numpy.core._exceptions while resample using spline interpolation
#### Code Sample, a copy-pastable example if possible

```python
time      value1      value2      value3
run_id

19            00:00:00     14.45       1000.0       5.800000
19     00:00:00.010000     14.45       1000.0       5.591578
19     00:00:00.020000     14.45       1000.0       5.878844
19     00:00:00.030000     14.45       1000.0       5.974964
19     00:00:00.040000     14.45       1000.0       5.643659
...                ...       ...          ...            ...
20     00:00:00.647290     17.46       1500.0       5.695907
```
#### Problem description
I have a grouped timeseries from measurement data which looks like this. I now would like to increase the sample rate from 0.01s to 0.001s using the following code.

```python
for name, group in data.groupby('run_id'):
  temp = group.reset_index().set_index('time').resample('0.001S').interpolate(method='spline', order=2)
```
This will end up in the following exception.

```python
numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'greater' input 1 from dtype('float64') to dtype('<m8[ns]') with casting rule 'same_kind'
```
Select a frequency for downsampling like this.

```python
temp = group.reset_index().set_index('time').resample('0.02S').interpolate(method='spline', order=2)
```
It seems to be working. The upsampling is working if I change the interpolation method to linear.

```python
temp = group.reset_index().set_index('time').resample('0.001S').interpolate(method='linear')
```
What I'm doing wrong here? I also tried an approach to apply the resampling directly to the groupby object like this.

```python
data.reset_index().set_index('time').groupby('run_id').resample('0.001S').interpolate(method='linear')
```
Does this make sense or do I need the apply() approach here?


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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.4.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None
pandas           : 0.25.3
numpy            : 1.17.1
pytz             : 2017.3
dateutil         : 2.6.1
pip              : 19.3.1
setuptools       : 41.2.0
Cython           : 0.27.3
pytest           : 5.1.2
hypothesis       : None
sphinx           : 2.1.2
blosc            : None
feather          : None
xlsxwriter       : 1.0.2
lxml.etree       : 4.1.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10
IPython          : 6.2.1
pandas_datareader: None
bs4              : 4.6.0
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.1.1
matplotlib       : 2.1.2
numexpr          : 2.6.4
odfpy            : None
openpyxl         : 2.4.10
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.0.0
sqlalchemy       : 1.2.1
tables           : None
xarray           : None
xlrd             : 1.1.0
xlwt             : 1.3.0
xlsxwriter       : 1.0.2


</details>
