Control axis range – When specific min/max limit values need to be set, use
plt.ylim(ymin, ymax)

Individual axes inversion – Disable sharing for selective inversion on subplots/twin axes

Customization – For advanced use cases with need to configure multiple axes precisely, use
plt.axis()
These recommendations serve as a practical guide. Developers can choose suitable techniques based on the context and customization needs.
Now let‘s shift gears and analyze what‘s happening under the hood.
Relationship to Coordinate Space and Transformations
Internally, the process of axes inversion relies heavily on coordinate space transformations.
So what exactly is happening mathematically when we call methods like invert_yaxis()
?
Fundamentally, Plot axes serve to map data points onto physical 2D pixel coordinate locations on the rendered graph image.
The key transformations involved are:
Data Space ⇾ Axes Space ⇾ Display Space
Now, inversion effects the intermediate Axes Space Mapping:

The normal orientation maps higher ydata values to lower pixel coordinates, with vertical axes inverted relative to Display Space.

Calling inversion flips the Axes Space to match Display Space orientation. So now higher ydata maps to higher pixels.
This axes transformation explains how data point locations get flipped vertically on rendering.
Inversion methods essentially manipulate the mathematical transform mappings to orient axes differently relative to the final display viewport.
These inverted transforms can apply across multiple subplots and axes, or be customized independently per different axes objects.
Implementation Challenges and Limitations
While Matplotlib provides a variety of axes inversion options, some challenges and limitations exist to keep in mind:
1. Shared vs Independent Axes
Handling inversion on figures with mixed shared and unshared axes can get tricky:
fig, axs = plt.subplots(1, 2, sharex=True, sharey=False)
Here xaxis is shared while yaxis isn‘t.
Inverting shared axes would cascade across multiple subplots. Developers need to account for these cascading effects.
2. Twinned Axes
Similarly, for figures using twin axes, simply calling invert_yaxis()
will not invert secondary axes by default. We need to handle primary and secondary axes independently:
ax1 = fig.add_subplot(1,1,1)
ax2 = ax1.twinx()
ax2.invert_yaxis()
3. Log Scale Plots
Log scaled axes need to be inverted differently using set_yscale()
:
ax.set_yscale("log")
ax.invert_yaxis() # Won‘t work!
ax.set_yscale("inverted") # Need this
This is because internally log scales use different transforms.
4. Animations
For animated plots, repeated inversion can cause issues with axes limits flickering.
Developers need to analyze and tweak the internal transformations used to avoid such artifacts.
These are some examples of nuances around customizing inversion effects correctly.
Thorough testing across plot types is recommended to catch all edge cases with multiaxes mixes.
Conclusion and Next Steps
We went through an extensive developer focused tour of inverting axes in Matplotlib powered Python plots.
The key takeaways covered were:
 Techniques – Using
invert_yaxis()
,plt.ylim()
,plt.axis()
 Use cases – Improved visualization, conventions, aesthetics
 Subplots – Controlling shared vs independent axes
 Underlying transforms – Relationship to coordinate space orientations
 Challenges – Shared axes, animations, log scales
From basic usage to internals, this guide provided a 360 degree view into working with vertical plot inversions.
Some next steps for even greater custom control are:
 Overlaying multiple axis objects for selective inversion
 Custom projection registrations using
matplotlib.projections
 Extending Matplotlib with specialized scale classes
But the methods discussed here should address common realworld requirements. I hope you enjoyed the detailed tour!