Mapa termiczna seaborn korzystająca z pand ramki danych

Question

Staram się masować ramkę danych w pandach w odpowiednim formacie mapy termicznej seaborn (lub matplotlib naprawdę), aby utworzyć mapę cieplną.

Mój obecny dataframe (tzw data_yule) wynosi:

 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 

i moje próby z użyciem matplotlib było:

plt.pcolor(data_yule.SymmetricDivision, data_yule.MutProb, data_yule.value) 

który wyrzucił błąd:

ValueError: not enough values to unpack (expected 2, got 1) 

i Seaborn próbę był:

sns.heatmap(data_yule.SymmetricDivision, data_yule.MutProb, data_yule.value) 

który rzucił:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). 

Wydaje się banalne jak obie funkcje chcą prostokątny zestaw danych, ale jestem brakuje czegoś, wyraźnie.

Answer 1

dane muszą być "pivoted" wyglądać

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 

Następnie można przekazać tablicę 2D (lub DataFrame) do seaborn.heatmap lub 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() 

plony