BUG: (linear) interpolation after resampling

[Make42]

When running

    import pandas as pd
    index = pd.date_range('1/1/2000', periods=9, freq='0.9S')
    series = pd.Series(range(9), index=index)
    
    >>> series
    2000-01-01 00:00:00.000    0
    2000-01-01 00:00:00.900    1
    2000-01-01 00:00:01.800    2
    2000-01-01 00:00:02.700    3
    2000-01-01 00:00:03.600    4
    2000-01-01 00:00:04.500    5
    2000-01-01 00:00:05.400    6
    2000-01-01 00:00:06.300    7
    2000-01-01 00:00:07.200    8
    Freq: 900L, dtype: int64

I get

    >>> series.resample(rule='0.5S').head(100)
    2000-01-01 00:00:00.000    0.0
    2000-01-01 00:00:00.500    1.0
    2000-01-01 00:00:01.000    NaN
    2000-01-01 00:00:01.500    2.0
    2000-01-01 00:00:02.000    NaN
    2000-01-01 00:00:02.500    3.0
    2000-01-01 00:00:03.000    NaN
    2000-01-01 00:00:03.500    4.0
    2000-01-01 00:00:04.000    NaN
    2000-01-01 00:00:04.500    5.0
    2000-01-01 00:00:05.000    6.0
    2000-01-01 00:00:05.500    NaN
    2000-01-01 00:00:06.000    7.0
    2000-01-01 00:00:06.500    NaN
    2000-01-01 00:00:07.000    8.0
    Freq: 500L, dtype: float64

However I do not expect to get

    >>> series.resample(rule='0.5S').interpolate(method='linear')
    2000-01-01 00:00:00.000    0.000000
    2000-01-01 00:00:00.500    0.555556
    2000-01-01 00:00:01.000    1.111111
    2000-01-01 00:00:01.500    1.666667
    2000-01-01 00:00:02.000    2.222222
    2000-01-01 00:00:02.500    2.777778
    2000-01-01 00:00:03.000    3.333333
    2000-01-01 00:00:03.500    3.888889
    2000-01-01 00:00:04.000    4.444444
    2000-01-01 00:00:04.500    5.000000
    2000-01-01 00:00:05.000    5.000000
    2000-01-01 00:00:05.500    5.000000
    2000-01-01 00:00:06.000    5.000000
    2000-01-01 00:00:06.500    5.000000
    2000-01-01 00:00:07.000    5.000000
    Freq: 500L, dtype: float64

instead I expect the last value to be still 8.0 and still 7.0 for the timestamp with 6.5 seconds.

I posted this on https://stackoverflow.com/q/46728152/4533188 and I was told this might be a bug - I got the same impression.

>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-129-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.20.3
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.2.2.post20170724
Cython: 0.26
numpy: 1.13.3
scipy: 0.19.1
xarray: None
IPython: None
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.8
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[AbdealiLoKo]

With 0.21.0 I get the right result:

In [8]: series.resample(rule='0.5S').head(20).interpolate(method='linear')
Out[8]: 
2000-01-01 00:00:00.000    0.0
2000-01-01 00:00:00.500    1.0
2000-01-01 00:00:01.000    1.5
2000-01-01 00:00:01.500    2.0
2000-01-01 00:00:02.000    2.5
2000-01-01 00:00:02.500    3.0
2000-01-01 00:00:03.000    3.5
2000-01-01 00:00:03.500    4.0
2000-01-01 00:00:04.000    4.5
2000-01-01 00:00:04.500    5.0
2000-01-01 00:00:05.000    6.0
2000-01-01 00:00:05.500    6.5
2000-01-01 00:00:06.000    7.0
2000-01-01 00:00:06.500    7.5
2000-01-01 00:00:07.000    8.0
Freq: 500L, dtype: float64

[TomAugspurger]

I think the idea is to avoid the need for .head.

IIRC, there's a similar issue about this, though I may be wrong. @Make42 could you search around for one?

[Make42]

@TomAugspurger: I went through all issues with the search term "interpolate". I am not sure which one you mean. Could it be #16381 or #14297 ?

[mroeschke]

I think #14297 is a duplicate issue as this one. Going to close this issue in favor of that one