Code Sample, a copy-pastable example if possible

import pandas as pd A = pd.DataFrame() A["author"] = ["X", "Y", "Z"] A["publisher"] = ["BBC", "NBC", "N24"] A["date"] = pd.to_datetime(['17-10-2010 07:15:30', '13-05-2011 08:20:35', "15-01-2013 09:09:09"]) # the following produces the faulty result A.apply(lambda x: {}, axis=1)

Problem description

The last line returns a dataframe with all entries replaced by NaN. This only happens if the following two conditions are both satisfied:

• a column with datetime64[ns] is present in the dataframe (in the above example the column with name date)
• the function applied to the dataframe returns a dictionary
  When using a Dataframe without the datetime column, the code returns the expected result (for the above result a pd.Series with empty dictionaries).

Why this is a (significant) problem:
Output of apply depends on presence of another column that is not used by applied function.

Potentially related:
I tried to search for a similar issues and found the already closed issues

• #8735
• #10740

However, these issues are fixed and already closed since 2015.

Expected Output

the expected output can be easily produced by removing the 6th line (A["date"] = ...)

>>> A.apply(lambda x: {}, axis=1) 0 {} 1 {} 2 {} dtype: object 

Output of pd.show_versions()

Checked with newest version of pandas:

• pandas (0.21.1)
• numpy (1.13.3)
• Python 3.6.2

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

INSTALLED VERSIONS ------------------ commit: None python: 3.6.2.final.0 python-bits: 64 OS: Darwin OS-release: 17.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: None LOCALE: en_CA.UTF-8 pandas: 0.21.1 pytest: None pip: 9.0.1 setuptools: 36.6.0 Cython: None numpy: 1.13.3 scipy: 1.0.0 pyarrow: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.1 pytz: 2017.3 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: 0.9999999 sqlalchemy: None pymysql: None psycopg2: None jinja2: None s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None