How to split a pandas time-series by NAN values
numpy, pandas, python, split, time-series
Solution
You can use `numpy.split` and then filter the resulting list. Here is one example assuming that the column with the values is labeled `"value"`:
events = np.split(df, np.where(np.isnan(df.value))[0])
# removing NaN entries
events = [ev[~np.isnan(ev.value)] for ev in events if not isinstance(ev, np.ndarray)]
# removing empty DataFrames
events = [ev for ev in events if not ev.empty]
You will have a list with all the events separated by the `NaN` values.
Problem
I have a pandas TimeSeries which looks like this: ``` 2007-02-06 15:00:00 0.780 2007-02-06 16:00:00 0.125 2007-02-06 17:00:00 0.875 2007-02-06 18:00:00 NaN 2007-02-06 19:00:00 0.565 2007-02-06 20:00:00 0.875 2007-02-06 21:00:00 0.910 2007-02-06 22:00:00 0.780 2007-02-06 23:00:00 NaN 2007-02-07 00:00:00 NaN 2007-02-07 01:00:00 0.780 2007-02-07 02:00:00 0.580 2007-02-07 03:00:00 0.880 2007-02-07 04:00:00 0.791 2007-02-07 05:00:00 NaN ``` I would like split the pandas TimeSeries everytime there occurs one or more NaN values in a row. The goal is that I have separated events. ``` Event1: 2007-02-06 15:00:00 0.780 2007-02-06 16:00:00 0.125 2007-02-06 17:00:00 0.875 Event2: 2007-02-06 19:00:00 0.565 2007-02-06 20:00:00 0.875 2007-02-06 21:00:00 0.910 2007-02-06 22:00:00 0.780 ``` I could loop through every row but is there also a smart way of doing that???