Issue 1:
Code

In [33]: df = pd.DataFrame({'date1': [Timestamp('2017-05-25 14:02:23'), NaT],
'date2': [Timestamp('2017-05-25 14:34:43'), Timestamp('2017-05-16 19:37:43')]})

In [34]: df2 = df.apply(lambda x: x.dt.tz_localize('UTC'), axis=0)

In [35]: df3 = df2.assign(date1=df2.date2)

In [36]: df
Out[36]:
date1 date2
0 2017-05-25 14:02:23 2017-05-25 14:34:43
1 NaT 2017-05-16 19:37:43

In [37]: df2
Out[37]:
date1 date2
0 2017-05-25 14:02:23+00:00 2017-05-25 14:34:43+00:00
1 NaT 2017-05-16 19:37:43+00:00

In [38]: df3
Out[38]:
date1 date2
0 2017-05-25 14:34:43+00:00 2017-05-25 14:34:43+00:00
1 2017-05-16 19:37:43+00:00 2017-05-16 19:37:43+00:00

In [39]: df.dtypes
Out[39]:
date1 datetime64[ns]
date2 datetime64[ns]
dtype: object

In [40]: df2.dtypes
Out[40]:
date1 datetime64[ns, UTC]
date2 datetime64[ns, UTC]
dtype: object

In [41]: df3.dtypes
Out[41]:
date1 datetime64[ns, UTC]
date2 datetime64[ns, UTC]
dtype: object

In [42]: df.T
Out[42]:
0 1
date1 2017-05-25 14:02:23 NaT
date2 2017-05-25 14:34:43 2017-05-16 19:37:43

In [43]: df2.T
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-43-7d2317a1beae> in <module>()
----> 1 df2.T

e:\github\pandas\pandas\core\frame.pyc in transpose(self, *args, **kwargs)
1876 """Transpose index and columns"""
1877 nv.validate_transpose(args, dict())
-> 1878 return super(DataFrame, self).transpose(1, 0, **kwargs)
1879
1880 T = property(transpose)

e:\github\pandas\pandas\core\generic.pyc in transpose(self, *args, **kwargs)
600
601 nv.validate_transpose_for_generic(self, kwargs)
--> 602 return self._constructor(new_values, **new_axes).__finalize__(self)
603
604 def swapaxes(self, axis1, axis2, copy=True):

e:\github\pandas\pandas\core\frame.pyc in __init__(self, data, index, columns, dtype, copy)
350 else:
351 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 352 copy=copy)
353 elif isinstance(data, (list, types.GeneratorType)):
354 if isinstance(data, types.GeneratorType):

e:\github\pandas\pandas\core\frame.pyc in _init_ndarray(self, values, index, columns, dtype, copy)
522 values = maybe_infer_to_datetimelike(values)
523
--> 524 return create_block_manager_from_blocks([values], [columns, index])
525
526 @property

e:\github\pandas\pandas\core\internals.pyc in create_block_manager_from_blocks(blocks, axes)
4378 placement=slice(0, len(axes[0])))]
4379
-> 4380 mgr = BlockManager(blocks, axes)
4381 mgr._consolidate_inplace()
4382 return mgr

e:\github\pandas\pandas\core\internals.pyc in __init__(self, blocks, axes, do_integrity_check, fastpath)
2880 raise AssertionError('Number of Block dimensions (%d) '
2881 'must equal number of axes (%d)' %
-> 2882 (block.ndim, self.ndim))
2883
2884 if do_integrity_check:

AssertionError: Number of Block dimensions (1) must equal number of axes (2)

Problem description

Transpose on dataframe with timestamps columns work. But, if columns consist of timestamps with time-zone, it fails.

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.21.0.dev+413.g7f93d2d

pytest: 3.2.0

pip: 9.0.1

setuptools: 36.2.7

Cython: 0.24.1

numpy: 1.12.1

scipy: 0.19.1

pyarrow: None

xarray: None

IPython: 5.1.0

sphinx: 1.4.6

patsy: 0.4.1

dateutil: 2.5.3

pytz: 2017.2

blosc: None

bottleneck: 1.2.0

tables: 3.2.2

numexpr: 2.6.2

feather: None

matplotlib: 2.0.2

openpyxl: 2.3.2

xlrd: 1.0.0

xlwt: 1.1.2

xlsxwriter: 0.9.3

lxml: 3.6.4

bs4: 4.5.1

html5lib: 0.999999999

sqlalchemy: 1.0.13

pymysql: 0.7.9.None

psycopg2: 2.7.3.1 (dt dec pq3 ext lo64)

jinja2: 2.8

s3fs: None

fastparquet: None

pandas_gbq: None

pandas_datareader: None

Issue 2:
I have the following array of Timestamps:

ts_array = np.array([[Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T')],
[Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T')],
[Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T')]], dtype=object)

I can't create a DataFrame from this array using the DataFrame constructor:

pd.DataFrame(ts_array)

Traceback (most recent call last):
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2885, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-46-ae20c6b6248f>", line 1, in <module>
pd.DataFrame(ts_array)
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 255, in __init__
copy=copy)
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 432, in _init_ndarray
return create_block_manager_from_blocks([values], [columns, index])
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 3986, in create_block_manager_from_blocks
mgr = BlockManager(blocks, axes)
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 2591, in __init__
(block.ndim, self.ndim))
AssertionError: Number of Block dimensions (1) must equal number of axes (2)

I can create the DataFrame from the array using from_records:

ts_df = pd.DataFrame.from_records(ts_array)

However, when I attempt to transpose this DataFrame, I wind up with the same AssertionError as before.

AssertionError: Number of Block dimensions (1) must equal number of axes (2)

If I convert the Timestamps to Datetimes, the error persists. I can, however, convert the Timestamps to Datetime64 objects, and this fixes the problem.

dt64_array = np.array([[ts.to_datetime64() for ts in sublist] for sublist in ts_array])
pd.DataFrame(dt64_array)

Out[56]:
0 1 2
0 2016-05-02 15:50:00 2016-05-02 15:50:00 2016-05-02 15:50:00
1 2016-05-02 17:10:00 2016-05-02 17:10:00 2016-05-02 17:10:00
2 2016-05-02 20:25:00 2016-05-02 20:25:00 2016-05-02 20:25:00

pd.DataFrame(dt64_array).transpose()

Out[57]:
0 1 2
0 2016-05-02 15:50:00 2016-05-02 17:10:00 2016-05-02 20:25:00
1 2016-05-02 15:50:00 2016-05-02 17:10:00 2016-05-02 20:25:00
2 2016-05-02 15:50:00 2016-05-02 17:10:00 2016-05-02 20:25:00

Though I found a suitable workaround, I feel like pandas should be able to construct and operate on DataFrames of Timestamps as easily as other other objects.

output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.8.0.dev0+970e99e
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None