pandasのデータフレームをseabornのヒートマップ(または実際にmatplotlib)の正しい形式にマッサージしてヒートマップを作成するのに苦労しています。
私の現在のデータフレーム(data_yuleと呼ばれる)は:
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
そして、matplotlibを使用した私の試みは:
plt.pcolor(data_yule.SymmetricDivision, data_yule.MutProb, data_yule.value)
エラーを投げました:
ValueError: not enough values to unpack (expected 2, got 1)
そして、海生まれの試みは:
sns.heatmap(data_yule.SymmetricDivision, data_yule.MutProb, data_yule.value)
投げた:
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
両方の関数が長方形のデータセットを必要とするので簡単なように見えますが、明らかに何かが欠けています。
データは "pivoted" である必要があります
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
次に、2D配列(またはDataFrame)をseaborn.heatmap
またはplt.pcolor
に渡すことができます。
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()