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数据：

sepal_length,sepal_width,petal_length,petal_width

5.1,3.5,1.4,0.2

4.9,3.0,1.4,0.2

4.7,3.2,1.3,0.2

4.6,3.1,1.5,0.2

5.0,3.6,1.4,0.2

5.4,3.9,1.7,0.4

4.6,3.4,1.4,0.3

5.0,3.4,1.5,0.2

4.4,2.9,1.4,0.2

4.9,3.1,1.5,0.1

5.4,3.7,1.5,0.2

4.8,3.4,1.6,0.2

4.8,3.0,1.4,0.1

4.3,3.0,1.1,0.1

5.8,4.0,1.2,0.2

5.7,4.4,1.5,0.4

5.4,3.9,1.3,0.4

5.1,3.5,1.4,0.3

5.7,3.8,1.7,0.3

5.1,3.8,1.5,0.3

5.4,3.4,1.7,0.2

5.1,3.7,1.5,0.4

4.6,3.6,1.0,0.2

5.1,3.3,1.7,0.5

4.8,3.4,1.9,0.2

5.0,3.0,1.6,0.2

5.0,3.4,1.6,0.4

5.2,3.5,1.5,0.2

5.2,3.4,1.4,0.2

4.7,3.2,1.6,0.2

4.8,3.1,1.6,0.2

5.4,3.4,1.5,0.4

5.2,4.1,1.5,0.1

5.5,4.2,1.4,0.2

4.9,3.1,1.5,0.2

5.0,3.2,1.2,0.2

5.5,3.5,1.3,0.2

4.9,3.6,1.4,0.1

4.4,3.0,1.3,0.2

5.1,3.4,1.5,0.2

5.0,3.5,1.3,0.3

4.5,2.3,1.3,0.3

4.4,3.2,1.3,0.2

5.0,3.5,1.6,0.6

5.1,3.8,1.9,0.4

4.8,3.0,1.4,0.3

5.1,3.8,1.6,0.2

4.6,3.2,1.4,0.2

5.3,3.7,1.5,0.2

5.0,3.3,1.4,0.2

7.0,3.2,4.7,1.4

6.4,3.2,4.5,1.5

6.9,3.1,4.9,1.5

5.5,2.3,4.0,1.3

6.5,2.8,4.6,1.5

5.7,2.8,4.5,1.3

6.3,3.3,4.7,1.6

4.9,2.4,3.3,1.0

6.6,2.9,4.6,1.3

5.2,2.7,3.9,1.4

5.0,2.0,3.5,1.0

5.9,3.0,4.2,1.5

6.0,2.2,4.0,1.0

6.1,2.9,4.7,1.4

5.6,2.9,3.6,1.3

6.7,3.1,4.4,1.4

5.6,3.0,4.5,1.5

5.8,2.7,4.1,1.0

6.2,2.2,4.5,1.5

5.6,2.5,3.9,1.1

5.9,3.2,4.8,1.8

6.1,2.8,4.0,1.3

6.3,2.5,4.9,1.5

6.1,2.8,4.7,1.2

6.4,2.9,4.3,1.3

6.6,3.0,4.4,1.4

6.8,2.8,4.8,1.4

6.7,3.0,5.0,1.7

6.0,2.9,4.5,1.5

5.7,2.6,3.5,1.0

5.5,2.4,3.8,1.1

5.5,2.4,3.7,1.0

5.8,2.7,3.9,1.2

6.0,2.7,5.1,1.6

5.4,3.0,4.5,1.5

6.0,3.4,4.5,1.6

6.7,3.1,4.7,1.5

6.3,2.3,4.4,1.3

5.6,3.0,4.1,1.3

5.5,2.5,4.0,1.3

5.5,2.6,4.4,1.2

6.1,3.0,4.6,1.4

5.8,2.6,4.0,1.2

5.0,2.3,3.3,1.0

5.6,2.7,4.2,1.3

5.7,3.0,4.2,1.2

5.7,2.9,4.2,1.3

6.2,2.9,4.3,1.3

5.1,2.5,3.0,1.1

5.7,2.8,4.1,1.3

6.3,3.3,6.0,2.5

5.8,2.7,5.1,1.9

7.1,3.0,5.9,2.1

6.3,2.9,5.6,1.8

6.5,3.0,5.8,2.2

7.6,3.0,6.6,2.1

4.9,2.5,4.5,1.7

7.3,2.9,6.3,1.8

6.7,2.5,5.8,1.8

7.2,3.6,6.1,2.5

6.5,3.2,5.1,2.0

6.4,2.7,5.3,1.9

6.8,3.0,5.5,2.1

5.7,2.5,5.0,2.0

5.8,2.8,5.1,2.4

6.4,3.2,5.3,2.3

6.5,3.0,5.5,1.8

7.7,3.8,6.7,2.2

7.7,2.6,6.9,2.3

6.0,2.2,5.0,1.5

6.9,3.2,5.7,2.3

5.6,2.8,4.9,2.0

7.7,2.8,6.7,2.0

6.3,2.7,4.9,1.8

6.7,3.3,5.7,2.1

7.2,3.2,6.0,1.8

6.2,2.8,4.8,1.8

6.1,3.0,4.9,1.8

6.4,2.8,5.6,2.1

7.2,3.0,5.8,1.6

7.4,2.8,6.1,1.9

7.9,3.8,6.4,2.0

6.4,2.8,5.6,2.2

6.3,2.8,5.1,1.5

6.1,2.6,5.6,1.4

7.7,3.0,6.1,2.3

6.3,3.4,5.6,2.4

6.4,3.1,5.5,1.8

6.0,3.0,4.8,1.8

6.9,3.1,5.4,2.1

6.7,3.1,5.6,2.4

6.9,3.1,5.1,2.3

5.8,2.7,5.1,1.9

6.8,3.2,5.9,2.3

6.7,3.3,5.7,2.5

6.7,3.0,5.2,2.3

6.3,2.5,5.0,1.9

6.5,3.0,5.2,2.0

6.2,3.4,5.4,2.3

5.9,3.0,5.1,1.8

将数据保存为自己想要的文档：txt\csv\xlsx

可视化代码：

#利用pyecharts

import numpy as np

from pyecharts import options as opts

from pyecharts.charts import Boxplot

import pandas as pd

flower = pd.read_excel(r'D:\桌面\数据清洗与可视化课件\2.matplotlib\flower作业\1.xlsx') #获取数据（根据自己的文件位置更改）

v1 = np.array([flower.sepal_length,flower.petal_length]).tolist() #数据必须处理成二维列表

v2 = np.array([flower.sepal_width,flower.petal_width]).tolist()

c = Boxplot()

c.add_xaxis(["花瓣", "花萼"]).add_yaxis("长度", c.prepare_data(v1)).add_yaxis("宽度", c.prepare_data(v2))

c.set_global_opts(title_opts=opts.TitleOpts(title="BoxPlot-基本示例"))

c.render_notebook() #实时显示

运行结果：