DataFrame.loc[] returns inconsistent types depending on row count
If a dataframe has a single row for a given index entry, it returns a Series. If it
has two rows for that index, it returns a DataFrame. I believe that it should return a
DataFrame in either case for consistency.

Image attached, small dataframe and notebook exhibiting the problem attached. OK, so
I can't attach either the dataframe or the notebook, even suffixing them with .txt (github barfs).
So I'm pasting the text fragment after the image..

<img width="959" alt="pandasinconsistenttypes" src="https://cloud.githubusercontent.com/assets/1727968/10252096/e0e7ed72-68fa-11e5-809c-a125c44a8fbf.png">

dataframe = '''\
Locus,Decision,Group,Var,Region,Gene,Rows,Mutation,Profile
chr01:0018961727,Homopolymer,VS,CA,exonic,PAX7,1.0,synonymous SNV,000000010010001000000000001101
chr01:0027057772,Bad,IR-PM-VS,CA,exonic,ARID1A,1.0,nonsynonymous SNV,000000000000000000001000100000
chr01:0027057772,Bad,IR-PM-VS,CA,exonic,ARID1A,1.0,nonsynonymous SNV,000000000000000000011001110100
chr01:0027057772,Bad,IR-PM-VS,CA,exonic,ARID1A,1.0,nonsynonymous SNV,100000000001010000010001110110\
'''
df = [line.split(',') for line in txt.split('\n')]
tdf = pd.DataFrame.from_records(df, index=(0,))
tdf
type(tdf.loc['chr01:0018961727']), type(tdf.loc['chr01:0027057772'])
