[jira] [Created] (ARROW-10919) Wrong values with Table slicing and conversion to/From pandas ExtensionArray

["Adrien Hoarau (Jira)" <ji...@apache.org>]

Adrien Hoarau created ARROW-10919:
-------------------------------------

             Summary: Wrong values with Table slicing and conversion to/From pandas ExtensionArray
                 Key: ARROW-10919
                 URL: https://issues.apache.org/jira/browse/ARROW-10919
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
    Affects Versions: 2.0.0
         Environment: INSTALLED VERSIONS
------------------
commit           : b5958ee1999e9aead1938c0bba2b674378807b3d
python           : 3.8.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-58-generic
Version          : #64-Ubuntu SMP Wed Dec 9 08:16:25 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8
pandas           : 1.1.5
numpy            : 1.19.4
pytz             : 2020.4
dateutil         : 2.8.1
pip              : 20.2.1
setuptools       : 49.2.1
Cython           : None
pytest           : 5.4.3
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.4
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 2.0.0
pytables         : None
pyxlsb           : None
s3fs             : 0.4.2
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

            Reporter: Adrien Hoarau
         Attachments: Screenshot from 2020-12-15 13-28-38.png

 
{code:java}
import pandas as pd
from pyarrow import Table

df = pd.DataFrame({'int_na': [0, None, 2, 3, None, 5, 6, None, 8]}, dtype=pd.Int64Dtype())
print(df)
{code}
    int_na

0 0 
1 <NA>
 2 2 
3 3 
4 <NA>
 5 5 
6 6 
7 <NA> 
8 8
{code:java}
Table.from_pandas(df).slice(2, None).to_pandas()
{code}
  int_na
0 2
1 <NA>
2 1
3 5
4 <NA>
5 1
6 8



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