I'm currently in the middle of transition to Pandas DataFrames from the various Numpy arrays. This has been relatively painless since Pandas, AFAIK, is built largely on top of Numpy. What I mean by that is that .mean(), .sum() etc all work as you would hope. On top of that, the ability to add a hierarchical index and use the .ix[] (index) attribute and .xs() (cross-section) method to pull out arbitray pieces of the data has greatly improved the readability and performance of my code (mainly by reducing the number of round-trips to my database).

One thing I haven't fully investigated yet is Pandas compatibility with the more advanced functionality of Scipy and Matplotlib. However, in case of any issues, it's easy enough to pull out a single column that behaves enough like an array for those libraries to work, or even convert to an array on the fly. A DataFrame's plotting methods, for instance, rely on matplotlib and take care of any conversion for you.

Also, if you're like me and your main use of Scipy is the statistics module, pystatsmodels is quickly maturing and relies heavily on pandas.

That's my two cents' worth

Assuming you've done import pandas as pd already:

df = pd.DataFrame(test) # converts your array to a DataFrame.
df = df.set_index('f0') # changes the index to be the first column.

You can do (assuming you know, that column B is the one to be expanded, you can iterate over dtype if you need to automate it further- to get the ones of compound type)

df=pd.DataFrame.from_records(map(lambda x: list(x), arr), columns=arr.dtype.names)
df2=pd.DataFrame(df["B"].tolist())
df2.columns=map(lambda x: f"B_{x+1}", df2.columns)

df=pd.concat([df, df2], sort=False, axis=1).drop(columns="B")

Outputs:

   A  C  B_1  B_2  B_3
0  1  6  5.0  3.0  7.0
1  2  9  2.0  1.0  3.0
2  3  3  3.0  8.0  4.0
3  4  2  1.0  7.0  4.0