BUG: iloc fills multiple columns, if columns have duplicate names

[henhuy]

Creating a DataFrame with two columns given duplicate names, changing data via indexer changes both columns:

a = pd.DataFrame(index=['a', 'b', 'c'], columns=['d', 'e', 'd']).fillna(0)
a.iloc[:, 0]['a'] = 3

Gives:
d e d
a 3 0 3
b 0 0 0
c 0 0 0

Instead, it should only edit first column:
d e d
a 3 0 0
b 0 0 0
c 0 0 0

INSTALLED VERSIONS

commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-68-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8

pandas: 0.17.1
nose: 1.3.6
pip: 1.5.4
setuptools: 3.3
Cython: None
numpy: 1.10.1
scipy: 0.13.3
statsmodels: None
IPython: 3.1.0
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: 0.8.0
xlsxwriter: 0.7.7
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 1.0.4
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None

[jreback]

This is a tricky bug, though you are chained indexing here, so no guarantees.

Instead do this.

In [7]: a.iloc[a.index.get_loc('a'), 0] = 3

In [8]: a
Out[8]: 
   d  e  d
a  3  0  0
b  0  0  0
c  0  0  0

[henhuy]

That's what I did right now - thanks!

[toobaz]

A "more licit" example of the bug in action:

In [4]: df1 = DataFrame([{'A': None, 'B': 1}, {'A': 2, 'B': 2}])
   ...: df2 = DataFrame([{'A': 3, 'B': 3}, {'A': 4, 'B': 4}])
   ...: df = concat([df1, df2], axis=1)
   ...: df.iloc[0, 0] = 15
   ...: df
   ...: 
Out[4]: 
      A  B     A  B
0  15.0  1  15.0  3
1   2.0  2   4.0  4

[toobaz]

A "more licit" example of the bug in action:

For reference: it is actually not the same bug, since it is fixed by #17163 , while the original example by the submitter is not.

I still think, however, that there is some way to expose the (original) bug without chained assigning.

[MohakGangwani]

Wouldn't it be better if pandas didn't allow us to have duplicate column names ? Having columns with same name would rather create confusion for us.