Description
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.dtypesProblem 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
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