pandas 带有对数刻度颜色条的 Seaborn 热图
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Seaborn Heatmap with logarithmic-scale colorbar
提问by fulatoro
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
有没有办法设置颜色条比例以登录seaborn热图？
我正在使用 Pandas 的数据透视表输出作为调用的输入
sns.heatmap(df_pivot_mirror,annot=False,xticklabels=256,yticklabels=128,cmap=plt.cm.YlOrRd_r)
Thank you.
谢谢你。
回答by cphlewis
Yes, but seaborn has hard-coded a linear tick locator for the colorbar, so the result might not be quite what you want:
是的，但是 seaborn 已经为颜色条硬编码了一个线性刻度定位器，所以结果可能不是你想要的：
# http://matplotlib.org/examples/pylab_examples/pcolor_log.html
# modified to use seaborn
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import numpy as np
from matplotlib.mlab import bivariate_normal
import seaborn as sns; sns.set()
N = 20
X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
# A low hump with a spike coming out of the top right.
# Needs to have z/colour axis on a log scale so we see both hump and spike.
# linear scale only shows the spike.
Z1 = bivariate_normal(X, Y, 0.1, 0.2, 1.0, 1.0) + 0.1 * bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
fig, axs = plt.subplots(ncols=2)
sns.heatmap(Z1, ax = axs[0])
sns.heatmap(Z1, ax = axs[1],
#cbar_kws={'ticks':[2,3]}, #Can't specify because seaborn does
norm=LogNorm(vmin=Z1.min(), vmax=Z1.max()))
axs[0].set_title('Linear norm colorbar, seaborn')
axs[1].set_title('Log norm colorbar, seaborn')
plt.show()
See the pylab example this started with for a pylab version that automatically gets colorbar tick labels (though is otherwise not as pretty).
请参阅 pylab 示例，该示例以自动获取颜色条刻度标签的 pylab 版本开始（尽管不那么漂亮）。
You can edit the seaborn code to make it work: if you alter the plot()function in /seaborn/matrix.py (ver 0.7.0):
您可以编辑 seaborn 代码以使其工作：如果您更改plot()/seaborn/matrix.py (ver 0.7.0) 中的函数：
# Possibly add a colorbar
if self.cbar:
ticker = mpl.ticker.MaxNLocator(6)
if 'norm' in kws.keys():
if type(kws['norm']) is mpl.colors.LogNorm:
ticker = mpl.ticker.LogLocator(numticks=8)
you get:
你得到：
I'll suggest that on the seaborn github, but if you want it earlier, there it is.
我会建议在seaborn github上，但如果你更早想要它，它就在那里。
回答by user2084795
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:
您可以使用matplotlib.colors.LogNorm对颜色条上的值进行标准化。我还必须在 seaborn 中手动设置标签，最终得到以下代码：
#!/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}
)
回答by Thomas G.
Short Answer:
简答：
from matplotlib.colors import LogNorm
sns.heatmap( df, norm=LogNorm())
回答by Jándr?
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.
响应 cphlewis（我没有足够的声誉），我使用cbar_kws;解决了这个问题；正如我在这里看到的：seaborn clustermap: set colorbar ticks。
For example cbar_kws={"ticks":[0,1,10,1e2,1e3,1e4,1e5]}.
例如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.
祝你今天过得愉快。
