In [1]:

    

import numpy as np
import pandas as pd
from pandas import Series, DataFrame


    

In [2]:

    

ser1 = Series(range(3), index=['C', 'A', 'B'])

ser1


    

    
Out[2]:





C    0
A    1
B    2
dtype: int64

In [3]:

    

ser1.sort_index()


    

    
Out[3]:





A    1
B    2
C    0
dtype: int64

In [5]:

    

ser1.sort_values()


    

    
Out[5]:





C    0
A    1
B    2
dtype: int64

In [7]:

    

from numpy.random import randn


    

In [8]:

    

ser2 = Series(randn(10))


    

In [9]:

    

ser2


    

    
Out[9]:





0   -1.238676
1   -0.262435
2    0.124352
3   -0.936106
4    1.164869
5    0.361579
6    1.693945
7    0.900263
8   -0.967899
9   -1.016677
dtype: float64

In [11]:

    

ser2.sort_values()


    

    
Out[11]:





0   -1.238676
9   -1.016677
8   -0.967899
3   -0.936106
1   -0.262435
2    0.124352
5    0.361579
7    0.900263
4    1.164869
6    1.693945
dtype: float64

In [12]:

    

ser2.rank()


    

    
Out[12]:





0     1.0
9     2.0
8     3.0
3     4.0
1     5.0
2     6.0
5     7.0
7     8.0
4     9.0
6    10.0
dtype: float64

In [13]:

    

ser3 = Series(randn(10))

ser3


    

    
Out[13]:





0    3.711138
1   -0.231539
2    1.119665
3   -0.799135
4    1.203301
5    0.523067
6    1.468005
7   -0.717893
8   -0.563958
9    0.392768
dtype: float64

In [14]:

    

ser3.rank()


    

    
Out[14]:





0    10.0
1     4.0
2     7.0
3     1.0
4     8.0
5     6.0
6     9.0
7     2.0
8     3.0
9     5.0
dtype: float64

In [16]:

    

ser3.sort_values()


    

    
Out[16]:





3   -0.799135
7   -0.717893
8   -0.563958
1   -0.231539
9    0.392768
5    0.523067
2    1.119665
4    1.203301
6    1.468005
0    3.711138
dtype: float64

In [17]:

    

ser3


    

    
Out[17]:





3   -0.799135
7   -0.717893
8   -0.563958
1   -0.231539
9    0.392768
5    0.523067
2    1.119665
4    1.203301
6    1.468005
0    3.711138
dtype: float64

In [ ]: