Pandas——ix vs loc vs iloc区别

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转载最新推荐文章于 2026-09-15 11:07:49 发布 8w 阅读 AI 写同款· GEO 优化 ›

Python3学习（四十四）：pandas中Dataframe索引.ix,.iloc,.loc的使用以及区别原文链接：https://blog.youkuaiyun.com/qq1483661204/article/details/77587881 作者：ML_BOY 1 定义一个pandas的DataFrame对像 import pandas as pd data = pd.DataFrame({'A':[1,2,3],'B':[4,5,6],'C':[7,8,9]},index=["a","b","... 阅读详情

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 <class 'pandas.core.index.Index'> with these indexers [a] of <type 'str'>
'''

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
'''

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