Question

我有一个用pandas组成的数据框，如下所示：

        a  b  c  d  e  f  g  h  i  j  k  l  m  n  o
    1   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
    2   0  0  0  0  0  0  1  0  1  0  1  0  0  1  0
    3   0  1  0  0  1  0  1  0  1  0  1  0  0  1  0
    4   0  0  0  0  0  1  0  1  0  0  1  0  0  1  0
    5   0  0  1  1  0  1  0  1  0  0  1  0  0  1  0
    6   1  0  0  1  0  1  0  1  0  0  1  0  0  1  0
    7   0  0  0  0  0  0  0  0  0  0  0  0  0  1  0
    8   0  0  0  0  0  0  0  0  1  0  1  0  0  1  0
    9   0  0  0  0  0  0  0  0  0  0  0  1  1  1  0
    10  0  0  0  1  0  1  0  1  0  0  1  0  0  1  0
    11  0  0  0  0  0  0  0  1  0  0  1  0  0  1  0
    12  0  0  0  0  1  0  1  0  1  0  1  0  0  1  0
    13  0  0  0  0  0  0  0  0  0  0  1  0  0  1  0
    14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1
    15  0  0  0  0  1  0  1  0  1  0  1  0  0  1  0
    16  0  0  0  0  0  0  0  0  0  0  0  1  0  1  0

我想对行进行排序，以便按降序排序。行的值是通过组合列形成的数字。例如，第1行是000000000000000，第2行是000000101010010.最终结果应该将第6行作为第一行，将第1行作为最后一行。我试过了

    dat.sort_values(by=['a'], ascending=False, axis=0)

但这仅在第一列中排序。还有其他方法可以重新排序行吗？

Answer 1

按当前顺序排列所有列：

df.sort_values(by=df.columns.tolist(), ascending=False)

#    a  b  c  d  e  f  g  h  i  j  k  l  m  n  o
#6   1  0  0  1  0  1  0  1  0  0  1  0  0  1  0
#3   0  1  0  0  1  0  1  0  1  0  1  0  0  1  0
#5   0  0  1  1  0  1  0  1  0  0  1  0  0  1  0
#10  0  0  0  1  0  1  0  1  0  0  1  0  0  1  0
#12  0  0  0  0  1  0  1  0  1  0  1  0  0  1  0
#15  0  0  0  0  1  0  1  0  1  0  1  0  0  1  0
#4   0  0  0  0  0  1  0  1  0  0  1  0  0  1  0
#2   0  0  0  0  0  0  1  0  1  0  1  0  0  1  0
#11  0  0  0  0  0  0  0  1  0  0  1  0  0  1  0
#8   0  0  0  0  0  0  0  0  1  0  1  0  0  1  0
#13  0  0  0  0  0  0  0  0  0  0  1  0  0  1  0
#9   0  0  0  0  0  0  0  0  0  0  0  1  1  1  0
#16  0  0  0  0  0  0  0  0  0  0  0  1  0  1  0
#7   0  0  0  0  0  0  0  0  0  0  0  0  0  1  0
#14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1
#1   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0

Answer 2

使用键进行排序

df.loc[df.astype(str).sum(1).sort_values(ascending=False).index]
Out[871]: 
    a  b  c  d  e  f  g  h  i  j  k  l  m  n  o
6   1  0  0  1  0  1  0  1  0  0  1  0  0  1  0
3   0  1  0  0  1  0  1  0  1  0  1  0  0  1  0
5   0  0  1  1  0  1  0  1  0  0  1  0  0  1  0
10  0  0  0  1  0  1  0  1  0  0  1  0  0  1  0
15  0  0  0  0  1  0  1  0  1  0  1  0  0  1  0
12  0  0  0  0  1  0  1  0  1  0  1  0  0  1  0
4   0  0  0  0  0  1  0  1  0  0  1  0  0  1  0
2   0  0  0  0  0  0  1  0  1  0  1  0  0  1  0
11  0  0  0  0  0  0  0  1  0  0  1  0  0  1  0
8   0  0  0  0  0  0  0  0  1  0  1  0  0  1  0
13  0  0  0  0  0  0  0  0  0  0  1  0  0  1  0
9   0  0  0  0  0  0  0  0  0  0  0  1  1  1  0
16  0  0  0  0  0  0  0  0  0  0  0  1  0  1  0
7   0  0  0  0  0  0  0  0  0  0  0  0  0  1  0
14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1
1   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0

根据Python中组合列形成的数字重新排序行

2 个答案: