特征工程前的数据可视化技巧

以泰坦尼克乘客生还数据为例。

读取数据后：

data.head()
data.describe()
data.info()

观察数据大概情况。

plt.figsize=(16,8)
sns.countplot('Survived',data=data)
plt.title('Survived')

sex:

f,ax=plt.subplots(1,2,figsize=(18,8))

data[['Sex','Survived']].groupby(['Sex']).mean().plot.bar(ax=ax[0])
ax[0].set_title('Sex vs Survived')

sns.countplot('Sex',hue='Survived',data=data,ax=ax[1])
ax[1].set_title('Sex:Survived or dead')

Pclass:

f,ax=plt.subplots(1,2,figsize=(18,8))

data['Pclass'].value_counts().plot.bar(color='Black',ax=ax[0])
ax[0].set_title('Pclass vs Survived')
ax[0].set_ylabel('Count')

sns.countplot('Pclass',hue='Survived',data=data,ax=ax[1],palette='Set2')
ax[1].set_title('Pclass:Survived or dead')

船舱等级和性别对结果的影响:

sns.factorplot('Pclass','Survived',hue='Sex',data=data,palette='Set2')

age(连续值特征对结果的影响):

f,ax=plt.subplots(1,2,figsize=(18,8))

sns.violinplot('Pclass','Age',hue='Survived',split=True,data=data,ax=ax[0],palette='Set2')
ax[0].set_title('Pclass and Age vs Survived')
ax[0].set_yticks(range(0,110,10))

sns.violinplot('Sex','Age',hue='Survived',split=True,data=data,ax=ax[1],palette='Set2')
ax[1].set_title('Sex and Age vs Survived')
ax[1].set_yticks(range(0,110,10))

年龄分布（缺失值填充后）：

f,ax=plt.subplots(1,2,figsize=(18,8))

data[data['Survived']==0].Age.plot.hist(bins=20,ax=ax[0],edgecolor='black',color='red')
ax[0].set_title('Survived=0')
x1=list(range(0,85,5))

data[data['Survived']==1].Age.plot.hist(bins=20,ax=ax[1],edgecolor='black',color='blue')
ax[1].set_title('Survived=1')
x2=list(range(0,85,5))

embarked:

sns.factorplot('Embarked','Survived',data=data)