DataFrame.sort_values(by=‘##',axis=0,ascending=True, inplace=False, na_position=‘last')

#利用字典dict创建数据框
import numpy as np
import pandas as pd
df=pd.DataFrame({'col1':['A','A','B',np.nan,'D','C'],
         'col2':[2,1,9,8,7,7],
         'col3':[0,1,9,4,2,8]
})
print(df)

>
 col1 col2 col3
0  A   2   0
1  A   1   1
2  B   9   9
3 NaN   8   4
4  D   7   2
5  C   7   8

#依据第一列排序，并将该列空值放在首位
print(df.sort_values(by=['col1'],na_position='first'))
>
 col1 col2 col3
3 NaN   8   4
0  A   2   0
1  A   1   1
2  B   9   9
5  C   7   8
4  D   7   2

#依据第二、三列，数值降序排序
print(df.sort_values(by=['col2','col3'],ascending=False))
>
 col1 col2 col3
2  B   9   9
3 NaN   8   4
5  C   7   8
4  D   7   2
0  A   2   0
1  A   1   1

#根据第一列中数值排序，按降序排列，并替换原数据
df.sort_values(by=['col1'],ascending=False,inplace=True,
           na_position='first')
print(df)
>
 col1 col2 col3
3 NaN   8   4
4  D   7   2
5  C   7   8
2  B   9   9
1  A   1   1
0  A   2   0

x = pd.DataFrame({'x1':[1,2,2,3],'x2':[4,3,2,1],'x3':[3,2,4,1]}) 
print(x)
#按照索引值为0的行，即第一行的值来降序排序
print(x.sort_values(by =0,ascending=False,axis=1))
>
  x1 x2 x3
0  1  4  3
1  2  3  2
2  2  2  4
3  3  1  1
  x2 x3 x1
0  4  3  1
1  3  2  2
2  2  4  2
3  1  1  3