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In [2]: idx=pd.IntervalIndex.from_breaks(range(4), closed='both')
In [3]: idxOut[3]:
IntervalIndex([[0, 1], [1, 2], [2, 3]]
closed='both',
dtype='interval[int64]')
In [4]: idx.is_non_overlapping_monotonicOut[4]: True
Problem description
The intervals have an overlap in the endpoints, 1 is shared among [0, 1], [1, 2] and 2 is shared among [1, 2], [2, 3], but is_non_overlapping_monotonic returns True indicating that the intervals are non-overlapping.
Expected Output
I'd expected is_non_overlapping_monotonic to return False.
If my interpretation of "overlapping" is incorrect, and in this context means an interval overlap (i.e. not a point/has positive length), then it'd be nice if this were more explicitly stated.
Output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.5.2.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
At first glance, I think this amounts to using strict inequalities when closed='both', and using the existing inclusive inequality code for is_non_overlapping_monotonic otherwise.
Code Sample, a copy-pastable example if possible
Problem description
The intervals have an overlap in the endpoints, 1 is shared among
[0, 1], [1, 2]
and 2 is shared among[1, 2], [2, 3]
, butis_non_overlapping_monotonic
returnsTrue
indicating that the intervals are non-overlapping.Expected Output
I'd expected
is_non_overlapping_monotonic
to returnFalse
.If my interpretation of "overlapping" is incorrect, and in this context means an interval overlap (i.e. not a point/has positive length), then it'd be nice if this were more explicitly stated.
Output of
pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.5.2.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: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 34.4.1
Cython: 0.25.2
numpy: 1.13.0rc2
scipy: 0.18.1
xarray: 0.9.5
IPython: 5.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: 1.4.4
bottleneck: 1.2.0
tables: 3.4.2
numexpr: 2.6.1
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.7.3
bs4: None
html5lib: None
sqlalchemy: 1.1.10
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: 0.1.6
pandas_datareader: 0.4.0
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