数据集为鸢尾花，效果如下：

seaborn版本

sns.set(style="darkgrid")# 添加背景
chart = sns.FacetGrid(iris_df, hue="species") .map(plt.scatter, "sepal length (cm)", "sepal width (cm)") .add_legend()
chart.fig.set_size_inches(12,6)

2. seaborn的jointplot绘制

sns.set(style="white", color_codes=True) 
sns.jointplot(x="sepal length (cm)", y="sepal width (cm)", data=iris_df, size=5)

3. seaborn的jointplot绘制（等高线牛逼版）

# 没加阴影
sns.set(style="white", color_codes=True) 
sns.jointplot(x='petal length (cm)', y='sepal width (cm)',data= iris_df, kind="kde", hue='species' # 按照鸢尾花的类别进行了颜色区分
)
plt.show()

sns.jointplot(x='petal length (cm)', y='sepal width (cm)',data= iris_df,              kind="kde",hue='species',joint_kws=dict(alpha =0.6,shade = True,),marginal_kws=dict(shade=True)
)

参考文章

组合点阵图

sns.pairplot

sns.pairplot(iris_df, hue="species", size=3)

叠加图Area Plot

iris_df.plot.area(y=['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width 
(cm)'],alpha=0.4,figsize=(12, 6));

按照功能

绘制混淆矩阵

其实就是热力图：

y_pred_grid_knn = knn_grid_search.predict(X_test)
y_pred_grid_logi = logistic_grid_search.predict(X_test)
y_pred_grid_nb = naive_bayes.predict(X_test)matrix_1 = confusion_matrix(y_test, y_pred_grid_knn) 
matrix_2 = confusion_matrix(y_test, y_pred_grid_logi) 
matrix_3 = confusion_matrix(y_test, y_pred_grid_nb) df_1 = pd.DataFrame(matrix_1,index = ['setosa','versicolor','virginica'], columns = ['setosa','versicolor','virginica'])df_2 = pd.DataFrame(matrix_2,index = ['setosa','versicolor','virginica'], columns = ['setosa','versicolor','virginica'])df_3 = pd.DataFrame(matrix_3,index = ['setosa','versicolor','virginica'], columns = ['setosa','versicolor','virginica'])
plt.figure(figsize=(20,5))
plt.subplots_adjust(hspace = .25)
plt.subplot(1,3,1)
plt.title('confusion_matrix(KNN)')
sns.heatmap(df_1, annot=True,cmap='Blues')
plt.subplot(1,3,2)
plt.title('confusion_matrix(logistic regression)')
sns.heatmap(df_2, annot=True,cmap='Greens')
plt.subplot(1,3,3)
plt.title('confusion_matrix(naive_bayes)')
sns.heatmap(df_3, annot=True,cmap='Reds')
plt.show()

绘制ROC&PR曲线（无敌）

在kaggle 偶然看到的，这也太好看了，用到了yellowbrick这个库

from yellowbrick.classifier import PrecisionRecallCurve, ROCAUC, ConfusionMatrix
from yellowbrick.style import set_palette
from yellowbrick.cluster import KElbowVisualizer
from yellowbrick.model_selection import LearningCurve, FeatureImportances
from yellowbrick.contrib.wrapper import wrap# --- LR Accuracy ---
LRAcc = accuracy_score(y_pred_grid_logi, y_test)
print('.:. Logistic Regression Accuracy:'+'\033[35m\033[1m {:.2f}%'.format(LRAcc*100)+' \033[0m.:.')# --- LR Classification Report ---
print('\033[35m\033[1m\n.: Classification Report'+'\033[0m')
print('*' * 25)
print(classification_report(y_test, y_pred_grid_logi))# --- Performance Evaluation ---
print('\033[35m\n\033[1m'+'.: Performance Evaluation'+'\033[0m')
print('*' * 26)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize = (14, 12))#--- LR Confusion Matrix ---
logmatrix = ConfusionMatrix(logistic_grid_search, ax=ax1, cmap='RdPu', title='Logistic Regression Confusion Matrix')
logmatrix.fit(X_train, y_train)
logmatrix.score(X_test, y_test)
logmatrix.finalize()# --- LR ROC AUC ---
logrocauc = ROCAUC(logistic_grid_search, ax = ax2, title = 'Logistic Regression ROC AUC Plot')
logrocauc.fit(X_train, y_train)
logrocauc.score(X_test, y_test)
logrocauc.finalize()# --- LR Learning Curve ---
loglc = LearningCurve(logistic_grid_search, ax = ax3, title = 'Logistic Regression Learning Curve')
loglc.fit(X_train, y_train)
loglc.finalize()# --- LR Precision Recall Curve ---
logcurve = PrecisionRecallCurve(logistic_grid_search, ax = ax4, ap_score = True, iso_f1_curves = True, title = 'Logistic Regression Precision-Recall Curve')
logcurve.fit(X_train, y_train)
logcurve.score(X_test, y_test)
logcurve.finalize()plt.tight_layout();