使用seaborn在python中绘制3列热图

Question

v1      v2      yy
15.25   44.34   100.00
83.05   59.78   100.00
96.61   65.09   100.00
100.00  75.47   100.00
100.00  50.00   100.00
100.00  68.87   100.00
100.00  79.35   100.00
100.00  100.00  100.00
100.00  63.21   100.00
100.00  100.00  100.00
100.00  68.87   100.00
0.00    56.52   92.86
10.17   52.83   92.86
23.73   46.23   92.86

在上面的数据框中，我想绘制一个热图，使用v1和v2作为x和y轴，yy作为值。我怎么能在python中做到这一点？我试过seaborn：

df = df.pivot('v1', 'v2', 'yy')
ax = sns.heatmap(df)

然而，这不起作用。还有其他解决办法吗？

Answer 1

seaborn heatmap绘制分类数据。这意味着每个出现的值将在热图中占用与任何其他值相同的空间，而与它们在数字上分开的距离无关。这对于数值数据通常是不期望的。而是可以选择以下技术之一。

Scatter

彩色散点图可能与热图一样好。点的颜色代表yy值。

ax.scatter(df.v1, df.v2, c=df.yy,  cmap="copper")

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u = u"""v1      v2      yy
15.25   44.34   100.00
83.05   59.78   100.00
96.61   65.09   100.00
100.00  75.47   100.00
100.00  50.00   100.00
100.00  68.87   100.00
100.00  79.35   100.00
100.00  100.00  100.00
100.00  63.21   100.00
100.00  100.00  100.00
100.00  68.87   100.00
0.00    56.52   92.86
10.17   52.83   92.86
23.73   46.23   92.86"""

import pandas as pd
import matplotlib.pyplot as plt
import io

df = pd.read_csv(io.StringIO(u), delim_whitespace=True )

fig, ax = plt.subplots()

sc = ax.scatter(df.v1, df.v2, c=df.yy,  cmap="copper")

fig.colorbar(sc, ax=ax)

ax.set_aspect("equal")


plt.show()

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Hexbin

您可能需要查看hexbin。数据将以六边形箱显示，数据汇总为每个箱内的平均值。这里的优点是，如果你选择gridsize large，它看起来就像一个散点图，而如果你把它做得很小，它就像一个热图，可以很容易地将图调整到所需的分辨率。

h1 = ax.hexbin(df.v1, df.v2, C=df.yy, gridsize=100, cmap="copper")
h2 = ax2.hexbin(df.v1, df.v2, C=df.yy, gridsize=10, cmap="copper")

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u = u"""v1      v2      yy
15.25   44.34   100.00
83.05   59.78   100.00
96.61   65.09   100.00
100.00  75.47   100.00
100.00  50.00   100.00
100.00  68.87   100.00
100.00  79.35   100.00
100.00  100.00  100.00
100.00  63.21   100.00
100.00  100.00  100.00
100.00  68.87   100.00
0.00    56.52   92.86
10.17   52.83   92.86
23.73   46.23   92.86"""

import pandas as pd
import matplotlib.pyplot as plt
import io

df = pd.read_csv(io.StringIO(u), delim_whitespace=True )

fig, (ax, ax2) = plt.subplots(nrows=2)

h1 = ax.hexbin(df.v1, df.v2, C=df.yy, gridsize=100, cmap="copper")
h2 = ax2.hexbin(df.v1, df.v2, C=df.yy, gridsize=10, cmap="copper")

fig.colorbar(h1, ax=ax)
fig.colorbar(h2, ax=ax2)
ax.set_aspect("equal")
ax2.set_aspect("equal")
ax.set_title("gridsize=100")
ax2.set_title("gridsize=10")
fig.subplots_adjust(hspace=0.3)
plt.show()

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Tripcolor

可以使用tripcolor绘图根据数据点获取绘图中的彩色区域，然后将其解释为三角形的边缘，根据边缘点着色。数据。这样的情节需要有更多的数据来提供有意义的表示。

ax.tripcolor(df.v1, df.v2, df.yy,  cmap="copper")

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u = u"""v1      v2      yy
15.25   44.34   100.00
83.05   59.78   100.00
96.61   65.09   100.00
100.00  75.47   100.00
100.00  50.00   100.00
100.00  68.87   100.00
100.00  79.35   100.00
100.00  100.00  100.00
100.00  63.21   100.00
100.00  100.00  100.00
100.00  68.87   100.00
0.00    56.52   92.86
10.17   52.83   92.86
23.73   46.23   92.86"""

import pandas as pd
import matplotlib.pyplot as plt
import io

df = pd.read_csv(io.StringIO(u), delim_whitespace=True )

fig, ax = plt.subplots()

tc = ax.tripcolor(df.v1, df.v2, df.yy,  cmap="copper")

fig.colorbar(tc, ax=ax)

ax.set_aspect("equal")
ax.set_title("tripcolor")

plt.show()

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请注意，如果整个网格中有更多数据点，则tricontourf图可能同样适用。

ax.tricontourf(df.v1, df.v2, df.yy,  cmap="copper")

Answer 2

您的数据有重复值的问题，如：

100.00  100.00  100.00
100.00  100.00  100.00

您必须删除重复值，然后转动并绘制如下：

import seaborn as sns
import pandas as pd

# fill data

df = pd.read_clipboard()
df.drop_duplicates(['v1','v2'], inplace=True)
pivot = df.pivot(index='v1', columns='v2', values='yy')
ax = sns.heatmap(pivot,annot=True)
plt.show()

print (pivot)

支点：

v2      44.34   46.23   50.00   52.83   56.52   59.78   63.21   65.09   \
v1                                                                       
0.00       NaN     NaN     NaN     NaN   92.86     NaN     NaN     NaN   
10.17      NaN     NaN     NaN   92.86     NaN     NaN     NaN     NaN   
15.25    100.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   
23.73      NaN   92.86     NaN     NaN     NaN     NaN     NaN     NaN   
83.05      NaN     NaN     NaN     NaN     NaN   100.0     NaN     NaN   
96.61      NaN     NaN     NaN     NaN     NaN     NaN     NaN   100.0   
100.00     NaN     NaN   100.0     NaN     NaN     NaN   100.0     NaN   

v2      68.87   75.47   79.35   100.00  
v1                                      
0.00       NaN     NaN     NaN     NaN  
10.17      NaN     NaN     NaN     NaN  
15.25      NaN     NaN     NaN     NaN  
23.73      NaN     NaN     NaN     NaN  
83.05      NaN     NaN     NaN     NaN  
96.61      NaN     NaN     NaN     NaN  
100.00   100.0   100.0   100.0   100.0