Upsample spline interpolation with smoothing
#### Code Sample, a copy-pastable example if possible

```python
from io import StringIO
import numpy as np
import pandas as pd
from scipy import interpolate
import matplotlib.pyplot as plt

csv = StringIO("""
date_time,temperature
2019-05-05 01:30:00,38.8
2019-05-05 03:20:00,39.5
2019-05-05 04:30:00,39.3
2019-05-05 05:30:00,38.9
2019-05-05 07:55:00,38.7
2019-05-05 09:45:00,39.1
""")
df = pd.read_csv(csv, parse_dates=['date_time'], index_col=0)

s = 0.3
x = df.index.values
y = df.temperature.values
xnew = np.arange(x[0], x[-1], np.timedelta64(5, 'm'))

tck = interpolate.splrep(x, y, k=3, s=s)
ynew = interpolate.splev(xnew.view(int), tck)

df.temperature.plot(marker='o', ls='', label='data')
plt.plot(xnew, ynew, label=f'scipy cubic spline (s={s})')
(df.temperature.resample('5min')
               .interpolate(method='spline', order=3, s=s)
               .plot(label=f'pandas cubic spline (s={s})'))
plt.legend();
```

See the same script as a notebook: 

- https://gist.github.com/tritemio/aea8c0e18e9bde6984ca199b7c012ad8

#### Problem description
Spline interpolation of order 3 with smoothing (`s>0`) gives an interpolation that does not pass through the data points. Scipy's version shows this behaviour. Pandas's version shows a smooth spline and then "jumps" in correspondence to the data points in order to "pass through the data". See figure below:

<img width="380" alt="index" src="https://user-images.githubusercontent.com/4156237/57311250-8efbc180-70eb-11e9-8800-0fd467e198b8.png">


#### Expected Output

Scipy and pandas interpolation should match.

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.2
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
