Pandas——ix vs loc vs iloc区别

Different Choices for Indexing

1. loc——通过行标签索引行数据

1.1 loc[1]表示索引的是第1行（index 是整数）

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = [0,1]
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.loc[1]
'''
a    4
b    5
c    6
'''

 

 

 

1.2 loc[‘d’]表示索引的是第’d’行（index 是字符）

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.loc['d']
'''
a    1
b    2
c    3
'''

 

 

 

1.3 如果想索引列数据，像这样做会报错

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.loc['a']
'''
KeyError: 'the label [a] is not in the [index]'
'''

 

 

 

1.4 loc可以获取多行数据

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.loc['d':]
'''
   a  b  c
d  1  2  3
e  4  5  6
'''

 

 

 

1.5 loc扩展——索引某行某列

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.loc['d',['b','c']]
'''
b    2
c    3
'''

 

 

 

1,6 loc扩展——索引某列

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.loc[:,['c']]
'''
   c
d  3
e  6
'''

 

 

 

当然获取某列数据最直接的方式是df.[列标签]，但是当列标签未知时可以通过这种方式获取列数据。

需要注意的是，dataframe的索引[1:3]是包含1,2,3的，与平时的不同。

2. iloc——通过行号获取行数据

2.1 想要获取哪一行就输入该行数字

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.loc[1]
'''
a    4
b    5
c    6
'''

 

 

 

2.2 通过行标签索引会报错

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.iloc['a']
'''
TypeError: cannot do label indexing on  with these indexers [a] of 
'''

 

 

 

2.3 同样通过行号可以索引多行

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.iloc[0:]
'''
   a  b  c
d  1  2  3
e  4  5  6
'''

 

 

 

2.4 iloc索引列数据

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.iloc[:,[1]]
'''
   b
d  2
e  5
'''

 

 

 

3. ix——结合前两种的混合索引

3.1 通过行号索引

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.ix[1]
'''
a    4
b    5
c    6
'''

 

 

 

3.2 通过行标签索引

 

import pandas as pd
data = [[1,2,3],[4,5,6]]
index = ['d','e']
columns=['a','b','c']
df = pd.DataFrame(data=data, index=index, columns=columns)
print df.ix['e']
'''
a    4
b    5
c    6
'''

 

想学人工智能（Python、数据分析、机器学习、深度学习、推荐系统、强化学习），来公众号AI派看看吧！！