carte thermique seaborn utilisant pandas dataframe
J'ai du mal à masser une trame de données en pandas dans le format correct pour la heatmap de seaborn (ou vraiment matplotlib) pour faire une heatmap.
Mon dataframe actuel (appelé data_yule) est:
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
et mes tentatives d'utilisation de matplotlib étaient:
plt.pcolor(data_yule.SymmetricDivision, data_yule.MutProb, data_yule.value)
qui a jeté l'erreur:
ValueError: not enough values to unpack (expected 2, got 1)
et la tentative marine était:
sns.heatmap(data_yule.SymmetricDivision, data_yule.MutProb, data_yule.value)
qui a jeté:
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
Cela semble trivial car les deux fonctions veulent un ensemble de données rectangulaire, mais il me manque quelque chose, clairement.
Les données doivent être "pivotées" pour ressembler
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
Ensuite, vous pouvez passer le tableau 2D (ou DataFrame) à seaborn.heatmap ou 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()
donne 