使用熊猫数据帧的seaborn热图

Question

我正在努力按照pabas中的数据帧按照seaborn的热图（或matplotlib）的正确格式来制作热图。

我当前的数据框（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

我使用matplotlib的尝试是：

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

引发了错误：

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

和seaborn的尝试是：

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

投掷：

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

这似乎微不足道，因为两个函数都需要矩形数据集，但我清楚地遗漏了一些东西。

Answer 1

数据需要"pivoted"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

然后您可以将2D数组（或DataFrame）传递给seaborn.heatmap或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()

的产率

Answer 2

使用 .grouby() 和 .unstack() 的另一个选项

df_m = df.groupby(["SymmetricDivision", "MutProb"])
          .size()
          .unstack(level=0)

sns.heatmap(df_m)

Answer 3

我必须创建一个热图来表示我的 DecisionTreeClassifier 模型的超参数调整结果，超参数为 ['max_depth', 'min_samples_split']。表示模型准确率的指标是 auc_roc_score。

我们将使用 seaborn heatmap 为 sns.heatmap() 创建数据集。

我们使用 pandas.pivot_table() 在 Pandas 中旋转 DataFrame，制作热图之前的操作之一是使用 Pandas 枢轴功能来重塑数据以制作热图。为了进一步理解，pandas pivot_table()

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt[![enter image description here][2]][2]

max_depth = [1, 5, 10, 50]
min_samples_split = [5, 10, 100, 500]

train_set_auc = [0.5478688747761202, 0.5478688747761202, 0.5478688747761202, 
                 0.5478688747761202, 0.6384311073066989, 0.6383831998001249, 
                 0.6382879025310534, 0.6381476488966438, 0.7099806625823346, 
                 0.7090830674744466, 0.7035862433327912, 0.6987068807767094, 
                 0.9203751110844185, 0.9140516357169505, 0.8909876822536001, 
                 0.845991656101182]

# Now, let me explain the approach
# 1. we will use pandas.pivot_table()[seaborn heatmap tutorial][1] [pandas pivot_table()][1] to create a 
# dataset for sns.heatmap()

# convert train_set_auc into 4X4 np array

def return_dataframe(lst, max_depth, min_samples):
    """ this function will take a list and convert it into a 
        4X4 numpy array. Further, it will create an empty dataframe 
        with columns= min_samples_split and indices as max_depth. Then 
        it will fill the entries with values from the numpy array
   """
    arr = []
    idx = 0

    for idx in range(0, len(lst), 4):
        arr.append(np.array(lst[idx:idx+4]))

    arr = np.array(arr)

    df = pd.DataFrame(data=arr, index=min_samples, columns=max_depth)

   return df

train_set1 = return_dataframe(lst=train_set1_auc, max_depth=max_depth, min_samples=min_samples_split)
fig, ax = plt.subplots()

ax = sns.heatmap(train_set1, annot=True, fmt='g', cmap='viridis')
plt.xlabel("Your x-label")
plt.ylabel("Your y-label")
plt.show()

我在编辑中添加了 plt.xlabel() 和 plt.ylabel()。抱歉，现在如下图所示。顺便说一下，这是有效的。尝试一次，如果你还没有