使用 pandas 数据框的 seaborn 热图
seaborn heatmap using pandas dataframe
我正在努力将 pandas 中的数据帧转换为 seaborn 的热图（或实际上是 matplotlib）的正确格式以制作热图。
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)
引发错误：
ValueError: not enough values to unpack (expected 2, got 1)
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，因为这两个函数都需要矩形数据集，但我显然遗漏了一些东西。
数据需要"pivoted"才能看起来像
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
然后你可以将二维数组（或DataFrame）传递给seaborn.heatmap或plt.pcolor:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# load the sample data
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]})
# pivot the dataframe from long to wide form
result = df.pivot(index='SymmetricDivision', columns='MutProb', values='value')
sns.heatmap(result, annot=True, fmt="g", cmap='viridis')
plt.show()
产量
另一个选项使用 .grouby() 和 .unstack()
df_m = df.groupby(["SymmetricDivision", "MutProb"])
.size()
.unstack(level=0)
sns.heatmap(df_m)
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']。表示模型准确度的指标是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，在制作热图之前进行的操作之一是使用 Pandas 旋转功能来重塑数据以制作热图。为了进一步了解，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，现在显示在下图中。顺便说一句，这行得通。试一次，如果你还没有
Pandas 数据框有一个内置方法来计算称为 corr(). This returns a correlation matrix (of DataFrame type), just like unutbu 提及的列之间的相关性。
这个相关矩阵可以直接传递给 sns.heatmap() 函数。
具体来说，
sns.heatmap(data_yule.corr(), annot=True)
我正在努力将 pandas 中的数据帧转换为 seaborn 的热图（或实际上是 matplotlib）的正确格式以制作热图。
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)
引发错误：
ValueError: not enough values to unpack (expected 2, got 1)
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，因为这两个函数都需要矩形数据集，但我显然遗漏了一些东西。
数据需要"pivoted"才能看起来像
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
然后你可以将二维数组（或DataFrame）传递给seaborn.heatmap或plt.pcolor:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# load the sample data
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]})
# pivot the dataframe from long to wide form
result = df.pivot(index='SymmetricDivision', columns='MutProb', values='value')
sns.heatmap(result, annot=True, fmt="g", cmap='viridis')
plt.show()
产量
另一个选项使用 .grouby() 和 .unstack()
df_m = df.groupby(["SymmetricDivision", "MutProb"])
.size()
.unstack(level=0)
sns.heatmap(df_m)
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']。表示模型准确度的指标是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，在制作热图之前进行的操作之一是使用 Pandas 旋转功能来重塑数据以制作热图。为了进一步了解，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，现在显示在下图中。顺便说一句，这行得通。试一次，如果你还没有
Pandas 数据框有一个内置方法来计算称为 corr(). This returns a correlation matrix (of DataFrame type), just like unutbu 提及的列之间的相关性。
这个相关矩阵可以直接传递给 sns.heatmap() 函数。
具体来说，
sns.heatmap(data_yule.corr(), annot=True)