BUG in MultiIndex truncated repr with integer level names

[KevinBaudin]

Reproducible example:

In [10]: df = pd.DataFrame({'col': range(9)}, index=pd.MultiIndex.from_product([['A0', 'A1', 'A2'], ['B0', 'B1', 'B2']], names=[1,2]))

In [11]: df
Out[11]: 
       col
1  2      
A0 B0    0
   B1    1
   B2    2
A1 B0    3
   B1    4
   B2    5
A2 B0    6
   B1    7
   B2    8

In [12]: pd.options.display.max_rows = 4

In [13]: df
Out[13]: 
       col
1  2      
A0 A0    0
   A0    1
...    ...
A2 A2    7
   A2    8

[9 rows x 1 columns]

So the truncated repr shows incorrectly the first index level (with integer level name 1) again for the second level.

---

Original post:

Code Sample, a copy-pastable example if possible

import wget
import pandas
import pickle

url = 'https://www.dropbox.com/s/aldllo0bi3m3wkl/stock?dl=1'
filename = wget.download(url)
df = pickle.load(open(filename))
df 
#bad display, index duplicated?
df.head() 
#expected display

Problem description

		merged
1	2
a.	a.	2
abel	abel	1
agnes	agnes	2
alain	alain	8
	alain	2

I have created a multi-index based on 2 columns .
Those two columns wont appear properly, index_column "2" being duplicated from "1"
When displaying up to the 60th first rows of dataframe, it's fine, then it duplicates again the column 1 in the column 2.

Expected Output

		merged
1	2
a.	masson-dubois	2
abel	pinchard	1
agnes	paquet	2
alain	corcia	8
	hudelot-noellat	2

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 2.7.9.final.0 python-bits: 64 OS: Linux OS-release: 4.9.4-moby machine: x86_64 processor: byteorder: little LC_ALL: None LANG: None LOCALE: None.None

pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.2
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None

[jreback]

pls show a copy-pastable example that doesn't rely on opening your file

[KevinBaudin]

Sorry, I didn't manage to find a simple reproducible example.
The problem only appeared on this particular dataframe at the moment.

PS : It's only a 22Kb file

[jreback]

this is not reproducible. you can try .sort_index()

[adrtod]

I could reproduce it but .sort_index() did not work.
The data seem not corrupted, only displaying is wrong

[jorisvandenbossche]

@KevinBaudin The file is not available anymore?

[KevinBaudin]

@jorisvandenbossche edited with new link, sorry.

[jorisvandenbossche]

@KevinBaudin The cause of the issue are the index level names ([1, 2]). If you set those to something else, you will see that the issue is resolved:

In [6]: df
Out[6]: 
                                 merged
1               2                      
a.              a.                    2
abel            abel                  1
agnes           agnes                 2
...
[100 rows x 1 columns]

In [7]: df.index.names = ['a', 'b']

In [8]: df
Out[8]: 
                                   merged
a               b                        
a.              masson-dubois           2
abel            pinchard                1
agnes           paquet                  2
...
[100 rows x 1 columns]

The reason for this is the integer level names (confusion between integer number of first (0) or second (1) level, or the level names (1 and 2)).
So it seems that the repr used in .head() is correctly dealing with this distinction, but the general repr not.

[jorisvandenbossche]

Smaller reproducible example:

In [10]: df = pd.DataFrame({'col': range(9)}, index=pd.MultiIndex.from_product([['A0', 'A1', 'A2'], ['B0', 'B1', 'B2']], names=[1,2]))

In [11]: df
Out[11]: 
       col
1  2      
A0 B0    0
   B1    1
   B2    2
A1 B0    3
   B1    4
   B2    5
A2 B0    6
   B1    7
   B2    8

In [12]: pd.options.display.max_rows = 4

In [13]: df
Out[13]: 
       col
1  2      
A0 A0    0
   A0    1
...    ...
A2 A2    7
   A2    8

[9 rows x 1 columns]

So it is the truncated repr that has this issue.