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Description
Code Sample, a copy-pastable example if possible
Without errors='coerce'
>>> d = { 'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00'] }
>>> df = pd.DataFrame(data=d)
>>> d
{'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00']}
>>> df
ts
0 2019-02-02 08:07:13+00
1 2019-02-02 08:03:22.54+00
>>> df.dtypes
ts object
dtype: object
>>> df['ts'] = pd.to_datetime(df['ts'], infer_datetime_format=True)
>>> df
ts
0 2019-02-02 08:07:13+00:00
1 2019-02-02 08:03:22.540000+00:00
>>> df.dtypes
ts datetime64[ns, UTC]
dtype: object
With errors='coerce'
>>> d = { 'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00'] }
>>> df = pd.DataFrame(data=d)
>>> d
{'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00']}
>>> df
ts
0 2019-02-02 08:07:13+00
1 2019-02-02 08:03:22.54+00
>>> df.dtypes
ts object
dtype: object
>>> df['ts'] = pd.to_datetime(df['ts'], infer_datetime_format=True, errors='coerce')
>>> df
ts
0 2019-02-02 08:07:13
1 NaT
Problem description
The functionality of to_datetime() with errors='coerce' is different than without. If I understand some of the other issues raised on this topic correctly, the functionality is different in some cases by design. In this case, the dates are very similiar, although different format.
Expected Output
>>> d = { 'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00'] }
>>> df = pd.DataFrame(data=d)
>>> d
{'ts': ['2019-02-02 08:07:13+00', '2019-02-02 08:03:22.54+00']}
>>> df
ts
0 2019-02-02 08:07:13+00
1 2019-02-02 08:03:22.54+00
>>> df.dtypes
ts object
dtype: object
>>> df['ts'] = pd.to_datetime(df['ts'], infer_datetime_format=True, errors='coerce')
>>> df
ts
0 2019-02-02 08:07:13+00:00
1 2019-02-02 08:03:22.540000+00:00
>>> df.dtypes
ts datetime64[ns, UTC]
dtype: object
Output of pd.show_versions()
[paste the output of pd.show_versions()
here below this line]
INSTALLED VERSIONS
commit: None
python: 3.6.5.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: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.14.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 3.8.0
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.8.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None