Issue 1:
Unintended behaviour of pandas happens when one tries to create a series applying

tuple (or list) to two columns of a dataframe, one of which consists of timestamps:

import pandas as pd
import numpy as np
d = pd.DataFrame({'a': pd.Series(np.random.randn(4)),
'b': ['a', 'list', 'of', 'words'],
'ts': pd.date_range('2016-10-01', periods=4, freq='H')})

d

a
b
ts

0
0.200813
a
2016-10-01 00:00:00

1
0.316971
list
2016-10-01 01:00:00

2
-0.186392
of
2016-10-01 02:00:00

3
-0.565593
words
2016-10-01 03:00:00

let's try first with columns 'a'and 'b':

d[['a', 'b']].apply(tuple, axis=1)

0 (0.2008128669491346, a)
1 (0.3169711841447721, list)
2 (-0.1863916899789735, of)
3 (-0.5655926199699992, words)
dtype: object

So far, everything is fine. Now let's do it with 'a' and 'ts':

d[['a', 'ts']].apply(tuple, axis=1)

a
ts

0
0.200813
2016-10-01 00:00:00

1
0.316971
2016-10-01 01:00:00

2
-0.186392
2016-10-01 02:00:00

3
-0.565593
2016-10-01 03:00:00

Oops.

It's easy to find a way around this, by coating the timestamps before apply and uncoating after:

def coating(t):
return lambda: t

def uncoating(x, f):
return x, f()

d['coated_ts'] = d['ts'].apply(coating)

d[['a', 'coated_ts']].apply(tuple, axis=1).apply(lambda t: uncoating(*t))

0 (0.2008128669491346, 2016-10-01 00:00:00)
1 (0.3169711841447721, 2016-10-01 01:00:00)
2 (-0.1863916899789735, 2016-10-01 02:00:00)
3 (-0.5655926199699992, 2016-10-01 03:00:00)
dtype: object

It would be nice if this strange behaviour was corrected.

pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

Issue 2:
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