CODEWITHSUNDEEP
pythonmatplotlibseabornheatmapcolorbar
fulatoro
fulatoro

Reputation: 755

Seaborn Heatmap with logarithmic-scale colorbar

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)

Upvotes: 70

Views: 61732

Answers (4)

cphlewis
cphlewis

Reputation: 16259

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())

Heatmap with lognorm colorbar, four tick labels

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'})

Heatmap with lognorm colorbar, four tick labels with inconsistent numerical formatting

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')

Heatmap with lognorm colorbar, two tick labels, scientific number formatting

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.

Upvotes: 64

Tomas Giro
Tomas Giro

Reputation: 4267

Short Answer:

from matplotlib.colors import LogNorm

sns.heatmap(df, norm=LogNorm())

Upvotes: 29

Jándrë
Jándrë

Reputation: 89

Responding to cphlewis (I don't have enough reputation), I solved this problem using cbar_kws; as I saw here: seaborn clustermap: set colorbar ticks.

For example cbar_kws={"ticks":[0,1,10,1e2,1e3,1e4,1e5]}.

from matplotlib.colors import LogNorm
s=np.random.rand(20,20)
sns.heatmap(s, norm=LogNorm(s.min(),s.max()),
            cbar_kws={"ticks":[0,1,10,1e2,1e3,1e4,1e5]},
            vmin = 0.001, vmax=10000)
plt.show()

Have a nice day.

Upvotes: 5

user2084795
user2084795

Reputation: 734

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 python3

import math

import numpy as np
import seaborn as sn
from matplotlib.colors import LogNorm

data = 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}
)

heatmap rand

Upvotes: 17

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