Seaborn Heatmap with logarithmic-scale colorbar

Question

Is there a way to set the color bar scale to log on a seaborn heat map graph?
I am using a pivot table output from pandas as an input to the call

 sns.heatmap(df_pivot_mirror,annot=False,xticklabels=256,yticklabels=128,cmap=plt.cm.YlOrRd_r) 

Thank you.

Answer 1

If you have a current install of seaborn, norm=LogNorm() in the call to heatmap works now. (Pointed out in the comments -- thank you.) Adding this to one of the seaborn examples:

import numpy as np import seaborn as sns; sns.set_theme(style='white') import matplotlib.pyplot as plt from matplotlib.colors import LogNorm, Normalize from matplotlib.ticker import MaxNLocator  flights = sns.load_dataset("flights") flights = flights.pivot("month", "year", "passengers")   f3, ax5 = plt.subplots(1,1) sns.heatmap(flights, square=True, norm=LogNorm()) 

You can pass through colorbar arguments as keywords in the seaborn wrapper, but they sometimes collide with the seaborn choices:

sns.heatmap(flights, square=True, norm=LogNorm(), cbar_kws={'ticks':MaxNLocator(2), 'format':'%.e'}) 

For comparison, this is the matplotlib heatmap without seaborn's improvements -- the colorbar arguments have both been applied:

f5, ax6 = plt.subplots(1,1) im6 = plt.imshow(flights, norm=LogNorm()) cbar6 = ax.figure.colorbar(im6, ax=ax6, ticks=MaxNLocator(2), format='%.e') 

If you have to use an older install and LogNorm doesn't work in seaborn, see the previous versions of this answer for a workaround.