Is it somehow possible to use resample
on irregularly spaced data? (I know that the documentation says it's for "resampling of regular time-series data", but I wanted to try if it works on irregular data, too. Maybe it doesn't, or maybe I am doing something wrong.)
In my real data, I have generally 2 samples per hour, the time difference between them ranging usually from 20 to 40 minutes. So I was hoping to resample them to a regular hourly series.
To test if I am using it right, I used some random list of dates that I already had, so it may not be a best example but at least a solution that works for it will be very robust. here it is:
fraction number time
0 0.729797 0 2014-10-23 15:44:00
1 0.141084 1 2014-10-30 19:10:00
2 0.226900 2 2014-11-05 21:30:00
3 0.960937 3 2014-11-07 05:50:00
4 0.452835 4 2014-11-12 12:20:00
5 0.578495 5 2014-11-13 13:57:00
6 0.352142 6 2014-11-15 05:00:00
7 0.104814 7 2014-11-18 07:50:00
8 0.345633 8 2014-11-19 13:37:00
9 0.498004 9 2014-11-19 22:47:00
10 0.131665 10 2014-11-24 15:28:00
11 0.654018 11 2014-11-26 10:00:00
12 0.886092 12 2014-12-04 06:37:00
13 0.839767 13 2014-12-09 00:50:00
14 0.257997 14 2014-12-09 02:00:00
15 0.526350 15 2014-12-09 02:33:00
Now I want to resample these for example monthly:
df_new = df.set_index(pd.DatetimeIndex(df['time']))
df_new['fraction'] = df.fraction.resample('M',how='mean')
df_new['number'] = df.number.resample('M',how='mean')
But I get TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'RangeIndex'
- unless I did something wrong with assigning the datetime index, it must be due to the irregularity?
So my questions are:
(I only see a solution in first reindexing the data to get finer intervals, interpolate the values in between and then reindexing it to hourly interval. If it is so, then a question regarding the correct implementation of reindex will follow shortly.)
You don't need to explicitly use DatetimeIndex
, just set 'time'
as the index and pandas will take care of the rest, so long as your 'time'
column has been converted to datetime using pd.to_datetime
or some other method. Additionally, you don't need to resample each column individually if you're using the same method; just do it on the entire DataFrame.
# Convert to datetime, if necessary.
df['time'] = pd.to_datetime(df['time'])
# Set the index and resample (using month start freq for compact output).
df = df.set_index('time')
df = df.resample('MS').mean()
The resulting output:
fraction number
time
2014-10-01 0.435441 0.5
2014-11-01 0.430544 6.5
2014-12-01 0.627552 13.5
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