1.使用tips数据集，创建一个展示不同时间段(午餐/晚餐)账单总额分布的箱线图

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pdsns.set_style("darkgrid")
plt.rcParams["axes.unicode_minus"] = Falsetips = pd.read_csv("./tips.csv")
sns.boxplot(data=tips,x='time',y='total_bill')plt.title('Distribution of Total Bill by Time of Day (Lunch/Dinner)')
plt.show()

运行结果：

2. 使用iris数据集，绘制花萼长度与花瓣长度的散点图，并按不同种类着色

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pdsns.set_theme()iris = pd.read_csv("./iris.csv")sns.scatterplot(data=iris,x="sepal_length",y='petal_length',hue='species')plt.title('Sepal Length vs Petal Length by Species')
plt.show()

运行结果：

3.创建航班乘客数据的月度变化折线图，按年份着色

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pdsns.set_style("darkgrid")
plt.rcParams["axes.unicode_minus"] = Falseflights = pd.read_csv("./flights.csv")
sns.lineplot(data=flights,x='month',y='passengers',hue='year',)plt.title('Monthly Flight Passengers with Yearly Trends')
plt.xticks(rotation=45)  # 旋转月份标签以便显示清楚
plt.tight_layout()
plt.show()

运行结果：

4.使用diamonds数据集(需从seaborn导入)，绘制克拉与价格的散点图，并按切工质量着色

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pdsns.set_style("darkgrid")
plt.rcParams["axes.unicode_minus"] = Falsediamonds = pd.read_csv("./diamonds.csv")
sns.scatterplot(data=diamonds,x='carat',y='price',hue='cut', )plt.title('Carat vs Price by Cut Quality')
plt.legend(title='Cut', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

运行结果：

5.使用penguins数据集，绘制企鹅不同物种的喙长与喙深的联合分布图

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pdsns.set_style("darkgrid")
plt.rcParams["axes.unicode_minus"] = Falsepenguins = pd.read_csv("./penguins.csv")
sns.jointplot(data=penguins,x='bill_length_mm',y='bill_depth_mm',kind='scatter',hue='species')plt.show()

运行结果：