VizPool，一个超强的Python可视化库！

pip install vizpool -i https://mirror.baidu.com/pypi/simple

import vizpool as vizpool

from vizpool.static import EDA
from vizpool.static import Evaluation

import seaborn as sns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, SGDClassifier,  LinearRegression
from sklearn.svm import SVC
from sklearn.tree import ExtraTreeClassifier
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder

# 加载数据集
df = pd.read_csv("tips.csv")

# EDA实例化(Exploratory Data Analysis)
tips_eda = EDA(df)

total_bill：消费总金额
tip：小费金额
sex：性别
smoker：是否吸烟
day：消费日期
time：消费时段
size：聚餐人数

# 饼图条形图
plt = tips_eda.pie_bar(hue='sex');
plt.savefig("Pie_bar.png")

# 所有数值列的箱线图网格
plt = tips_eda.boxplot(height=3, width=10)
plt.savefig("Box_all.png")

# 指定分类的所有数值列的箱线图网格
plt = tips_eda.boxplot(hue='sex', height=6)
plt.savefig("Box_sex.png")

# 特定数值列的箱线图
plt = tips_eda.boxplot(col_to_plot=['total_bill'], width=4, height=3)
plt.savefig("Box_total_bill.png")

# 指定分类的任何数值的箱线图
plt = tips_eda.boxplot(col_to_plot=['total_bill'], hue='sex', width=4, height=3)
plt.savefig("Box_sex_total_bill.png")

# 数据中所有非数字列的计数图网格
plt = tips_eda.countplot()
plt.savefig("Count.png")

# 单个分类列的计数图
plt = tips_eda.countplot(['sex'], height=4, width=4)
plt.savefig("Count_sex.png")

# 所有数值列的直方图网格
plt = tips_eda.histogram(height=7)
plt.savefig("Histogram.png")

# 直方图, 其中分类列作为关键字参数传递给hue
plt = tips_eda.histogram(hue='sex', height=7)
plt.savefig("Histogram_sex.png")

# 特定数值列的直方图
plt = tips_eda.histogram(col_to_plot=['total_bill'], height=4, width=6)
plt.savefig("Histogram_total_bill.png")

# 指定分类的特定数值列的直方图
plt = tips_eda.histogram(col_to_plot=['total_bill'], hue='sex', height=4, width=6)
plt.savefig("Histogram_sex_total_bill.png")

# 所有分类列相对于所提供的数字列的条形图网格
plt = tips_eda.barplot(y='tip', estimator='sum')
plt.savefig("Bar.png")

# 针对所提供的数字列的所有分类列的条形图网格，色调设置为分类列
plt = tips_eda.barplot(y='tip', hue='time').show()
plt.savefig("Bar_time.png")

# 选取单变量的柱状图
plt = tips_eda.barplot(y='tip', col_to_plot='smoker', hue='time', height=3, width=4)
plt.savefig("Bar_single.png")

# 针对目标列或分类列的所有数值列的Violinplot作为关键字参数传递
plt = tips_eda.violinplot(hue='sex', height=7)
plt.savefig("Violin.png")

# 作为关键字参数传递的针对目标列或分类列的选择性数值列的Violinplot
plt = tips_eda.violinplot(col_to_plot=['total_bill', 'tip'], hue='sex', height=5)
plt.savefig("Violin_col.png")

# 热力图
plt = tips_eda.corr_heatmap(height=5, width=5)
plt.savefig("Correlation_Heatmap.png")

# 两个数值变量的联合图
plt = tips_eda.jointplot(x='total_bill',
                         y='tip',
                         height=5, width=5,
                         color='green')
plt.savefig("Joint.png")

# 包含作为关键字参数传递的分类列的所有数值特征的成对图
plt = tips_eda.pairplot(hue='sex', height=5, width=8)
plt.savefig("Pair.png")

# 使用sklearn机器学习管道获取特征重要性
pipeline_data = df[["total_bill", "tip", "size", "sex", "smoker"]]
target_class_labels = pipeline_data['smoker'].unique().tolist()
target = LabelEncoder().fit_transform(pipeline_data.pop('smoker'))
X_train, X_val, y_train, y_val = train_test_split(pipeline_data, target, test_size=0.2, random_state=42)

# 实例化评估类
model_eval = Evaluation(y_val)

col_trans = ColumnTransformer(transformers=[
    ('num_processing', StandardScaler(), ["total_bill", "tip", "size"]),
    ('cat_processing', OneHotEncoder(), ['sex'])
], remainder='drop')
pipe_rfc = Pipeline(steps=[
    ('tranformer', col_trans),
    ('classifier', RandomForestClassifier())
])
pipe_rfc.fit(X_train, y_train)
plt = model_eval.feature_importance(pipe_rfc, X_val=X_val, pipeline=True,
                                    height=6, width=16)
plt.savefig("RandomForestClassifier.png")

# 用估计器获得特征的重要性
df = df[["total_bill", "tip", "size", "smoker"]]
target_class_labels = df['smoker'].unique().tolist()
target = LabelEncoder().fit_transform(df.pop('smoker'))
X_train, X_val, y_train, y_val = train_test_split(df, target, test_size=0.2, random_state=42)
X_train.shape, X_val.shape, y_train.shape, y_val.shape

logistic_reg_clf = LogisticRegression()
logistic_reg_clf.fit(X_train, y_train)
logistic_predictions = logistic_reg_clf.predict(X_val)

etc = ExtraTreeClassifier()
etc.fit(X_train, y_train)
etc_predictions = etc.predict(X_val)

svc = SVC(probability=True)
svc.fit(X_train, y_train)
svc_predictions = svc.predict(X_val)

sgd = SGDClassifier()
sgd.fit(X_train, y_train)
sgd_predictions = sgd.predict(X_val)
plt = model_eval.feature_importance(etc, X_val=X_val,
                                    height=6, width=16)
plt.savefig("ExtraTreeClassifier.png")

# 带比率的混淆矩阵
plt = model_eval.confusion_matrix(logistic_predictions,
                                  target_names=target_class_labels,
                                  height=3, width=4)
plt.savefig("predictions_rate.png")

# 带计数的混淆矩阵
plt = model_eval.confusion_matrix(logistic_predictions, target_names=target_class_labels,
                                  height=3, width=4, normalize=False)
plt.savefig("predictions_count.png")

# ROC曲线
plt = model_eval.auc_roc_plot(X_val, [sgd, svc, logistic_reg_clf, etc],
                              ['SGDClassifier', 'SVC', 'Logistic Regression', 'ExtraTreeClf'],
                              height=5, width=8)
plt.savefig("ROC.png")

# 残差图
# 训练线性回归模型
lr = LinearRegression()
X = df[['size', 'total_bill']]
y = df['tip']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
lr.fit(X_train, y_train)
y_predicted = lr.predict(X_val)

# 模型评估
lr_model_eval = Evaluation(y_val)
plt = lr_model_eval.residplot(y_predicted=y_predicted, color='red')
plt.savefig("predicted.png")