pandas警告：.ix is deprecated. .ix .loc .iloc的使用

df.loc[行范围，列范围]

Selection by Label

For getting a cross section using a label:

 
   In [26]: df.loc[dates[0]]
Out[26]: 
A    0.469112
B   -0.282863
C   -1.509059
D   -1.135632
Name: 2013-01-01 00:00:00, dtype: float64
 
  

Selecting on a multi-axis by label:

 
   In [27]: df.loc[:,['A','B']]
Out[27]: 
                   A         B
2013-01-01  0.469112 -0.282863
2013-01-02  1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04  0.721555 -0.706771
2013-01-05 -0.424972  0.567020
2013-01-06 -0.673690  0.113648
 
  

Showing label slicing, both endpoints are included:

 
   In [28]: df.loc['20130102':'20130104',['A','B']]
Out[28]: 
                   A         B
2013-01-02  1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04  0.721555 -0.706771
 
  

Reduction in the dimensions of the returned object:

 
   In [29]: df.loc['20130102',['A','B']]
Out[29]: 
A    1.212112
B   -0.173215
Name: 2013-01-02 00:00:00, dtype: float64
 
  

For getting a scalar value:

 
   In [30]: df.loc[dates[0],'A']
Out[30]: 0.46911229990718628

For getting fast access to a scalar (equivalent to the prior method):

 
   In [31]: df.at[dates[0],'A']
Out[31]: 0.46911229990718628

Selection by Position

See more in Selection by Position.

Select via the position of the passed integers:

 
   In [32]: df.iloc[3]
Out[32]: 
A    0.721555
B   -0.706771
C   -1.039575
D    0.271860
Name: 2013-01-04 00:00:00, dtype: float64
 
  

By integer slices, acting similar to numpy/python:

 
   In [33]: df.iloc[3:5,0:2]
Out[33]: 
                   A         B
2013-01-04  0.721555 -0.706771
2013-01-05 -0.424972  0.567020
 
  

By lists of integer position locations, similar to the numpy/python style:

 
   In [34]: df.iloc[[1,2,4],[0,2]]
Out[34]: 
                   A         C
2013-01-02  1.212112  0.119209
2013-01-03 -0.861849 -0.494929
2013-01-05 -0.424972  0.276232
 
  

For slicing rows explicitly:

 
   In [35]: df.iloc[1:3,:]
Out[35]: 
                   A         B         C         D
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
 
  

For slicing columns explicitly:

 
   In [36]: df.iloc[:,1:3]
Out[36]: 
                   B         C
2013-01-01 -0.282863 -1.509059
2013-01-02 -0.173215  0.119209
2013-01-03 -2.104569 -0.494929
2013-01-04 -0.706771 -1.039575
2013-01-05  0.567020  0.276232
2013-01-06  0.113648 -1.478427
 
  

For getting a value explicitly:

 
   In [37]: df.iloc[1,1]
Out[37]: -0.17321464905330858

For getting fast access to a scalar (equivalent to the prior method):

 
   In [38]: df.iat[1,1]
Out[38]: -0.17321464905330858