目前无法通过直接调用 sort_values 来完成此操作.有关于它的一个公开的错误报告.

通过转置、按列排序，然后再次转置，您仍然可以做得不太好:

>>>ct.T.sort_values([1, 2, 3]).Ty b c aX1 0 1 22 1 0 03 2 0 0

For example,

df = pd.DataFrame({'x':[1,1,1,2,3,3], 'y':['a','a','c','b','b','b']})
ct = pd.crosstab(df.x, df.y)
ct
y  a  b  c
x         
1  2  0  1
2  0  1  0
3  0  2  0

How do I sort the columns of ct based on the values in row1, row2, and row3 (in that order of priority)?

I've tried the following, neither of which work

ct.sort_values([1, 2, 3], axis=1)
ct.sort_values(['1','2','3'], axis=1)

This cannot currently be done with a direct call to sort_values. There is an open bug report about it.

You can still do it less nicely by transposing, sorting by columns, then transposing again:

>>> ct.T.sort_values([1, 2, 3]).T
y  b  c  a
x         
1  0  1  2
2  1  0  0
3  2  0  0