python入门技巧之特征分析（连续特征（图））

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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")

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