df.applymap() produces unwanted type conversion of datetime64[ns] to timedelta

[Apeder]

Code Sample, a copy-pastable example if possible

time_data.head()

print time_data.dtypes

time_data[nt] = time_data[nt].applymap(lambda x: x.strftime('%I:%M:%S %p') if pd.notnull(x) else pd.NaT)

print time_data.dtypes

Problem description

When executing applymap on a masked subset of df datetime columns, two of the four columns are converted to timedelta. Can't figure out what might be happening, perhaps an error similar to #18493? But why only two of the four?!

Here's what time_data.head() looks like:

         Date           Name               In AM              Out AM  \
0  2017-11-06   AUSTIN LEWIS 1900-01-01 06:10:24 1900-01-01 12:03:23   
1  2017-11-06     FRED MOORE 1900-01-01 06:58:37 1900-01-01 12:12:11   
2  2017-11-06  KERRIE PAUSSA 1900-01-01 11:58:48 1900-01-01 19:39:49   
3  2017-11-06   OMAR CUELLAR                 NaT                 NaT   
4  2017-11-07   AUSTIN LEWIS 1900-01-01 07:07:27 1900-01-01 12:06:43   

                In PM              Out PM Sick Time  
0 1900-01-01 12:32:03 1900-01-01 17:31:50       NaT  
1 1900-01-01 12:42:53 1900-01-01 17:31:50       NaT  
2                 NaT                 NaT       NaT  
3 1900-01-01 20:00:19 1900-01-01 23:59:41       NaT  
4 1900-01-01 12:35:26 1900-01-01 17:33:20       NaT              

Expected Output

Date                        object
Name                        object
In AM               datetime64[ns]
Out AM              datetime64[ns]
In PM               datetime64[ns]
Out PM              datetime64[ns]
Sick Time           datetime64[ns]
Total Clock Time            object
dtype: object

Date                        object
Name                        object
In AM               datetime64[ns]
Out AM              datetime64[ns]
In PM               datetime64[ns]
Out PM              datetime64[ns]
Sick Time           datetime64[ns]
Total Clock Time            object
dtype: object

Output of pd.show_versions()

Name                        object
In AM               datetime64[ns]
Out AM              datetime64[ns]
In PM               datetime64[ns]
Out PM              datetime64[ns]
Sick Time           datetime64[ns]
Total Clock Time            object
dtype: object

Date                         object
Name                         object
In AM                        object
Out AM                       object
In PM               timedelta64[ns]
Out PM              timedelta64[ns]
Sick Time            datetime64[ns]
Total Clock Time            float64
dtype: object 

INSTALLED VERSIONS ------------------ commit: None python: 2.7.10.final.0 python-bits: 64 OS: Darwin OS-release: 17.3.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 38.2.4
Cython: None
numpy: 1.14.0
scipy: None
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: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.1.15
pymysql: 0.8.0
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[Apeder]

Ah, turns out the issue was using pd.NaT rather than np.NaN. Le sigh.