Upsampling time series data

In upsampling, the frequency of the time series is increased. As a result, we have more sample points than data points. One of the main questions is how to account for the entries in the series where we have no measurement.

Let's start with hourly data for a single day:

>>> rng = pd.date_range('4/29/2015 8:00', periods=10, freq='H')
>>> ts = pd.Series(np.random.randint(0, 100, len(rng)), index=rng)
>>> ts.head()
2015-04-29 08:00:00    30
2015-04-29 09:00:00    27
2015-04-29 10:00:00    54
2015-04-29 11:00:00     9
2015-04-29 12:00:00    48
Freq: H, dtype: int64

If we upsample to data points taken every 15 minutes, our time series will be extended with NaN values:

>>> ts.resample('15min')
>>> ts.head()
2015-04-29 08:00:00    30
2015-04-29 08:15:00   NaN
2015-04-29 08:30:00   NaN
2015-04-29 08:45:00   NaN
2015-04-29 09:00:00    27

There are various ways to deal with missing values, which can be controlled by the fill_method...