Python 数据分析 - 索引和选择数据

缺乏、安全感 2022-01-07 22:19 229阅读 0赞

# `loc`,`iloc`,`ix`三者间的区别和联系 #

## `loc` ##

`.loc` is primarily label based, but may also be used with a boolean array.  
就是说，loc方法主要是用`label`来选择数据的。1

*  A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index. This use is not an integer position along the index)
 *  A list or array of labels \['a', 'b', 'c'\]
 *  A slice object with labels 'a':'f', (note that contrary to usual python slices, both the start and the stop are included!)
 *  A boolean array

总的形式还是要保持的df\[xx:xx,xx:xx\]，只不过这里边可以不用切片，但是中间的`,`还是很关键的。可以不写`,`，那么，就表示取某一行。但是，不能表示取某一列。

import pandas as pd import numpy as np test=pd.DataFrame(np.random.randn(20).reshape(4,5),index=['A','B','C','D'],columns=['E','F','G','H','I']) test Out[4]: E F G H I A -0.833316 -1.982666 1.055594 0.781759 -0.107631 B -1.514709 -1.422883 0.204399 -0.487639 -1.652785 C -0.424735 0.400529 -0.786582 0.855885 0.059894 D 2.016221 -1.314878 -1.745535 -0.907778 0.834966 test.loc['A'] Out[5]: E -0.833316 F -1.982666 G 1.055594 H 0.781759 I -0.107631 Name: A, dtype: float64 test.loc['E'] KeyError: 'the label [E] is not in the [index]' #看见了吧，是“闭区间” test.loc['A':'B','E':'F'] Out[8]: E F A -0.833316 -1.982666 B -1.514709 -1.422883

**`label`切片选择时，貌似是“闭区间”，`:`后边的也是包含进去的。**

## `iloc` ##

`.iloc` is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.  
`iloc`主要就是基于`position`的选择。注意了，这里的`position`选择是一种”左闭右开“区间，意思就是df\[m:n\]只选择m:n-1行的数据。

*  An integer e.g. 5
 *  A list or array of integers \[4, 3, 0\]
 *  A slice object with ints 1:7
 *  A boolean array

import pandas as pd
    
    import numpy as np
    
    test=pd.DataFrame(np.random.randn(20).reshape(4,5),index=['A','B','C','D'],columns=['E','F','G','H','I'])
    
    test
    Out[4]: 
              E         F         G         H         I
    A -0.833316 -1.982666  1.055594  0.781759 -0.107631
    B -1.514709 -1.422883  0.204399 -0.487639 -1.652785
    C -0.424735  0.400529 -0.786582  0.855885  0.059894
    D  2.016221 -1.314878 -1.745535 -0.907778  0.834966
    
    #看见了吧，是“左闭右开”区间呀！
    test.iloc[0:1,0:1]
    Out[10]: 
              E
    A -0.833316

## `ix` ##

`.ix` supports mixed integer and label based access. It is primarily label based, but will fall back to integer positional access unless the corresponding axis is of integer type.  
`ix`就是一种集大成者的选择方法呀！既支持`position`选择，也支持`label`选择。主要是`label`选择。

import pandas as pd
    
    import numpy as np
    
    test=pd.DataFrame(np.random.randn(20).reshape(4,5),index=['A','B','C','D'],columns=['E','F','G','H','I'])
    
    test
    Out[4]: 
              E         F         G         H         I
    A -0.833316 -1.982666  1.055594  0.781759 -0.107631
    B -1.514709 -1.422883  0.204399 -0.487639 -1.652785
    C -0.424735  0.400529 -0.786582  0.855885  0.059894
    D  2.016221 -1.314878 -1.745535 -0.907778  0.834966
    
    #下面的`ix`是不是和`loc`作用差不多啊~
    test.ix['A':'B','E':'F']
    Out[12]: 
              E         F
    A -0.833316 -1.982666
    B -1.514709 -1.422883
    
    #下面的是和`iloc`差不多了
    test.ix[0:1,0:1]
    Out[11]: 
              E
    A -0.833316

但是需要注意的是，当`index`或者`columns`是整数时，`ix`索引其实是按`label`选择的，因此，是`闭区间的`。

# 参考 #

发现还是官方文档说的最详细啊！希望以后有机会多看看这里的内容~

--------------------

1.  [官方文档-Indexing and Selecting Data][-Indexing and Selecting Data]↩

转载于:https://www.cnblogs.com/michael-xiang/p/10466866.html

[-Indexing and Selecting Data]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing