Apply on DataFrame with datetime

[sebov]

Code Sample, a copy-pastable example if possible

x = datetime.datetime.now()
df = pd.DataFrame([[x, 1], [x, 2]], columns=['a', 'b'])
print(df.apply(lambda row: {'result': row['b']}, axis=1))
print()
x = datetime.datetime.now()
df = pd.DataFrame([[x, 1], [x, 2]], columns=['a', 'b'], dtype='int64')
print(df.apply(lambda row: {'result': row['b']}, axis=1))
print()
x = 101
df = pd.DataFrame([[x, 1], [x, 2]], columns=['a', 'b'])
print(df.apply(lambda row: {'result': row['b']}, axis=1))
print()
x = datetime.date.today()
df = pd.DataFrame([[x, 1], [x, 2]], columns=['a', 'b'])
print(df.apply(lambda row: {'result': row['b']}, axis=1))
print()
x = {'x': 2}
df = pd.DataFrame([[x, 1], [x, 2]], columns=['a', 'b'])
print(df.apply(lambda row: {'result': row['b']}, axis=1))

Problem description

The latter 4 cases produce the results that I can expect -- Series containing dicts. The first one is somehow strange as it produces a DataFrame:
a b
0 NaN NaN
1 NaN NaN

Also, when I change the first case's print line to print(df.apply(lambda row: [10,20], axis=1)) I am also receiving a DataFrame:
a b
0 10 20
1 10 20

Expected Output

0 {'result': 1}
1 {'result': 2}
dtype: object

0 {'result': 1}
1 {'result': 2}
dtype: object

0 {'result': 1}
1 {'result': 2}
dtype: object

0 {'result': 1}
1 {'result': 2}
dtype: object

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]
INSTALLED VERSIONS

commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-96-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 36.5.0
Cython: None
numpy: 1.13.3
scipy: 0.19.1
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0b10
sqlalchemy: None
pymysql: None
psycopg2: 2.7.3.1 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[sinhrks]

Basically, .apply returning a dict results in DataFrame. Thus, I think your expected output is caused by a bug.

x = datetime.datetime.now()
df = pd.DataFrame([[x, 1], [x, 2]], columns=['a', 'b'])
print(df.apply(lambda row: {'a': row['b'], 'b': row['a']}, axis=1))
#    a                          b
# 0  1 2017-10-23 19:40:42.481066
# 1  2 2017-10-23 19:40:42.481066

df = pd.DataFrame([[x, 1], [x, 2]], columns=['a', 'b'])
print(df.apply(lambda row: {'a': row['b']}, axis=1))
#      a   b
# 0  1.0 NaN
# 1  2.0 NaN

[jreback]

we already note this bug multiple times: https://github.com/pandas-dev/pandas/issues?q=is%3Aissue+label%3AApply+is%3Aclosed

[jreback]

though it’s not actually a bug see he related issues

[jreback]

master is here: #15628