Divide One Pandas Dataframe by Another - Ignore index but respect columns

Question

I have 2 dataframes. I would like to broadcast a divide operation

df1= pd.DataFrame([[1.,2.,3.,4.], [5.,6.,7.,8.], [9.,10.,11.,12.]],
                  columns=['A','B','C','D'], index=['x','y','z'])

df2= pd.DataFrame([[0.,1.,2.,3.]], columns=['A','B','D','C'], index=['q'])

Notice that the columns are aligned slightly differently in df2.

I would like to divide df1 by df2 where the row is broadcast but the column labels are respected.

   A   B   C   D
x  1   2   3   4
y  5   6   7   8
z  9  10  11  12


   A  B  D  C
q  0  1  2  3

This would be wrong.

df1.values/df2.values

[[         inf   2.           1.5          1.33333333]
 [         inf   6.           3.5          2.66666667]
 [         inf  10.           5.5          4.        ]]

Answer I desire is:

   A    B   C      D
x  inf  2   1      2
y  inf  6   2.33   4
z  inf  10  3.66   6

Answer 1

If you divide by a Series (by selecting that one row of the second dataframe), pandas will align this series on the columns of the first dataframe, giving the desired result:

In [75]: df1 / df2.loc['q']
Out[75]:
     A   B         C  D
x  inf   2  1.000000  2
y  inf   6  2.333333  4
z  inf  10  3.666667  6

If you don't know/want to use the name of that one row, you can use squeeze to convert the one-column dataframe to a series: df1 / df2.squeeze() (see answer of @EdChum).

Answer 2

May be, you could order your df2 columns same of df1 and then divide on values

In [53]: df1.values/df2[df1.columns].values
Out[53]:
array([[         inf,   2.        ,   1.        ,   2.        ],
       [         inf,   6.        ,   2.33333333,   4.        ],
       [         inf,  10.        ,   3.66666667,   6.        ]])