Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np
arr = np.array([[1,2,3],[4,5,6]])
df = pd.DataFrame(arr)

Problem description

the expected output is arr[0][1] equals df[0][1],but the real thing is that two elems aren't equal in value.I have seen the source code,a code line as following

#other code
value = value.T
/#other code

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 2.7.11.final.0 python-bits: 64 OS: Darwin OS-release: 14.5.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: zh_CN.UTF-8 LOCALE: None.None pandas: 0.19.2 nose: None pip: 9.0.1 setuptools: 29.0.1 Cython: 0.24 numpy: 1.12.1 scipy: 0.17.0 statsmodels: 0.6.1 xarray: None IPython: 4.1.2 sphinx: None patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: 2.6.1 matplotlib: 1.5.1 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: 3.6.0 bs4: 4.4.1 html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.8 boto: None pandas_datareader: None **Comment From: jreback** 
In [19]: df
Out[19]: 
   0  1  2
0  1  2  3
1  4  5  6

In [20]: arr
Out[20]: 
array([[1, 2, 3],
       [4, 5, 6]])
df[0] selects a *column* of a DataFrame. This is by far a more common operation, whereas ``arr[0]`` is a row-operation. you can use ``df.loc[0]`` to achieve a row operation if you'd like. pandas has multiple ways of indexing (via positions or labels) and operators for those. the docs are pretty [complete](http://pandas.pydata.org/pandas-docs/stable/indexing.html)