DataFrame.iat indexing with duplicate columns

[wavexx]

# error
In [27]: pd.DataFrame([[1, 1]], columns=['x','x']).iat[0,0]
TypeError: len() of unsized object

# ok
In [26]: pd.DataFrame([[1, 1]], columns=['x','y']).iat[0,0]
Out[26]: 1

I have some weird issue in a DataFrame I'm creating from a row-based array.
Using python3 and pandas 0.17.1 (from debian unstable), I get:

df = pandas.DataFrame(data=data[1:], columns=data[0])
df.iat[0, 0]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line 1555, in __getitem__
    return self.obj.get_value(*key, takeable=self._takeable)
  File "/usr/lib/python3/dist-packages/pandas/core/frame.py", line 1808, in get_value
    series = self._iget_item_cache(col)
  File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 1116, in _iget_item_cache
    lower = self.take(item, axis=self._info_axis_number, convert=True)
  File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 1371, in take
    convert=True, verify=True)
  File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3628, in take
    axis=axis, allow_dups=True)
  File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3510, in reindex_indexer
    indexer, fill_tuple=(fill_value,))
  File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3536, in _slice_take_blocks_ax0
    slice_or_indexer, self.shape[0], allow_fill=allow_fill)
  File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 4865, in _preprocess_slice_or_indexer
    return 'fancy', indexer, len(indexer)
TypeError: len() of unsized object

Interestingly, I can otherwise manage the dataframe just fine.
The same code, running under python2.7 shows no issue.

What could be the cause of such error?

[TomAugspurger]

data isn't defined. Can you post a copy-pastable example?

[wavexx]

The dataset was too big, and trying to reduce the example proved to be harder than I though (the dataset is merged over and over in several loops).

Any hint in what could cause this knowing the traceback? Or what to look for in the resulting df so that I can come up with an example?

[jreback]

pls post pd.show_versions()
show data.info() right before the call to .iat as well as data.head()

[wavexx]

INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-1-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.17.1
nose: 1.3.6
pip: None
setuptools: 18.7
Cython: 0.23.2
numpy: 1.9.2
scipy: 0.16.1
statsmodels: None
IPython: 2.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2012c
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.0rc2
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None

The dataset has too many columns (>2k) for data.head() to be of any use here.
I tried subsetting it while it still triggers the issue, and I now noticed this in data.info():

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1337 entries, 0 to 1336
Data columns (total 265 columns):
TYPE           1337 non-null object
               1337 non-null     object
    object
dtype: object
dtype: object
CODE           1337 non-null object
SAMPLE-TYP     1337 non-null object
C8:0           1337 non-null object
C14:0          1337 non-null object
C16:0          1337 non-null object
C17:0          1337 non-null object
C18:1          1337 non-null object
C18:0          1337 non-null object

Notice the first/second column types, are pasted here literally (looks like come memory corruption). Indeed, I also noticed now that if I break out of the loop with an exit() python segfaults...

[jreback]

it looks like you have an actual pandas object (DataFrame maybe) inside each cell in the TYPE column. not surprised it doesn't work then. This is theory is ok, except for indexing does not work very well with this.

show df.iloc[0] and df.iloc[0,0] (if they don't trigger errors)

[wavexx]

They work:

>>> data.iloc[0]
CODE                  XS05301
SAMPLE-TYP            Analyte
PLPE_PE_28:0%         2.29483
PLPE_16:0/16:1%    0.00195465
PLPE_16:0/16:0%    0.00357268
PLPE_16:0/18:3%    0.00302107
....

>>> data.iloc[0,0]
'XS05301'

[jreback]

show the TYPE columns as that the one with the issue (and do a type(data.iloc[....])

[wavexx]

It's just a constant string:

>>> data.TYPE.describe()
count      1337
unique        1
top       Tirol
freq       1337
Name: TYPE, dtype: object

I actually drop it later. To me it looks like it's the second column that could be potentially doubtful.

But I'll have to dig into this more closely, now that I noticed the segfault, and it's quite reproducible, there are a few modules (such as xlrd) from my test program that I can remove by going through a few more hoops.

[wavexx]

Turns out, data[0] (the header list) contains at one step several names which are identical.

[wavexx]

Indeed, that seem to be the only issue I had. By ensuring names are unique, I also don't have more segmentation faults.

Would it make sense to check the value provided directly in the constructor, or is it too expensive?

[jreback]

so can you post a short repro?

[wavexx]

data = pd.DataFrame([[1, 1]], columns=['x','x'])
data.iat[0,0]

[jreback]

@wavexx thanks!

# error
In [27]: pd.DataFrame([[1, 1]], columns=['x','x']).iat[0,0]
TypeError: len() of unsized object

# ok
In [26]: pd.DataFrame([[1, 1]], columns=['x','y']).iat[0,0]
Out[26]: 1