BUG: Incorrect localization of naive time string with Series and datetime[ns, tz] dtype

[mroeschke]

In [2]: ts_string = '2013-01-01 00:00:00' 

In [3]: pd.Series([ts_string], dtype='datetime64[ns, CET]')
Out[3]: 
0   2013-01-01 01:00:00+01:00
dtype: datetime64[ns, CET]

In [4]: pd.Series([pd.Timestamp(ts_string, tz='CET')])
Out[4]: 
0   2013-01-01 00:00:00+01:00
dtype: datetime64[ns, CET]

Problem description

It appears that a Series will localize a naive string to UTC first and then convert it to the specified timezone specified in dtype instead of immediately localizing the Series to the timezone.

Note: It looks to work correctly for numeric timestamps:

In [11]: ts = 1.5e15

In [12]: pd.Series([ts], dtype='datetime64[ns, CET]')
Out[12]: 
0   1970-01-18 09:40:00+01:00
dtype: datetime64[ns, CET]

In [13]: pd.Series([pd.Timestamp(ts, tz='CET')])
Out[13]: 
0   1970-01-18 09:40:00+01:00
dtype: datetime64[ns, CET]

Expected Output

Out[4]: 
0   2013-01-01 00:00:00+01:00

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: 0d676a3ccf1d7aa986416a7488b941496f936d98
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.21.0.dev+405.g0d676a3
pytest: 3.2.1
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.26
numpy: 1.13.1
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.8
lxml: 3.8.0
bs4: 4.3.2
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: 0.7.9.None
psycopg2: 2.7.1 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[gfyoung]

Weird and buggy...nice catch! PR to patch is welcome!

[mroeschke]

Looks like the culprit is here. Not completely sure why the comment mentions things have to be UTC at this point but will attempt to change and see if anything breaks.