如何通过 pandas 数据框在Matplotlib热图中创建预定义的颜色范围

问题描述

我有以下数据框:

import pandas as pd
Index= ['aaa', 'bbb', 'ccc', 'ddd', 'eee']
Cols = ['A', 'B', 'C', 'D']
data= [[ 1, 0.3, 2.1, 1.3],[ 2.5, 1, 1, 0.77],[ 0.0, 1, 2, 1],[ 0, 3.2, 1, 1.2],[ 10, 1, 1, 1]]
df = pd.DataFrame(data, index=Index, columns=Cols)

看起来像这样:

In [25]: df
Out[25]:
        A    B    C     D
aaa   1.0  0.3  2.1  1.30
bbb   2.5  1.0  1.0  0.77
ccc   0.0  1.0  2.0  1.00
ddd   0.0  3.2  1.0  1.20
eee  10.0  1.0  1.0  1.00

我要做的是创建具有以下条件的热图:

What I want to do is to create a heat map with the following condition:

• 值< 1:蓝色
• 值== 1:白色
• 1<值＜ 2:浅红色
• 值> = 2:深红色

理想情况下，颜色必须是渐变的.这是我失败的失败尝试:

Ideally the color would have to be in gradation.This is my failed poor attempt:

from matplotlib import colors
cmap = colors.ListedColormap(['darkblue','blue','white','pink','red'])
bounds=[-0.5, 0.5, 1.5, 2.5, 3.5]
norm = colors.BoundaryNorm(bounds, cmap.N)
heatmap = plt.pcolor(np.array(data), cmap=cmap, norm=norm)
plt.colorbar(heatmap, ticks=[0, 1, 2, 3])

会产生以下情节:

什么是正确的方法?

推荐答案

要获得渐变色，可以执行以下操作:

To get gradiated colours you can do:

import matplotlib.pyplot as plt
# Builtin colourmap "seismic" has the blue-white-red
#   scale you want
plt.pcolor(np.array(data), cmap=plt.cm.seismic, vmin=0, vmax=2)
plt.colorbar()
plt.show()

在这里，我已经设置了vmin和vmax，以使它们等距大约是1.0的白色值，我认为这意味着任何高于2.0的值都不会比这些值更暗在2.0.选择更大的范围可能会得到更好的结果范围，即使这表示比例包括负数值，例如vmin=-2, vmax=4.

Here I've set vmin and vmax so that they're equally spacedaround the white value at 1.0, unfortunately I think this meansthat any values above 2.0 don't become any darker than thoseat 2.0. You may get better results by choosing a widerrange, even if this means the scale includes negativevalues, e.g. vmin=-2, vmax=4.

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