Closed
Description
Pandas resample bugs when trying to resample a time serie with same size splits :
I have a time serie of size 10:
rng = pd.date_range('20130101',periods=10,freq='T')
ts=pd.Series(np.random.randn(len(rng)), index=rng)
print(ts)
2013-01-01 00:00:00 -1.811999
2013-01-01 00:01:00 -0.890837
2013-01-01 00:02:00 -0.363520
2013-01-01 00:03:00 -0.026245
2013-01-01 00:04:00 1.515072
2013-01-01 00:05:00 0.920129
2013-01-01 00:06:00 -0.125954
2013-01-01 00:07:00 0.588933
2013-01-01 00:08:00 -1.278408
2013-01-01 00:09:00 -0.172525
Freq: T, dtype: float64
When trying to resample in 8 equal parts it works fine:
length = 8
print(ts)
timeSpan = (ts.index[-1]-ts.index[0]+timedelta(minutes=1))
rule = int(timeSpan.total_seconds()/length)
tsNew=ts.resample(str(rule)+"S")
print(tsNew)
2013-01-01 00:00:00 0.124147
2013-01-01 00:01:15 0.558947
2013-01-01 00:02:30 0.076321
2013-01-01 00:03:45 0.178429
2013-01-01 00:05:00 -1.357948
2013-01-01 00:06:15 0.931305
2013-01-01 00:07:30 0.984052
2013-01-01 00:08:45 -1.758608
Freq: 75S, dtype: float64
But when tying to split into 9 parts there are nan arriving :
length = 9
print(ts)
timeSpan = (ts.index[-1]-ts.index[0]+timedelta(minutes=1))
rule = int(timeSpan.total_seconds()/length)
tsNew=ts.resample(str(rule)+"S")
print(tsNew)
2013-01-01 00:00:00 -0.264389
2013-01-01 00:01:06 NaN
2013-01-01 00:02:12 NaN
2013-01-01 00:03:18 NaN
2013-01-01 00:04:24 NaN
2013-01-01 00:05:30 NaN
2013-01-01 00:06:36 NaN
2013-01-01 00:07:42 NaN
2013-01-01 00:08:48 NaN
2013-01-01 00:09:54 NaN
Freq: 66S, dtype: float64
Do you have an idea how to solve this issue ?
PS: with length > 10 it doesn't works neither...