数据理解

简单查看数据

from pandas import read_csv
filename = 'pima.csv'
names = ['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(filename,names=names)
peek = data.head(10)
print(peek)

数据维度

from pandas import read_csv
filename = 'pima.csv'
names = ['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(filename,names=names)
print(data.shape)

数据属性与类型

from pandas import read_csv
filename = 'pima.csv'
names = ['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(filename,names=names)
print(data.dtypes)

数据属性与类型

from pandas import read_csv
from pandas import set_option
filename = 'pima.csv'
names = ['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(filename,names=names)
set_option('display.width',100)
set_option('precision',4)
print(data.describe())

数据分组分布

from pandas import read_csv
filename = 'pima.csv'
names = ['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(filename,names=names)
print(data.groupby('class').size())

数据属性相关性

数据属性的相关性是指数据的两个属性是否相互影响，以及这种影响是什么方式，可使用皮尔逊相关系数（该系数介于1到-1之间，1为正相关，0为无关，-1为负相关）
当数据相关性比较高时，考虑对特征进行降维处理，如下使用DataFrame的corr（）方法计算关系矩阵

from pandas import read_csv
from pandas import set_option
filename = 'pima.csv'
names = ['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(filename,names=names)
set_option('display.width',100)
set_option('precision',2)
print(data.corr(method='pearson'))

数据的分布分析

计算数据的高斯偏离情况

from pandas import read_csv
filename = 'pima.csv'
names = ['preg','plas','pres','skin','test','mass','pedi','age','class']
data = read_csv(filename,names=names)
print(data.skew())