Seaborn Heatmap with logarithmic-scale colorbar

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 npimport seaborn as sns; sns.set_theme(style='white')import matplotlib.pyplot as pltfrom matplotlib.colors import LogNorm, Normalizefrom matplotlib.ticker import MaxNLocatorflights = 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.

Short Answer:

from matplotlib.colors import LogNormsns.heatmap(df, norm=LogNorm())

You can normalize the values on the colorbar with matplotlib.colors.LogNorm.I also had to manually set the labels in seaborn and ended up with the following code:

#!/usr/bin/env python3import mathimport numpy as npimport seaborn as snfrom matplotlib.colors import LogNormdata = np.random.rand(20, 20)log_norm = LogNorm(vmin=data.min().min(), vmax=data.max().max())cbar_ticks = [math.pow(10, i) for i in range(math.floor(math.log10(data.min().min())), 1+math.ceil(math.log10(data.max().max())))]sn.heatmap(    data,    norm=log_norm,    cbar_kws={"ticks": cbar_ticks})