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In [2]: dt1_tz=pd.Timestamp('2017-01-01', tz='US/Eastern')
In [3]: dt1_no_tz=pd.Timestamp('2017-01-01')
In [4]: dt2_tz=pd.Timestamp('2017-01-04', tz='US/Eastern')
In [5]: dt2_no_tz=pd.Timestamp('2017-01-04')
If start is tz aware and end is not, date_range coerces to tz aware:
Code Sample, a copy-pastable example if possible
Setup:
If
start
is tz aware andend
is not,date_range
coerces to tz aware:If
end
is tz aware andstart
is not,date_range
raises:Same behavior for
DatetimeIndex
:Problem description
Behavior is inconsistent for mixed tz aware and tz unaware
start
/end
.Expected Output
I'd expect this to be consistent in both cases. If my understanding is correct, this should raise in both cases.
Output of
pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.21.0
pytest: 3.1.2
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.26
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.4
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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