ENH: .interval accessor

[jreback]

similar to how we work with .dt, .str, and .cat accessors, it might be nice to expose an .interval accessor; in particular this might make nice indexing expressions, xref to #16316

http://stackoverflow.com/questions/44088460/interval-datatype-in-pandas-find-midpoint-left-center-etc/44088970#44088970

In [13]: df = pd.DataFrame({'month': [1, 1, 2, 2], 'distances': range(4), 'value': range(4)})

In [14]: df
Out[14]: 
   distances  month  value
0          0      1      0
1          1      1      1
2          2      2      2
3          3      2      3

In [15]: result = df.groupby(['month', pd.cut(df.distances, 2)]).value.mean()

In [16]: result
Out[16]: 
month  distances    
1      (-0.003, 1.5]    0.5
2      (1.5, 3.0]       2.5
Name: value, dtype: float64

In [17]: pd.IntervalIndex(result.index.get_level_values('distances')).left
Out[17]: Float64Index([-0.003, 1.5], dtype='float64')

In [18]: pd.IntervalIndex(result.index.get_level_values('distances')).right
Out[18]: Float64Index([1.5, 3.0], dtype='float64')

In [19]: pd.IntervalIndex(result.index.get_level_values('distances')).mid
Out[19]: Float64Index([0.7485, 2.25], dtype='float64')

[jreback]

cc @shoyer @zfrenchee @buyology @TomAugspurger

[jreback]

e.g. this might make a reasonable syntax for indexing

df.loc[df.my_interval_column.interval.overlaps(.....)]
df.loc[df.my_interval_column.interval.contains(....)]

we do with for example now

In [20]: df = pd.DataFrame({'A': pd.date_range('20170101', periods=10), 'value': range(10)})

In [21]: df.loc[df.A.dt.weekday]
Out[21]: 
           A  value
6 2017-01-07      6
0 2017-01-01      0
1 2017-01-02      1
2 2017-01-03      2
3 2017-01-04      3
4 2017-01-05      4
5 2017-01-06      5
6 2017-01-07      6
0 2017-01-01      0
1 2017-01-02      1

In [22]: df.loc[df.A.dt.weekday==2]
Out[22]: 
           A  value
3 2017-01-04      3

In [23]: df.loc[df.A.dt.weekday==1]
Out[23]: 
           A  value
2 2017-01-03      2
9 2017-01-10      9

[jreback]

cc @jschendel