pandas中DataFrame选择数据可以用iloc选择器，注意iloc不是函数。iloc用于位置索引筛选，也可以用于布尔值筛选。

　　官方介绍如下：

　　Purely integer-location based indexing for selection by position。.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

　　我们按照这个df为例来进行学习。

df = pd.DataFrame([['baidu','800','200'],['sogou','500','100'],['google','100','60']],columns=['pingtai','pv','uv'],index=['a','b','c'])
print(df)

  pingtai   pv   uv
a   baidu  800  200
b   sogou  500  100
c  google  100   60

　　1、iloc位置索引选择单行，返回Series或者DataFrame

df = pd.DataFrame([['baidu','800','200'],['sogou','500','100'],['google','100','60']],columns=['pingtai','pv','uv'],index=['a','b','c'])
print(df.iloc[0])
print('===')
print(df.iloc[[0]])

pingtai    baidu
pv           800
uv           200
Name: a, dtype: object
===
  pingtai   pv   uv
a   baidu  800  200

　　2、iloc位置索引选择多行及所有列，返回DataFrame

df = pd.DataFrame([['baidu','800','200'],['sogou','500','100'],['google','100','60']],columns=['pingtai','pv','uv'],index=['a','b','c'])
print(df.iloc[[0,1]])

  pingtai   pv   uv
a   baidu  800  200
b   sogou  500  100

　　3、iloc位置索引选择多行多列，返回DataFrame

df = pd.DataFrame([['baidu','800','200'],['sogou','500','100'],['google','100','60']],columns=['pingtai','pv','uv'],index=['a','b','c'])
print(df.iloc[[0,1],[1,2]])

    pv   uv
a  800  200
b  500  100

　　4、iloc位置切片索引（末端不包含）选择多行及所有列，返回DataFrame

df = pd.DataFrame([['baidu','800','200'],['sogou','500','100'],['google','100','60']],columns=['pingtai','pv','uv'],index=['a','b','c'])
print(df.iloc[[0,1],:])

  pingtai   pv   uv
a   baidu  800  200
b   sogou  500  100

　　5、iloc位置切片索引（末端不包含）选择多行多列，返回DataFrame

df = pd.DataFrame([['baidu','800','200'],['sogou','500','100'],['google','100','60']],columns=['pingtai','pv','uv'],index=['a','b','c'])
print(df.iloc[0:2,0:2])

  pingtai   pv
a   baidu  800
b   sogou  500

　　6、iloc布尔索引（末端不包含）选择多行多列，返回DataFrame

df = pd.DataFrame([['baidu','800','200'],['sogou','500','100'],['google','100','60']],columns=['pingtai','pv','uv'],index=['a','b','c'])
print(df.iloc[[True,True,True],[False,True,True]])

    pv   uv
a  800  200
b  500  100
c  100   60

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