Python数据可视化 | seaborn heatmap可视化模式亲测_协方差矩阵可视化绘图

seaborn heatmap可视化模式亲测

利用heatmap绘制协方差矩阵是数据可视化中常见的操作，而对颜色的选取则是一种艺术了。在不同的场景下有可能我们需要不同的色调或者颜色的搭配。而seaborn中的heatmap函数为我们提供了便捷。

Seaborn中有非常多的颜色选项可以选择，这里将效果一一亲测。数据如下：这里我们用最为简单的数据绘制协方差矩阵的图。

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set()
data=pd.DataFrame({'A':[1,4,5,2,5,6,3,5,6,3,3],
                 'B':[4,3,7,3,5,2,4,3,5,5,2],
                 'C':[5,8,9,3,5,7,3,5,3,4,4],
                 'D':[5,4,3,6,7,3,5,2,6,6,4],
                 'E':[4,5,7,3,6,3,2,4,5,2,1],
                 'F':[7,6,4,7,4,7,9,3,2,2,3],
                 'G':[4,5,2,5,8,9,1,2,4,4,3],
                 'H':[6,4,2,6,2,6,1,3,8,9,6],
                 'I':[4,3,5,2,7,8,3,4,2,5,3],
                 'J':[4,2,5,7,8,4,5,2,5,1,2],
                 'K':[4,5,8,2,3,1,4,5,8,3,2]})
f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax)
ax.set_title('default')
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Text(0.5, 1, 'default')
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可以看到，这就是heatmap的默认颜色模式，除了这种模式，我们可以指定cmap的值，使得绘制出来的图像呈现不同的颜色（在字符串后面加’_r’则进行反向）

1 Accent

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Accent')
ax.set_title('Accent')
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Text(0.5, 1, 'Accent')
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2 Blues

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Blues')
ax.set_title('Blues')
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Text(0.5, 1, 'Blues')
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3 BrBG

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='BrBG')
ax.set_title('BrBG')
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Text(0.5, 1, 'BrBG')
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4 BuGn

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='BuGn')
ax.set_title('BuGn')
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Text(0.5, 1, 'BuGn')
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5 BuPu

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='BuPu')
ax.set_title('BuPu')
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Text(0.5, 1, 'BuPu')
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6 CMRmap

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='CMRmap')
ax.set_title('CMRmap')
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Text(0.5, 1, 'CMRmap')
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7 Dark2

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Dark2')
ax.set_title('Dark2')
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Text(0.5, 1, 'Dark2')
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8 GnBu

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='GnBu')
ax.set_title('GnBu')
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Text(0.5, 1, 'GnBu')
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9 Greens

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Greens')
ax.set_title('Greens')
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Text(0.5, 1, 'Greens')
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10 Greys

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Greys')
ax.set_title('Greys')
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Text(0.5, 1, 'Greys')
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11 OrRd

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='OrRd')
ax.set_title('OrRd')
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Text(0.5, 1, 'OrRd')
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12 Oranges

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Oranges')
ax.set_title('Oranges')
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Text(0.5, 1, 'Oranges')
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13 PRGn

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='PRGn')
ax.set_title('PRGn')
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Text(0.5, 1, 'PRGn')
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14 Paired

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Paired')
ax.set_title('Paired')
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Text(0.5, 1, 'Paired')
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15 Pastel1

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Pastel1')
ax.set_title('Pastel1')
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Text(0.5, 1, 'Pastel1')
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16 Pastel2

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Pastel2')
ax.set_title('Pastel2')
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Text(0.5, 1, 'Pastel2')
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17 PiYG

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='PiYG')
ax.set_title('PiYG')
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Text(0.5, 1, 'PiYG')
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18 PuBu

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='PuBu')
ax.set_title('PuBu')
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Text(0.5, 1, 'PuBu')
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19 PuBuGn

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='PuBuGn')
ax.set_title('PuBuGn')
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Text(0.5, 1, 'PuBuGn')
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20 PuOr

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='PuOr')
ax.set_title('PuOr')
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Text(0.5, 1, 'PuOr')
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21 PuRd

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='PuRd')
ax.set_title('PuRd')
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Text(0.5, 1, 'PuRd')
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22 Purples

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Purples')
ax.set_title('Purples')
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Text(0.5, 1, 'Purples')
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23 RdBu

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='RdBu')
ax.set_title('RdBu')
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Text(0.5, 1, 'RdBu')
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24 RdGy

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='RdGy')
ax.set_title('RdGy')
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Text(0.5, 1, 'RdGy')
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25 RdPu

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='RdPu')
ax.set_title('RdPu')
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Text(0.5, 1, 'RdPu')
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26 RdYlBu

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='RdYlBu')
ax.set_title('RdYlBu')
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Text(0.5, 1, 'RdYlBu')
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27 RdYlGn

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='RdYlGn')
ax.set_title('RdYlGn')
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Text(0.5, 1, 'RdYlGn')
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28 Reds

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Reds')
ax.set_title('Reds')
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Text(0.5, 1, 'Reds')
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29 Set1

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Set1')
ax.set_title('Set1')
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Text(0.5, 1, 'Set1')
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30 Set2

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Set2')
ax.set_title('Set2')
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Text(0.5, 1, 'Set2')
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31 Set3

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Set3')
ax.set_title('Set3')
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Text(0.5, 1, 'Set3')
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32 Spectral

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Spectral')
ax.set_title('Spectral')
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Text(0.5, 1, 'Spectral')
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33 Wistia

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='Wistia')
ax.set_title('Wistia')
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Text(0.5, 1, 'Wistia')
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34 YlGn

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='YlGn')
ax.set_title('YlGn')
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Text(0.5, 1, 'YlGn')
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35 YlGnBu

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='YlGnBu')
ax.set_title('YlGnBu')
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Text(0.5, 1, 'YlGnBu')
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36 YlOrBr

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='YlOrBr')
ax.set_title('YlOrBr')
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Text(0.5, 1, 'YlOrBr')
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37 YlOrRd

f, ax = plt.subplots(figsize=(13,11))
sns.heatmap(data.corr(), annot=True, ax=ax,cmap='YlOrRd')
ax.set_title('YlOrRd')
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Text(0.5, 1, 'YlOrRd')
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