python入门技巧之特征分析（连续特征（图））

sns.distplot  #单一特征分布

sns.violinplot  #几个特征之间关系

例如

msurv = train[(train['Survived']==1) & (train['Sex']=="male")]
fsurv = train[(train['Survived']==1) & (train['Sex']=="female")]
mnosurv = train[(train['Survived']==0) &  (train['Sex']=="male")]
fnosurv = train[(train['Survived']==0)& (train['Sex']=="female")]

plt.figure(figsize=[13,5])
plt.subplot(121)
sns.distplot(fsurv['Age'].dropna().values, bins=range(0, 81, 1), kde=False, color=survcol)
sns.distplot(fnosurv['Age'].dropna().values, bins=range(0, 81, 1), kde=False, color=nosurvcol,
            axlabel='Female Age')
plt.subplot(122)
sns.distplot(msurv['Age'].dropna().values, bins=range(0, 81, 1), kde=False, color=survcol)
sns.distplot(mnosurv['Age'].dropna().values, bins=range(0, 81, 1), kde=False, color=nosurvcol,
            axlabel='Male Age')

sns.violinplot(x="Pclass", y="Age", hue="Survived", data=train, split=True)
plt.hlines([0,10], xmin=-1, xmax=3, linestyles="dotted")