机器学习——Seaborn练习题

 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 numpy as np
import pandas as pdplt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = Falsedf = pd.read_csv("../data/tips.csv",encoding="utf-8")sns.boxplot(data = df,x = "time",y = "total_bill"
)plt.title("时间-账单总额(x-y)箱线图")
plt.show()

 结果展示：

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 numpy as np
import pandas as pdplt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = Falsedf = pd.read_csv("../data/iris.csv",encoding="utf-8")sns.scatterplot(data = df,x = "sepal_length",y = "petal_length",hue = "species"
)plt.title("花萼长度-花瓣长度散点图")
plt.show()

 结果展示：

 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 numpy as np
import pandas as pdplt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = Falsedf = pd.read_csv("../data/flights.csv",encoding="utf-8")sns.lineplot(data = df,x = "month",y = "passengers",hue = "year"
)plt.title("月-乘客数量")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

结果展示：

 

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 numpy as np
import pandas as pdplt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = Falsedf = pd.read_csv("../data/diamonds.csv",encoding="utf-8")sns.scatterplot(data = df,x = "carat",y = "price",hue = "cut",
)plt.title("克拉-价格散点图")
plt.show()

结果展示：

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 numpy as np
import pandas as pdplt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = Falsedf = pd.read_csv("../data/penguins.csv",encoding="utf-8")sns.jointplot(data = df,x = "bill_length_mm",y = "bill_depth_mm",hue = "species",
)plt.title("企鹅不同物种间喙长与喙深的联合分布图")
plt.tight_layout()
plt.show()

结果展示：