與matplotlib聯(lián)合使用

1，畫柱形圖

屬性在y軸

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

kde_kws? 注釋

2? distplot? 曲線柱形結(jié)合圖

y軸的數(shù)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，畫核密度估計，相比于直方圖更加利于顯示特征變化

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

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

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

label 多特征時不同特征的標簽，alpha透明程度，shadw填充

4,scipy.sparse.hstack vstack? ?矩陣拼接

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

5，畫箱線圖

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

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

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

6，畫散點圖

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

7,畫點線圖

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

8，畫熱度圖

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