wierd behavior using apply() creating list based on current columns

[STguerin]

Code Sample, a copy-pastable example if possible

d=[['hello',1,'GOOD','long.kw'],
   [1.2,'chipotle',np.nan,'bingo'],
   ['various',np.nan,3000,123.456]]                                                    
t=pd.DataFrame(data=d, columns=['A','B','C','D']) 
t['combined'] = t.apply(lambda x: list([x['A'], x['B'],  x['C'], x['D']]),axis=1)    

Problem description

[I am confuse why this is not working properly, if I initiate the 'combined' columns first to 0 first, it works. I understand that this is a sub-optimal approach but I am just wondering why is this breaking up]

Expected Output

t['combined'] = t.values.tolist()
t
Out[80]: 
         A         B     C        D                       combined
0    hello         1  GOOD  long.kw      [hello, 1, GOOD, long.kw]
1     1.20  chipotle   NaN    bingo    [1.2, chipotle, nan, bingo]
2  various       NaN  3000   123.46  [various, nan, 3000, 123.456]

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.0.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.11.3 scipy: 0.18.1 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.5.1 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: 1.2.0 tables: 3.2.2 numexpr: 2.6.1 matplotlib: 2.0.0 openpyxl: 2.4.1 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.6 lxml: 3.7.2 bs4: 4.5.3 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.1.5 pymysql: None psycopg2: None jinja2: 2.9.4 boto: 2.45.0 pandas_datareader: None

[TomAugspurger]

See #15628 and issues linking to / from that.

The short-version is DataFrame.apply tries to infer an output based on the result. The result of your output is inferred to be a DataFrame with the same columns.

You're probably better off with something like

In [51]: pd.Series([list(x) for x in t.itertuples(index=False)])
Out[51]:
0        [hello, 1, GOOD, long.kw]
1      [1.2, chipotle, nan, bingo]
2    [various, nan, 3000, 123.456]
dtype: object