Description
Code Sample, a copy-pastable example if possible
Constructing a Series
from a list of Interval
objects results in an object dtype, and is not backed by an IntervalArray
:
In [2]: s = pd.Series([pd.Interval(0, 1), pd.Interval(1, 2), pd.Interval(2, 3)])
In [3]: s.dtype
Out[3]: dtype('O')
In [4]: s.values
Out[4]:
array([Interval(0, 1, closed='right'), Interval(1, 2, closed='right'),
Interval(2, 3, closed='right')], dtype=object)
Note that constructing a Series
from an IntervalArray
results in the correct dtype and is backed by an IntervalArray
:
In [5]: s2 = pd.Series(pd.core.arrays.IntervalArray.from_breaks(range(4)))
In [6]: s2.dtype
Out[6]: interval[int64]
In [7]: s2.values
Out[7]:
IntervalArray([(0, 1], (1, 2], (2, 3]],
closed='right',
dtype='interval[int64]')
Problem description
The input data is not being inferred as interval dtype, but rather as object dtype, and is not being backed by an IntervalArray
.
Expected Output
I'd expect to the Series
to have an interval dtype and be backed by an IntervalArray
.
Output of pd.show_versions()
INSTALLED VERSIONS
commit: 8212001
python: 3.6.1.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: 0.24.0.dev0+948.g82120016e
pytest: 3.8.2
pip: 9.0.1
setuptools: 39.0.1
Cython: 0.28.2
numpy: 1.13.3
scipy: 1.0.0
pyarrow: 0.6.0
xarray: 0.9.6
IPython: 6.1.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 0.9.8
lxml: 3.8.0
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
gcsfs: None