Pandas Series passed to function in DataFrame.apply on empty dataframe lack index

Code Sample, a copy-pastable example if possible

from StringIO import StringIO
import pandas as pd
from random import random 
s = "a,b,c"
df = pd.read_csv(StringIO(s))
f1 = lambda row: random()
new_col1 = df.apply(f1, axis=1)
df["d"] = new_col1
f2 = lambda row: 0 if row["a"] > 10 else 1
new_col2 = df.apply(f2, axis=1)
df["e"] = new_col2
df

Expected Output

Empty DataFrame
Columns: [a, b, c, d, e]
Index: []

The problem seems to be that a Series row is passed to the lambda and that 'a' can't be used to index into it. This is only breaks for an empty DataFrame. If it has values the output is as expected.

According to the docs:

Objects passed to functions are Series objects having index either the DataFrame’s index (axis=0) or the columns (axis=1)

output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-21-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 20.7.0
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None

Comment From: jreback

you need to pass reduce=True

Comment From: jreback

In [14]: new_col2 = df.apply(f2, axis=1, reduce=True)

In [15]: new_col2
Out[15]: Series([], dtype: float64)