sns.lmplot(x=   ,   y=   ,    data=    ,hue=   ,  fit_reg=True/False....)

参数说明：

x/y=...     ： 是指定画图时的x坐标是啥，y是啥，这里不是将其命名，而是指出以什么参数为x、y轴   ;一般是某一个“属性”,即特征

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data=       ：这里指定数据，并且数据一定要是DataFrame结构

                     这里就涉及到将load_或者fetch获得的数据结构进行变化：

Data_load=pd.DataFrame(data["data"],columns=data.feature_names)

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hue=        :这里是指按照什么进行分类,

data【“data”】获取的数据一般是这样的：

一般我们把它再加一列，就是将每个样本的目标值，即标签加入进去

Data_load["target"]=data.target

所以这里的hue一般这样写：

hue=Data_load.target或者["target"]

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fit_reg=T/F:是否进行线性拟合

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整体代码：

# 将数据用seaborn库进行可视化
data_1=pd.DataFrame(data=dataSet1["data"],columns=dataSet1.feature_names)
data_1["target"]=dataSet1.target
print(data_1)
print(data_1.columns[0])
sns.lmplot(x=data_1.columns[0], y=data_1.columns[1], data=data_1, hue="target")
plt.xlabel("cols1")
plt.ylabel("cols2")
plt.title("鸢尾花")
plt.show()

结果：

注意图的相关显示的属性，如x坐标名称，y坐标名称，图的题目等是同matplotlib那个一样，都是 “plt.xxx”

最后的展示也是“plt.show()”