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Description
Problem description
pandas.DataFrame.where
seems to be not replacing NaTs properly.
As in the example below, NaT values stay in data frame after applying .where((pd.notnull(df)), None)
Code sample
In [26]: pd.__version__
Out[26]: '0.19.2'
In [27]: df
Out[27]:
d v
0 2015-01-01 30.0
1 NaT 40.0
2 2015-01-03 NaN
In [28]: pd.notnull(df)
Out[28]:
d v
0 True True
1 False True
2 True False
In [29]: df.where((pd.notnull(df)), None)
Out[29]:
d v
0 2015-01-01 30
1 NaT 40
2 2015-01-03 None
INSTALLED VERSIONS
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.3.1
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None