python - 如何使用 seaborn 制作以下条形图？

Question

SR=pd.DataFrame([['Linear Regression', 0.9533333333333334, 0.9747081712062257, 0.8255813953488372],['Ridge Classifier', 0.905, 0.9980544747081712, 0.3488372093023256],     ['Decision Tree Classifier',0.9883333333333333,0.9922178988326849,0.9651162790697675],     ['Random Forest', 0.9916666666666667, 0.9980544747081712, 0.9534883720930233],['XG Boost', 0.9916666666666667, 0.9980544747081712, 0.9534883720930233],['Neural Network', 1.0, 1.0, 1.0]], columns = ['Model', 'Accuracy','Sensitivity','Specificity'])

如何使用我拥有seaborn的数据框创建附加的条形图（在 excel 上制作） SR？

标签： pythonpandasdataframematplotlibseaborn

Answer 1

首先，你应该重新塑造你的数据框pandas.melt：

SR = pd.melt(frame = SR,
             id_vars = 'Model',
             var_name = 'Statistic',
             value_name = 'value')

所以你得到：

                       Model    Statistic     value
0          Linear Regression     Accuracy  0.953333
1           Ridge Classifier     Accuracy  0.905000
2   Decision Tree Classifier     Accuracy  0.988333
3              Random Forest     Accuracy  0.991667
4                   XG Boost     Accuracy  0.991667
5             Neural Network     Accuracy  1.000000
6          Linear Regression  Sensitivity  0.974708
7           Ridge Classifier  Sensitivity  0.998054
8   Decision Tree Classifier  Sensitivity  0.992218
9              Random Forest  Sensitivity  0.998054
10                  XG Boost  Sensitivity  0.998054
11            Neural Network  Sensitivity  1.000000
12         Linear Regression  Specificity  0.825581
13          Ridge Classifier  Specificity  0.348837
14  Decision Tree Classifier  Specificity  0.965116
15             Random Forest  Specificity  0.953488
16                  XG Boost  Specificity  0.953488
17            Neural Network  Specificity  1.000000

然后你可以绘制新的数据框：

sns.barplot(ax = ax, data = SR, x = 'Model', y = 'value', hue = 'Statistic')

完整代码

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

SR=pd.DataFrame([['Linear Regression', 0.9533333333333334, 0.9747081712062257, 0.8255813953488372],['Ridge Classifier', 0.905, 0.9980544747081712, 0.3488372093023256],     ['Decision Tree Classifier',0.9883333333333333,0.9922178988326849,0.9651162790697675],     ['Random Forest', 0.9916666666666667, 0.9980544747081712, 0.9534883720930233],['XG Boost', 0.9916666666666667, 0.9980544747081712, 0.9534883720930233],['Neural Network', 1.0, 1.0, 1.0]], columns = ['Model', 'Accuracy','Sensitivity','Specificity'])

SR = pd.melt(frame = SR,
             id_vars = 'Model',
             var_name = 'Statistic',
             value_name = 'value')

fig, ax = plt.subplots(figsize = (12, 6))

sns.barplot(ax = ax, data = SR, x = 'Model', y = 'value', hue = 'Statistic')

plt.show()