Marine heatmap using pandas data

Question

I am trying to massage a dataframe in pandas into the correct format for a marine heat map (or actually matplotlib) to make a heat map.

My current data frame (called data_yule):

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 to use matplotlib were:

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

who threw an error:

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

and attempted sea voyage was:

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

who threw:

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

It seems trivial, since both functions need a rectangular dataset, but I'm missing something, of course.

Answer 1

Data must be pivoted to look like

 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.heatmap or 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() 

gives