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'>
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.
pd.show_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
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.
@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]
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)]