How to efficienty up-sample hourly averages in a Series? - pandas

Question

I had a look to the "sql-like" windows function for pandas, and to "rolling".
However, it seems to me I can't have a condition on timestamps in the index, but maybe I'm wrong.
So far, I've been writing this very inefficient code to have an hourly average as a window function.
Anyone knowing a quicker and nicer method?
def avg_on_hour(data: pd.Series()):
new_series = pd.Series()
start_date = data.index.min()
end_date = data.index.max()
delta = dt.timedelta(hours=1)
this_time = start_date
while this_time < end_date:
this_date = this_time.date()
this_hour = this_time.hour
day_slice = data[(data.index.date == this_date) & (data.index.hour == this_hour)]
day_avg = day_slice.mean()
day_slice.iloc[:] = day_avg
new_series = new_series.append(day_slice, verify_integrity=True)
this_time = this_time + delta
return new_series
Example:

Answer 1

Pandas has rolling on datetime, given that the series is datetime indexed
# sample data:
np.random.seed(1)
size=10
s = pd.Series(np.random.rand(size),
index=pd.date_range('2020-01-01', freq='7T', periods=size))
# rolling mean
series.rolling('1H').mean()
Output:
2020-01-01 00:00:00 0.417022
2020-01-01 00:07:00 0.568673
2020-01-01 00:14:00 0.379154
2020-01-01 00:21:00 0.359948
2020-01-01 00:28:00 0.317310
2020-01-01 00:35:00 0.279815
2020-01-01 00:42:00 0.266450
2020-01-01 00:49:00 0.276339
2020-01-01 00:56:00 0.289720
2020-01-01 01:03:00 0.303252
Freq: 7T, dtype: float64
Update: from your comment, it looks like you are looking for groupby:
s.groupby(s.index.floor('H')).transform('mean')
or
s.groupby(pd.Grouper(freq='H')).transform('mean')
Output:
2020-01-01 00:00:00 0.289720
2020-01-01 00:07:00 0.289720
2020-01-01 00:14:00 0.289720
2020-01-01 00:21:00 0.289720
2020-01-01 00:28:00 0.289720
2020-01-01 00:35:00 0.289720
2020-01-01 00:42:00 0.289720
2020-01-01 00:49:00 0.289720
2020-01-01 00:56:00 0.289720
2020-01-01 01:03:00 0.538817
Freq: 7T, dtype: float64