seaborn

与matplotlib联合使用

1，画柱形图

属性在y轴

g = sns.countplot(y=column, data=dataset  ，kde_kws={"label": ">$50K")

kde_kws  注释

2  distplot  曲线柱形结合图

y轴的数值是概率密度

https://blog.csdn.net/qq_39949963/article/details/79362501

http://sofasofa.io/forum_main_post.php?postid=1005980

3，画核密度估计，相比于直方图更加利于显示特征变化

https://www.cnblogs.com/feffery/p/11128113.html

sns.kdeplot(subset['score'].dropna(),

              label = b_type, shade = False, alpha = 0.8);

label 多特征时不同特征的标签，alpha透明程度，shadw填充

4,scipy.sparse.hstack vstack   矩阵拼接

https://blog.csdn.net/TH_NUM/article/details/80044197

5，画箱线图

f, ax = plt.subplots(figsize=(8, 6))

fig = sns.boxplot(x=train['OverallQual'], y="SalePrice", data=data)

fig.axis(ymin=0, ymax=800000);

6，画散点图

data.plot.scatter(x='TotalBsmtSF', y='SalePrice', alpha=0.3, ylim=(0,800000))

7,画点线图

sns.pointplot(x=list(scores.keys()), y=[score for score, _ in scores.values()], markers=['o'], linestyles=['-'])

8，画热度图

sns.heatmap(df_test.drop(['PassengerId'], axis=1).corr(), ax=axs[1], annot=True, square=True, cmap='coolwarm', annot_kws={'size': 14})