BUG: read multi-index column csv with index_col=False borks

[jreback]

http://stackoverflow.com/questions/21318865/read-multi-index-on-the-columns-from-csv-file

[hayd]

For convenience, here's test case:

from StringIO import StringIO
s1 = '''Male, Male, Male, Female, Female
R, R, L, R, R
.86, .67, .88, .78, .81'''

s2 = '''Male, Male, Male, Female, Female
R, R, L, R, R
.86, .67, .88, .78, .81
.86, .67, .88, .78, .82'''

In [11]: pd.read_csv(StringIO(s1), header=[0, 1])
Out[11]: 
Empty DataFrame
Columns: [(Male, R), ( Male,  R), ( Male,  L), ( Female,  R), ( Female,  R)]
Index: []

In [12]: pd.read_csv(StringIO(s2), header=[0, 1])
Out[12]: 
   (Male, R)  ( Male,  R)  ( Male,  L)  ( Female,  R)  ( Female,  R)
0       0.86         0.67         0.88           0.82           0.82

seems to skip first row after header.

Note: columns tuplized as wanted to see if this was also a bug in 0.12.

[jreback]

@hayd could try tupleize_cols=True and and see if it works

[TomAugspurger]

Mine is skipping all the subsequent rows (s1 and s2 are from Andy's example):

In [5]: pd.read_csv(StringIO(s1), header=[0, 1])
Out[5]: 
Empty DataFrame
Columns: [(Male, R), ( Male,  R), ( Male,  L), ( Female,  R), ( Female,  R)]
Index: []

[0 rows x 5 columns]

In [6]: pd.read_csv(StringIO(s2), header=[0, 1])
Out[6]: 
Empty DataFrame
Columns: [(Male, R), ( Male,  R), ( Male,  L), ( Female,  R), ( Female,  R)]
Index: []

[0 rows x 5 columns]

[hayd]

@jreback maybe was just being thick about tuplize columns (forgot repr of mi) is working fine, OT though.

There is a change in 0.12 and 0.13. I see what @TomAugspurger sees in 0.13.

[jreback]

The problem is that it is confused by the lack of an index_col I think; specify index_col=0 actually works (but kills the first value....)

[hayd]

Seems like the column after the header is being used for the naming of the index?

In [11]: pd.read_csv(StringIO(s1), header=[0, 1], index_col=0)
Out[11]: 
Empty DataFrame
Columns: [( Male,  R), ( Male,  L), ( Female,  R), ( Female,  R)]
Index: []

In [12]: pd.read_csv(StringIO(s2), header=[0, 1], index_col=0)
Out[12]: 
      ( Male,  R)  ( Male,  L)  ( Female,  R)  ( Female,  R)
.86                                                         
0.86         0.67         0.88           0.82           0.82

[jreback]

@hayd yes if it can, but this is where the index_col matters, it is a heuristic (and maybe wrong in this case)

[waitingkuo]

Seems the problem is caused by the duplicated columns ( Female, R). If you modify the second row to a, b, c, d, e, the function works normally. Is it a bug? Or should we throw some exception while there're duplicated multi-columns?

[jreback]

@waitingkuo hmm a duplicated multi index is technically valid (prob not tested very well though)
I think this my be related to index_col - it basically has to try to guess if their are names present or not

want to dig in?

[waitingkuo]

For duplicated single column, some sequence numbers would be append:

In [13]: pd.read_csv(StringIO('R,R,L,R,R\n1,2,3,4,5'))
Out[13]: 
   R  R.1  L  R.2  R.3
0  1    2  3    4    5

[1 rows x 5 columns]

According to this logic, the multi-column one

Male,Male,Male,Female,Female
R,R,L,R,R

should be converted to

Male,Male,Male,Female,Female
R,R.1,L,R,R.1

Does it make sense?

[hayd]

That looks correct. However there is also a flag for this, mangle_dupe_cols:

In [7]: pd.read_csv(StringIO('R,R,L,R,R\n1,2,3,4,5'), mangle_dupe_cols=False)
Out[7]: 
   R  R  L  R  R
0  5  5  3  5  5

[1 rows x 5 columns]

[hayd]

Well... er that's a bug!

[waitingkuo]

Things also go wrong when we set header as a list

In [4]: pd.read_csv(StringIO('R,R,L,R,R\n1,2,3,4,5'), header=[0])
Out[4]:  
Empty DataFrame
Columns: [R, R, L, R, R]
Index: []

[0 rows x 5 columns]

[waitingkuo]

I've figured out the problem and fixed it in python2. However, I got stuck in python3. Can anyone who have experience in python3 give me a hand?

My commit
waitingkuo@b969e96

My Travis Failed build
https://travis-ci.org/waitingkuo/pandas/jobs/17788195

[jreback]

use lzip instead of zip
it's imported from pandas.compat