Python 如何将分类报告和混淆矩阵输出导出为pdf或excel文件

在下面的代码中，我想：

将

classification\u报告

和

cm

的值导出到excel

.xlsx

文件

将

classification\u报告

和

cm

的图像导出到

pdf

文件

运行我的代码，

分类报告

和

混乱矩阵（cm）

如下所示（参见下图）。但是，我想做第1和第2步

完整代码：

#libraries

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

#import data

df=pd.read_csv('https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv', delimiter=",")


X = df.loc[:,df.columns!='species'] 
y = df.loc[:,df.columns == 'species']

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=69876)

# train classifier and predict on test set 
model = XGBClassifier()
model.fit(X_train,y_train)
y_pred = model.predict(X_test)

print(classification_report(y_test,y_pred)) #   
cm =confusion_matrix(y_test, y_pred) #
print(cm) 
sns.heatmap(cm, annot=True) 
plt.show()

#libraries

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

#import data

df=pd.read_csv('https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv', delimiter=",")


X = df.loc[:,df.columns!='species'] 
y = df.loc[:,df.columns == 'species']

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=69876)

# train classifier and predict on test set 
model = XGBClassifier()
model.fit(X_train,y_train)
y_pred = model.predict(X_test)

print(classification_report(y_test,y_pred)) #   
cm =confusion_matrix(y_test, y_pred) #
print(cm) 
sns.heatmap(cm, annot=True) 
plt.show()