Resample for discrete values
#### Example from https://stackoverflow.com/questions/39809703/python-pandas-resampling-instantaneous-hourly-data-to-daily-timestep-including-0

```python
index = pd.date_range('1/1/2000', periods=9, freq='6H')
series = pd.Series([2, 3, 6, 9, 5, 1, 3, 9, 4], index=index)

series
2000-01-01 00:00:00    2
2000-01-01 06:00:00    3
2000-01-01 12:00:00    6
2000-01-01 18:00:00    9
2000-01-02 00:00:00    5
2000-01-02 06:00:00    1
2000-01-02 12:00:00    3
2000-01-02 18:00:00    9
2000-01-03 00:00:00    4
Freq: 6H, dtype: int64
```
#### Problem description
This has been asked about in a stackoverflow question, but the answer is inefficient and only works for regular time series.
If we have a time series where each value is a discrete measurement, resampling/aggregating would require some kind of interpolation assumption across the resampling period. In it's simplest form, a linear interpolation would just require the time series to be shifted back one step (using the shift(-1)) and take the pandas resampled mean of the original and shifted time series.

#### Expected Output
```python
index = pd.date_range('1/1/2000', periods=9, freq='6H')
series = pd.Series([2, 3, 6, 9, 5, 1, 3, 9, 4], index=index)
series_new = (series + series.shift(-1))/2
res_good = series_new.resample('D').mean()

res_good
2000-01-01    5.375
2000-01-02    4.375
2000-01-03      NaN
```
Compared to the below which only utilizes the values within the resampling period as if the values represented some kind of aggregate measurement since the last time stamp...
```python
res_bad1 = series.resample('D').mean()
2000-01-01    5.0
2000-01-02    4.5
2000-01-03    4.0
```
If there was a closed=both option...
```python
2000-01-01    5.0
2000-01-02    4.0
```

Maybe I'm missing something in the parameters for resample or interpolate with resample, but I would imagine that this is not such an uncommon resampling situation.

Thanks!

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 32
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

pandas: 0.21.0
pytest: 3.0.5
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 1.0.0
pyarrow: None
xarray: 0.10.0
IPython: 5.3.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.4
feather: None
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.9.4
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
