sorting data according to conditions is an important built-in operation. To sort rows or columns (in dictionary order), use the sort? Index method, which returns a new sorted object.

frame= DataFrame(np.arange(8).reshape(2,4),
				index=['one','two'],columns=[list('dabc')])
frame
Out[5]: 
     d  a  b  c
one  0  1  2  3
two  4  5  6  7

frame.sort_index()
Out[9]: 
     d  a  b  c
one  4  5  6  7
two  0  1  2  3

frame.sort_index(axis=1)
Out[11]: 
     a  b  c  d
two  1  2  3  0
one  5  6  7  4

frame.sort_index(axis=1,ascending=False)#Reverse sorting
Out[12]: 
     d  c  b  a
two  0  3  2  1
one  4  7  6  5

Assign a value to by and sort by the value of one or more rows and columns

f2 = DataFrame({'b':[4,7,-3,2],'a':[0,1,0,1]})
f2
Out[55]: 
   b  a
0  4  0
1  7  1
2 -3  0
3  2  1


f2.sort_index(by='b')
__main__:1: FutureWarning: by argument to sort_index is deprecated,
 please use .sort_values(by=...)
Out[59]: 
   b  a
2 -3  0
3  2  1
0  4  0
1  7  1

#It is recommended to use. sort_values(by =
f2.sort_values(by='b')
Out[60]: 
   b  a
2 -3  0
3  2  1
0  4  0
1  7  1

Sort by priority

f2.sort_values(by=['a','b'])
Out[61]: 
   b  a
2 -3  0
0  4  0
3  2  1
1  7  1

f2.sort_values(by=['b','a'])
Out[62]: 
   b  a
2 -3  0
3  2  1
0  4  0
1  7  1

Sort rows

f2.sort_values(by=[1],axis=1)
Out[64]: 
   a  b
0  0  4
1  1  7
2  0 -3
3  1  2

f2.sort_values(by=[1,2],axis=1)
Out[65]: 
   a  b
0  0  4
1  1  7
2  0 -3
3  1  2

Rank rank()

obj
Out[66]: 
0    7
1   -5
2    7
3    4
4    2
5    0
6    4
dtype: int64

obj.rank()
Out[67]: 
0    6.5
1    1.0
2    6.5
3    4.5
4    3.0
5    2.0
6    4.5
dtype: float64

obj.rank(method='min')
Out[69]: 
0    6.0
1    1.0
2    6.0
3    4.0
4    3.0
5    2.0
6    4.0
dtype: float64

obj.rank(method='max')
Out[70]: 
0    7.0
1    1.0
2    7.0
3    5.0
4    3.0
5    2.0
6    5.0
dtype: float64

obj.rank(method='first',ascending=False)
Out[71]: 
0    1.0
1    7.0
2    2.0
3    3.0
4    5.0
5    6.0
6    4.0
dtype: float64

Similarly, DataFrame can also rank in ranks