Cannot apply lambda function to DataFrame with columns of type numpy.datetime64

[meditativeape]

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

ts1 = np.datetime64('2017-11-29T03:30')
ts2 = np.datetime64('2017-11-29T03:45')
d = {'number' : pd.Series([1., 2.]), 'string': pd.Series(['haha', 'wawa']), 'datetime64': pd.Series([ts1, ts2])}
df = pd.DataFrame(d)
df.apply(lambda row: (row.number, row.string), axis=1)

Problem description

Applying lambda function to a DataFrame with columns of type numpy.datetime64 throws an unexpected error: ValueError: Shape of passed values is (2, 2), indices imply (2, 3). The error message is not helpful, and I believe this error is thrown due to a bug. Dropping columns of type numpy.datetime64 fixes the issue.

Expected Output

0    (1.0, haha)
1    (2.0, wawa)
dtype: object

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 2.7.14.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.20.3
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 0.19.1
xarray: None
IPython: 5.4.1
sphinx: 1.6.3
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: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[jreback]

duplicate of #15628

.apply has to guess the return shape from your opaque passed function. This is not always possible.

idiomatically what you are doing is quite odd, simply do this.

In [43]: df[['number', 'string']]
Out[43]: 
   number string
0     1.0   haha
1     2.0   wawa

[meditativeape]

Thanks for the suggestion, @jreback !