How to rotate Seaborn heatmap in python?

Question

default settings of seaborn.heatmap gives

the x-axis starts from the origin of 0 then increases towards the right
the y-axis starts from an origin of 9 then increases towards the upward

This is odd compared to matplotlib.pyplot.pcolormesh, which gives a y-axis that starts from an origin of 0 that moves upward, like what we'd intuitively want since it only makes sense for origins to be (0,0), not (0,9)!

How to make the y-axis of heatmap also start from an origin of 0, instead of 9, moving upward? (while of course re-orienting the data correspondingly)

I tried transposing the input data, but this doesn't look right and the axes don't change. I don't think it's a flip about the y-axis that's needed, but a simple rotating of the heatmap.

vahndi · Accepted Answer · 2020-09-15 11:43:48Z

You can flip the y-axis using ax.invert_yaxis():

import seaborn as sns import numpy as np np.random.seed(0) sns.set_theme() uniform_data = np.random.rand(10, 12) ax = sns.heatmap(uniform_data) ax.invert_yaxis()

If you want to do the rotation you describe, you have to transpose the matrix first:

import seaborn as sns import numpy as np np.random.seed(0) sns.set_theme() uniform_data = np.random.rand(10, 12) ax = sns.heatmap(uniform_data.T) ax.invert_yaxis()

The reason for the difference is that they are assuming different coordinate systems. pcolormesh is assuming that you want to access the elements using cartesian coordinates i.e. [x, y] and it displays them in the way you would expect. heatmap is assuming you want to access the elements using array coordinates i.e. [row, col], so the heatmap it gives has the same layout as if you print the array to the console.

Why do they use different coordinate systems? I would be speculating but I think it's due to the ages of the 2 libraries. matplotlib, particularly its older commands is a port from Matlab, so many of the assumptions are the same. seaborn was developed for Python much later, specifically aimed at statistical visualization, and after pandas was already existent. So I would guess that mwaskom chose the layout to replicate how a DataFrame looks when you print it to the screen.

any idea why plt.pcolormesh does it properly be default and seaborn.heatmap() gives an inverted transpose of what pcolormesh does by default? Or is it the other way around and pcolormesh is doing the orientation wrong, while heatmap is right? which of the two is doing it right?
Answer updated - they're both right for different coordinate systems!

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