Code Sample, a copy-pastable example if possible

import pandas as pd

ts = pd.Series([pd.Timestamp('2017-07-31 20:08:46.110998-04:00'), 
                pd.Timestamp('2017-08-01 20:08:46.110998-04:00'), 
                pd.Timestamp('2017-08-02 20:08:46.110998-04:00')])

def func(elem):
    print(type(elem))
    return elem
print(type(ts))
print(type(ts[0]))

ts.apply(func);

# Prints out:
# <class 'pandas.core.series.Series'>
# <class 'pandas._libs.tslib.Timestamp'>
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>

Problem description

I have a Series with Timestamps as values rather than the index. I expect the apply method to be called on each element, but it is not, rather it gets called on a DatetimeIndex.

Expected Output

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.0.final.0 python-bits: 64 OS: Darwin OS-release: 16.7.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_CA.UTF-8 LOCALE: en_CA.UTF-8 pandas: 0.20.2 pytest: 3.0.5 pip: 9.0.1 setuptools: 35.0.1 Cython: None numpy: 1.13.0 scipy: 0.19.1 xarray: 0.9.6 IPython: 6.0.0 sphinx: 1.5.3 patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: 1.2.0 tables: None numexpr: None feather: None matplotlib: 2.0.0 openpyxl: 2.4.8 xlrd: 1.0.0 xlwt: None xlsxwriter: 0.9.8 lxml: None bs4: None html5lib: 0.999 sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.9.5 s3fs: None pandas_gbq: None pandas_datareader: None

Comment From: nathanielatom

Also, some info on my use case:

I want to apply the tz_localize method to each Timestamp in the series. I originally tried tz_localize on the series itself, but that raised 

TypeError: index is not a valid DatetimeIndex or PeriodIndex

I realize it is possible to achieve this by using reindex, but I was wondering if it was possible to do this with Timestamps as Series values as well.

Comment From: jorisvandenbossche

@nathanielatom you can use tz_localize/tz_convert on the Series through the dt accessor:

In [19]: ts.dt.tz_convert('UTC')
Out[19]: 
0   2017-08-01 00:08:46.110998+00:00
1   2017-08-02 00:08:46.110998+00:00
2   2017-08-03 00:08:46.110998+00:00
dtype: datetime64[ns, UTC]

Comment From: jorisvandenbossche

Further, the reason you get the output you see with apply, is because apply will first try to invoke the function on all values (which are holded under the hood as a DatetimeIndex, although it are the values of the Series), and only if that fails, will call the function on each element.

If you adapt the function a little bit to raise when it doesn't get a scalar value, you see the expected output:

In [21]: def func(elem):
    ...:     assert not hasattr(elem, 'ndim')
    ...:     print(type(elem))
    ...:     return elem
    ...: 

In [22]: ts.apply(func)
<class 'pandas._libs.tslib.Timestamp'>
<class 'pandas._libs.tslib.Timestamp'>
<class 'pandas._libs.tslib.Timestamp'>
Out[22]: 
0   2017-07-31 20:08:46.110998-04:00
1   2017-08-01 20:08:46.110998-04:00
2   2017-08-02 20:08:46.110998-04:00
dtype: datetime64[ns, pytz.FixedOffset(-240)]