Python 使用熊猫数据框的seaborn热图
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原文地址: http://stackoverflow.com/questions/37790429/
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seaborn heatmap using pandas dataframe
提问by cancerconnector
I am struggling to massage a dataframe in pandas into the correct format for seaborn's heatmap (or matplotlib really) to make a heatmap.
我正在努力将 Pandas 中的数据帧转换为 seaborn 热图（或实际上是 matplotlib）的正确格式以制作热图。
My current dataframe (called data_yule) is:
我当前的数据帧（称为 data_yule）是：
Unnamed: 0 SymmetricDivision test MutProb value
3 3 1.0 sackin_yule 0.100 -4.180864
8 8 1.0 sackin_yule 0.050 -9.175349
13 13 1.0 sackin_yule 0.010 -11.408114
18 18 1.0 sackin_yule 0.005 -10.502450
23 23 1.0 sackin_yule 0.001 -8.027475
28 28 0.8 sackin_yule 0.100 -0.722602
33 33 0.8 sackin_yule 0.050 -6.996394
38 38 0.8 sackin_yule 0.010 -10.536340
43 43 0.8 sackin_yule 0.005 -9.544065
48 48 0.8 sackin_yule 0.001 -7.196407
53 53 0.6 sackin_yule 0.100 -0.392256
58 58 0.6 sackin_yule 0.050 -6.621639
63 63 0.6 sackin_yule 0.010 -9.551801
68 68 0.6 sackin_yule 0.005 -9.292469
73 73 0.6 sackin_yule 0.001 -6.760559
78 78 0.4 sackin_yule 0.100 -0.652147
83 83 0.4 sackin_yule 0.050 -6.885229
88 88 0.4 sackin_yule 0.010 -9.455776
93 93 0.4 sackin_yule 0.005 -8.936463
98 98 0.4 sackin_yule 0.001 -6.473629
103 103 0.2 sackin_yule 0.100 -0.964818
108 108 0.2 sackin_yule 0.050 -6.051482
113 113 0.2 sackin_yule 0.010 -9.784686
118 118 0.2 sackin_yule 0.005 -8.571063
123 123 0.2 sackin_yule 0.001 -6.146121
and my attempts using matplotlib was:
我使用 matplotlib 的尝试是：
plt.pcolor(data_yule.SymmetricDivision, data_yule.MutProb, data_yule.value)
which threw the error:
抛出错误：
ValueError: not enough values to unpack (expected 2, got 1)
and the seaborn attempt was:
而seaborn的尝试是：
sns.heatmap(data_yule.SymmetricDivision, data_yule.MutProb, data_yule.value)
which threw:
其中抛出：
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
It seems trivial as both functions want rectangular dataset, but I'm missing something, clearly.
这似乎微不足道，因为这两个函数都需要矩形数据集，但显然我遗漏了一些东西。
回答by unutbu
The data needs to be "pivoted"to look like
数据需要“旋转”才能看起来像
In [96]: result
Out[96]:
MutProb 0.001 0.005 0.010 0.050 0.100
SymmetricDivision
0.2 -6.146121 -8.571063 -9.784686 -6.051482 -0.964818
0.4 -6.473629 -8.936463 -9.455776 -6.885229 -0.652147
0.6 -6.760559 -9.292469 -9.551801 -6.621639 -0.392256
0.8 -7.196407 -9.544065 -10.536340 -6.996394 -0.722602
1.0 -8.027475 -10.502450 -11.408114 -9.175349 -4.180864
Then you can pass the 2D array (or DataFrame) to seaborn.heatmapor plt.pcolor:
然后您可以将二维数组（或 DataFrame）传递给seaborn.heatmapor plt.pcolor：
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.DataFrame({'MutProb': [0.1,
0.05, 0.01, 0.005, 0.001, 0.1, 0.05, 0.01, 0.005, 0.001, 0.1, 0.05, 0.01, 0.005, 0.001, 0.1, 0.05, 0.01, 0.005, 0.001, 0.1, 0.05, 0.01, 0.005, 0.001], 'SymmetricDivision': [1.0, 1.0, 1.0, 1.0, 1.0, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6, 0.6, 0.6, 0.6, 0.6, 0.4, 0.4, 0.4, 0.4, 0.4, 0.2, 0.2, 0.2, 0.2, 0.2], 'test': ['sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule', 'sackin_yule'], 'value': [-4.1808639999999997, -9.1753490000000006, -11.408113999999999, -10.50245, -8.0274750000000008, -0.72260200000000008, -6.9963940000000004, -10.536339999999999, -9.5440649999999998, -7.1964070000000007, -0.39225599999999999, -6.6216390000000001, -9.5518009999999993, -9.2924690000000005, -6.7605589999999998, -0.65214700000000003, -6.8852289999999989, -9.4557760000000002, -8.9364629999999998, -6.4736289999999999, -0.96481800000000006, -6.051482, -9.7846860000000007, -8.5710630000000005, -6.1461209999999999]})
result = df.pivot(index='SymmetricDivision', columns='MutProb', values='value')
sns.heatmap(result, annot=True, fmt="g", cmap='viridis')
plt.show()
yields
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