I have a dataframe of metadata represented as `DF_0` and then a dataframe of actual data (`DF_1`). I want to use `pd.concat` on my dataframes but not with respect to indices. The indices of `DF_0` are arbitrary and the indices of `DF_1` correspond to the values in `attr_1` of `DF_0`. 

The only ways I could think of doing it were: 

(1) to make `attr_1` my index then merge which I'd rather not do; or 

(2) sort and then merge [but missing data might mess this up]. I feel like `pandas` has thought of this.

**Does anyone know of a way to use `pd.concat` or something similar [my real data has strings, floats, ints] to merge 2 [or more?] dataframes along the "axis" represented by the values of a particular column in a particular dataframe?** 

The values may or not be in order. Are the ways I described above the only ways? They seem messy...

```
DF_0 = pd.DataFrame(np.arange(15).reshape(5,3), 
                    columns=["attr_%d"%j for j in range(3)])
#    attr_0  attr_1  attr_2
# 0       0       1       2
# 1       3       4       5
# 2       6       7       8
# 3       9      10      11
# 4      12      13      14

DF_1 = pd.DataFrame([[0,1,0,1,1],[0,0,0,1,0],[1,1,1,0,1]], 
                     index = ["other_%d"%j for j in range(3)], 
                     columns = [1,4,7,10,13]).T
#     other_0  other_1  other_2
# 1         0        0        1
# 4         1        0        1
# 7         0        0        1
# 10        1        1        0
# 13        1        0        1

# What I want
DF_X = pd.DataFrame(
                    np.concatenate([DF_0.as_matrix(), DF_1.as_matrix()], axis=1), 
                    columns=list(DF_0.columns) + list(DF_1.columns))
#    attr_0  attr_1  attr_2  other_0  other_1  other_2
# 0       0       1       2        0        0        1
# 1       3       4       5        1        0        1
# 2       6       7       8        0        0        1
# 3       9      10      11        1        1        0
# 4      12      13      14        1        0        1

```