Panel slice assigning does not reindex correctly?

[CRP]

Suppose I have

a=Panel(randn(3,10,2))
b=Series(randn(10))
b.sort()

now the index of b has a different order, yet same values as the major_index of a

if I do

a.ix[0,:,0]=b

the slice of a gets assigned the values of b without reindexing first, but somehow the index is till kept track of:

b
Out[220]: 
4   -2.465417
8   -2.202345
3   -1.173703
5   -0.802436
7   -0.307515
0   -0.045943
1    0.155771
6    0.367755
9    0.458332
2    1.629641

a.ix[0,:,0]=b

a.ix[0,:,0]
Out[223]: 
0   -2.465417
1   -2.202345
2   -1.173703
3   -0.802436
4   -0.307515
5   -0.045943
6    0.155771
7    0.367755
8    0.458332
9    1.629641

a.ix[0,:,0]==b
Out[224]: 
0    True
1    True
2    True
3    True
4    True
5    True
6    True
7    True
8    True
9    True

[changhiskhan]

Actually this is a problem for DataFrame as well:

In [43]: df = DataFrame(np.random.randn(len(b), 4), columns=['A', 'B', 'C', 'D'])

In [44]: df
Out[44]: 
          A         B         C         D
0 -1.784259 -0.523289  0.066058  0.416523
1 -1.592496  1.191418  0.504262  0.508421
2 -0.566371  0.228974  0.516623  0.209813
3  1.447583  0.808516 -0.764518  0.231316
4  1.058431 -0.061739 -0.936624  1.220623
5  1.794763  0.311278 -0.101091 -2.709552
6 -0.602857  0.157062 -2.161062  0.783041
7  0.683989  0.421005 -0.900611  0.862070
8  0.287105  0.220197 -1.467609  1.256840
9  1.050565  0.240500 -0.232877  0.230123

In [45]: df.ix[:, 0] = b

In [46]: df
Out[46]: 
          A         B         C         D
0 -0.434466 -0.523289  0.066058  0.416523
1  0.426553  1.191418  0.504262  0.508421
2  0.486326  0.228974  0.516623  0.209813
3  0.599647  0.808516 -0.764518  0.231316
4  0.664624 -0.061739 -0.936624  1.220623
5  0.719194  0.311278 -0.101091 -2.709552
6  0.857904  0.157062 -2.161062  0.783041
7  1.360497  0.421005 -0.900611  0.862070
8  2.037406  0.220197 -1.467609  1.256840
9  2.350040  0.240500 -0.232877  0.230123

In [47]: b
Out[47]: 
9   -0.434466
5    0.426553
1    0.486326
8    0.599647
4    0.664624
7    0.719194
0    0.857904
3    1.360497
2    2.037406
6    2.350040

[wesm]

This is great. Thanks, Chang