机器学习基础——Seaborn使用

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 matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as snstips = pd.read_csv('./tips.csv')sns.boxplot(data = tips,x = 'time',y = 'total_bill',
)plt.show()

 

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 matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as snsiris = pd.read_csv('./iris.csv')sns.scatterplot(data = iris,x = 'sepal_length',y = 'sepal_width',hue = 'species'
)plt.show()

 

3. 创建航班乘客数据的月度变化折线图,按年份着色

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as snsflights = pd.read_csv('./flights.csv')sns.lineplot(data = flights,x = 'month',y = 'passengers',hue = 'year'
)plt.show()

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 matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as snsdiamonds = sns.load_dataset('diamonds')sns.scatterplot(data=diamonds,x='carat',y='price',hue='cut')
plt.show()

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 matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as snspenguins = pd.read_csv('./penguins.csv')sns.jointplot(data = penguins,x = 'bill_length_mm',y = 'bill_depth_mm',hue = 'species'kind = 'scatter'
)plt.show()